Dataset Viewer
repo_owner
stringclasses 1
value | repo_name
stringclasses 1
value | tag_name
stringlengths 7
32
| name
stringlengths 21
112
| published_at
stringdate 2025-04-22 09:42:25
2025-06-02 13:03:59
| body
stringlengths 283
58.3k
| last_updated
stringdate 2025-05-09 16:54:50
2025-06-03 00:24:45
|
|---|---|---|---|---|---|---|
huggingface | transformers | v4.51.3-SAM-HQ-preview | SAM-HQ (based on v4.51.3) | 2025-05-08T13:04:07+00:00 | A new model is added to transformers: SAM-HQ
It is added on top of the v4.51.3 release, and can be installed from the following tag: `v4.51.3-SAM-HQ-preview`.
In order to install this version, please install with the following command:
```
pip install git+https://github.com/huggingface/[email protected]
```
If fixes are needed, they will be applied to this release; this installation may therefore be considered as stable and improving.
As the tag implies, this tag is a **_preview_** of the SAM-HQ model. This tag is a tagged version of the `main` branch and does not follow semantic versioning. This model will be included in the next minor release: `v4.52.0`.
## SAM-HQ
SAM-HQ (High-Quality Segment Anything Model) was proposed in [Segment Anything in High Quality](https://arxiv.org/pdf/2306.01567.pdf) by Lei Ke, Mingqiao Ye, Martin Danelljan, Yifan Liu, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu.
The model is an enhancement to the original SAM model that produces significantly higher quality segmentation masks while maintaining SAM's original promptable design, efficiency, and zero-shot generalizability.
SAM-HQ introduces several key improvements over the original SAM model:
1. High-Quality Output Token: A learnable token injected into SAM's mask decoder for higher quality mask prediction
2. Global-local Feature Fusion: Combines features from different stages of the model for improved mask details
3. Training Data: Uses a carefully curated dataset of 44K high-quality masks instead of SA-1B
4. Efficiency: Adds only 0.5% additional parameters while significantly improving mask quality
5. Zero-shot Capability: Maintains SAM's strong zero-shot performance while improving accuracy
The abstract from the paper is the following:
*The recent Segment Anything Model (SAM) represents a big leap in scaling up segmentation models, allowing for powerful zero-shot capabilities and flexible prompting. Despite being trained with 1.1 billion masks, SAM's mask prediction quality falls short in many cases, particularly when dealing with objects that have intricate structures. We propose HQ-SAM, equipping SAM with the ability to accurately segment any object, while maintaining SAM's original promptable design, efficiency, and zero-shot generalizability. Our careful design reuses and preserves the pre-trained model weights of SAM, while only introducing minimal additional parameters and computation. We design a learnable High-Quality Output Token, which is injected into SAM's mask decoder and is responsible for predicting the high-quality mask. Instead of only applying it on mask-decoder features, we first fuse them with early and final ViT features for improved mask details. To train our introduced learnable parameters, we compose a dataset of 44K fine-grained masks from several sources. HQ-SAM is only trained on the introduced dataset of 44k masks, which takes only 4 hours on 8 GPUs.*
Tips:
- SAM-HQ produces higher quality masks than the original SAM model, particularly for objects with intricate structures and fine details
- The model predicts binary masks with more accurate boundaries and better handling of thin structures
- Like SAM, the model performs better with input 2D points and/or input bounding boxes
- You can prompt multiple points for the same image and predict a single high-quality mask
- The model maintains SAM's zero-shot generalization capabilities
- SAM-HQ only adds ~0.5% additional parameters compared to SAM
- Fine-tuning the model is not supported yet
## Usage example
SAM-HQ can be found on the [Huggingface Hub](https://huggingface.co/models?other=sam_hq).
```python
import torch
from PIL import Image
import requests
from transformers import SamHQModel, SamHQProcessor
device = "cuda" if torch.cuda.is_available() else "cpu"
model = SamHQModel.from_pretrained("sushmanth/sam_hq_vit_b").to(device)
processor = SamHQProcessor.from_pretrained("sushmanth/sam_hq_vit_b")
img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D location of a window in the image
inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to(device)
with torch.no_grad():
outputs = model(**inputs)
masks = processor.image_processor.post_process_masks(
outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()
)
scores = outputs.iou_scores
```
You can also process your own masks alongside the input images in the processor to be passed to the model:
```python
import torch
from PIL import Image
import requests
from transformers import SamHQModel, SamHQProcessor
device = "cuda" if torch.cuda.is_available() else "cpu"
model = SamHQModel.from_pretrained("sushmanth/sam_hq_vit_b").to(device)
processor = SamHQProcessor.from_pretrained("sushmanth/sam_hq_vit_b")
img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
mask_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
segmentation_map = Image.open(requests.get(mask_url, stream=True).raw).convert("1")
input_points = [[[450, 600]]] # 2D location of a window in the image
inputs = processor(raw_image, input_points=input_points, segmentation_maps=segmentation_map, return_tensors="pt").to(device)
with torch.no_grad():
outputs = model(**inputs)
masks = processor.image_processor.post_process_masks(
outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()
)
scores = outputs.iou_scores
``` | 2025-05-09T16:54:50.171918 |
huggingface | transformers | v4.51.3-GraniteMoeHybrid-preview | GraniteMoeHybrid (based on v4.51.3) | 2025-05-08T13:10:59+00:00 | A new model is added to transformers: GraniteMoeHybrid
It is added on top of the v4.51.3 release, and can be installed from the following tag: `v4.51.3-GraniteMoeHybrid-preview`.
In order to install this version, please install with the following command:
```
pip install git+https://github.com/huggingface/[email protected]
```
If fixes are needed, they will be applied to this release; this installation may therefore be considered as stable and improving.
As the tag implies, this tag is a **_preview_** of the GraniteMoeHybrid model. This tag is a tagged version of the `main` branch and does not follow semantic versioning. This model will be included in the next minor release: `v4.52.0`.
## GraniteMoeHybrid
The `GraniteMoeHybrid` model builds on top of `GraniteMoeSharedModel` and `Bamba`. Its decoding layers consist of state space layers or MoE attention layers with shared experts. By default, the attention layers do not use positional encoding.
## Usage example
GraniteMoeHybrid can be found on the [Huggingface Hub](https://huggingface.co/models?other=granitemoehybrid).
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "ibm-granite/granite-4.0-tiny-preview"
tokenizer = AutoTokenizer.from_pretrained(model_path)
# drop device_map if running on CPU
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto")
model.eval()
# change input text as desired
prompt = "Write a code to find the maximum value in a list of numbers."
# tokenize the text
input_tokens = tokenizer(prompt, return_tensors="pt")
# generate output tokens
output = model.generate(**input_tokens, max_new_tokens=100)
# decode output tokens into text
output = tokenizer.batch_decode(output)
# loop over the batch to print, in this example the batch size is 1
for i in output:
print(i)
``` | 2025-05-09T16:54:50.171941 |
huggingface | transformers | v4.51.3-D-FINE-preview | D-FINE (based on v4.51.3) | 2025-05-08T13:06:40+00:00 |
A new model is added to transformers: D-FINE
It is added on top of the v4.51.3 release, and can be installed from the following tag: `v4.51.3-D-FINE-preview`.
In order to install this version, please install with the following command:
```
pip install git+https://github.com/huggingface/[email protected]
```
If fixes are needed, they will be applied to this release; this installation may therefore be considered as stable and improving.
As the tag implies, this tag is a **_preview_** of the D-FINE model. This tag is a tagged version of the `main` branch and does not follow semantic versioning. This model will be included in the next minor release: `v4.52.0`.
## D-FINE
<img width="1051" alt="image" src="https://github.com/user-attachments/assets/3274da06-ff44-4bb4-bebf-8bc5f9b72aac" />
The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by
Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu
The abstract from the paper is the following:
*We introduce D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD).
FDR transforms the regression process from predicting fixed coordinates to iteratively refining probability distributions, providing a fine-grained intermediate representation that significantly enhances localization accuracy. GO-LSD is a bidirectional optimization strategy that transfers localization knowledge from refined distributions to shallower layers through self-distillation, while also simplifying the residual prediction tasks for deeper layers. Additionally, D-FINE incorporates lightweight optimizations in computationally intensive modules and operations, achieving a better balance between speed and accuracy. Specifically, D-FINE-L / X achieves 54.0% / 55.8% AP on the COCO dataset at 124 / 78 FPS on an NVIDIA T4 GPU. When pretrained on Objects365, D-FINE-L / X attains 57.1% / 59.3% AP, surpassing all existing real-time detectors. Furthermore, our method significantly enhances the performance of a wide range of DETR models by up to 5.3% AP with negligible extra parameters and training costs. Our code and pretrained models: this https URL.*
## Usage example
D-FINE can be found on the [Huggingface Hub](https://huggingface.co/models?other=d_fine).
```python
>>> import torch
>>> from transformers.image_utils import load_image
>>> from transformers import DFineForObjectDetection, AutoImageProcessor
>>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
>>> image = load_image(url)
>>> image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine_x_coco")
>>> model = DFineForObjectDetection.from_pretrained("ustc-community/dfine_x_coco")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> results = image_processor.post_process_object_detection(outputs, target_sizes=[(image.height, image.width)], threshold=0.5)
>>> for result in results:
... for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
... score, label = score.item(), label_id.item()
... box = [round(i, 2) for i in box.tolist()]
... print(f"{model.config.id2label[label]}: {score:.2f} {box}")
cat: 0.96 [344.49, 23.4, 639.84, 374.27]
cat: 0.96 [11.71, 53.52, 316.64, 472.33]
remote: 0.95 [40.46, 73.7, 175.62, 117.57]
sofa: 0.92 [0.59, 1.88, 640.25, 474.74]
remote: 0.89 [333.48, 77.04, 370.77, 187.3]
``` | 2025-05-09T16:54:50.171950 |
huggingface | transformers | v4.51.3-CSM-preview | CSM (based on v4.51.3) | 2025-05-08T13:15:22+00:00 | A new model is added to transformers: CSM
It is added on top of the v4.51.3 release, and can be installed from the following tag: `v4.51.3-CSM-preview`.
In order to install this version, please install with the following command:
```
pip install git+https://github.com/huggingface/[email protected]
```
If fixes are needed, they will be applied to this release; this installation may therefore be considered as stable and improving.
As the tag implies, this tag is a **_preview_** of the CSM model. This tag is a tagged version of the `main` branch and does not follow semantic versioning. This model will be included in the next minor release: `v4.52.0`.
## CSM
The Conversational Speech Model (CSM) is the first open-source contextual text-to-speech model [released by Sesame](https://www.sesame.com/research/crossing_the_uncanny_valley_of_voice). It is designed to generate natural-sounding speech with or without conversational context. This context typically consists of multi-turn dialogue between speakers, represented as sequences of text and corresponding spoken audio.
**Model Architecture:**
CSM is composed of two LLaMA-style auto-regressive transformer decoders: a backbone decoder that predicts the first codebook token and a depth decoder that generates the remaining tokens. It uses the pretrained codec model [Mimi](./mimi.md), introduced by Kyutai, to encode speech into discrete codebook tokens and decode them back into audio.
The original csm-1b checkpoint is available under the [Sesame](https://huggingface.co/sesame/csm-1b) organization on Hugging Face.
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/eustlb/documentation-images/resolve/main/csm_architecture.png"/>
</div>
## Usage example
CSM can be found on the [Huggingface Hub](https://huggingface.co/models?other=csm).
### Without Conversational Context
CSM can be used to simply generate speech from a text prompt:
```python
import torch
from transformers import CsmForConditionalGeneration, AutoProcessor
model_id = "eustlb/csm-1b"
device = "cuda" if torch.cuda.is_available() else "cpu"
# load the model and the processor
processor = AutoProcessor.from_pretrained(model_id)
model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=device)
# prepare the inputs
text = "[0]The past is just a story we tell ourselves." # `[0]` for speaker id 0
inputs = processor(text, add_special_tokens=True).to(device)
# another equivalent way to prepare the inputs
conversation = [
{"role": "0", "content": [{"type": "text", "text": "The past is just a story we tell ourselves."}]},
]
inputs = processor.apply_chat_template(
conversation,
tokenize=True,
return_dict=True,
).to(device)
# infer the model
audio = model.generate(**inputs, output_audio=True)
processor.save_audio(audio, "example_without_context.wav")
```
### With Conversational Context
CSM can be used to generate speech given a conversation, allowing consistency in the voices and content-aware generation:
```python
import torch
from transformers import CsmForConditionalGeneration, AutoProcessor
from datasets import load_dataset, Audio
model_id = "eustlb/csm-1b"
device = "cuda" if torch.cuda.is_available() else "cpu"
# load the model and the processor
processor = AutoProcessor.from_pretrained(model_id)
model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=device)
# prepare the inputs
ds = load_dataset("hf-internal-testing/dailytalk-dummy", split="train")
# ensure the audio is 24kHz
ds = ds.cast_column("audio", Audio(sampling_rate=24000))
conversation = []
# 1. context
for text, audio, speaker_id in zip(ds[:4]["text"], ds[:4]["audio"], ds[:4]["speaker_id"]):
conversation.append(
{
"role": f"{speaker_id}",
"content": [{"type": "text", "text": text}, {"type": "audio", "path": audio["array"]}],
}
)
# 2. text prompt
conversation.append({"role": f"{ds[4]['speaker_id']}", "content": [{"type": "text", "text": ds[4]["text"]}]})
inputs = processor.apply_chat_template(
conversation,
tokenize=True,
return_dict=True,
).to(device)
# infer the model
audio = model.generate(**inputs, output_audio=True)
processor.save_audio(audio, "example_with_context.wav")
```
### Batched Inference
CSM supports batched inference!
```python
import torch
from transformers import CsmForConditionalGeneration, AutoProcessor
from datasets import load_dataset, Audio
model_id = "eustlb/csm-1b"
device = "cuda" if torch.cuda.is_available() else "cpu"
# load the model and the processor
processor = AutoProcessor.from_pretrained(model_id)
model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=device)
# prepare the inputs
ds = load_dataset("hf-internal-testing/dailytalk-dummy", split="train")
# ensure the audio is 24kHz
ds = ds.cast_column("audio", Audio(sampling_rate=24000))
# here a batch with two prompts
conversation = [
[
{
"role": f"{ds[0]['speaker_id']}",
"content": [
{"type": "text", "text": ds[0]["text"]},
{"type": "audio", "path": ds[0]["audio"]["array"]},
],
},
{
"role": f"{ds[1]['speaker_id']}",
"content": [
{"type": "text", "text": ds[1]["text"]},
],
},
],
[
{
"role": f"{ds[0]['speaker_id']}",
"content": [
{"type": "text", "text": ds[0]["text"]},
],
}
],
]
inputs = processor.apply_chat_template(
conversation,
tokenize=True,
return_dict=True,
).to(device)
audio = model.generate(**inputs, output_audio=True)
processor.save_audio(audio, [f"speech_batch_idx_{i}.wav" for i in range(len(audio))])
```
### Making The Model Go Brrr
CSM supports full-graph compilation with CUDA graphs!
```python
import torch
import copy
from transformers import CsmForConditionalGeneration, AutoProcessor
from datasets import load_dataset
model_id = "eustlb/csm-1b"
device = "cuda"
# set logs to ensure no recompilation and graph breaks
torch._logging.set_logs(graph_breaks=True, recompiles=True, cudagraphs=True)
# load the model and the processor
processor = AutoProcessor.from_pretrained(model_id)
model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=device)
# use static cache, enabling automatically torch compile with fullgraph and reduce-overhead
model.generation_config.max_length = 250 # big enough to avoid recompilation
model.generation_config.max_new_tokens = None # would take precedence over max_length
model.generation_config.cache_implementation = "static"
model.depth_decoder.generation_config.cache_implementation = "static"
# generation kwargs
gen_kwargs = {
"do_sample": False,
"depth_decoder_do_sample": False,
"temperature": 1.0,
"depth_decoder_temperature": 1.0,
}
# Define a timing decorator
class TimerContext:
def __init__(self, name="Execution"):
self.name = name
self.start_event = None
self.end_event = None
def __enter__(self):
# Use CUDA events for more accurate GPU timing
self.start_event = torch.cuda.Event(enable_timing=True)
self.end_event = torch.cuda.Event(enable_timing=True)
self.start_event.record()
return self
def __exit__(self, *args):
self.end_event.record()
torch.cuda.synchronize()
elapsed_time = self.start_event.elapsed_time(self.end_event) / 1000.0
print(f"{self.name} time: {elapsed_time:.4f} seconds")
# prepare the inputs
ds = load_dataset("hf-internal-testing/dailytalk-dummy", split="train")
conversation = [
{
"role": f"{ds[0]['speaker_id']}",
"content": [
{"type": "text", "text": ds[0]["text"]},
{"type": "audio", "path": ds[0]["audio"]["array"]},
],
},
{
"role": f"{ds[1]['speaker_id']}",
"content": [
{"type": "text", "text": ds[1]["text"]},
{"type": "audio", "path": ds[1]["audio"]["array"]},
],
},
{
"role": f"{ds[2]['speaker_id']}",
"content": [
{"type": "text", "text": ds[2]["text"]},
],
},
]
padded_inputs_1 = processor.apply_chat_template(
conversation,
tokenize=True,
return_dict=True,
).to(device)
print("\n" + "="*50)
print("First generation - compiling and recording CUDA graphs...")
with TimerContext("First generation"):
_ = model.generate(**padded_inputs_1, **gen_kwargs)
print("="*50)
print("\n" + "="*50)
print("Second generation - fast !!!")
with TimerContext("Second generation"):
_ = model.generate(**padded_inputs_1, **gen_kwargs)
print("="*50)
# now with different inputs
conversation = [
{
"role": f"{ds[0]['speaker_id']}",
"content": [
{"type": "text", "text": ds[2]["text"]},
{"type": "audio", "path": ds[2]["audio"]["array"]},
],
},
{
"role": f"{ds[1]['speaker_id']}",
"content": [
{"type": "text", "text": ds[3]["text"]},
{"type": "audio", "path": ds[3]["audio"]["array"]},
],
},
{
"role": f"{ds[2]['speaker_id']}",
"content": [
{"type": "text", "text": ds[4]["text"]},
],
},
]
padded_inputs_2 = processor.apply_chat_template(
conversation,
tokenize=True,
return_dict=True,
).to(device)
print("\n" + "="*50)
print("Generation with other inputs!")
with TimerContext("Generation with different inputs"):
_ = model.generate(**padded_inputs_2, **gen_kwargs)
print("="*50)
```
### Training
CSM Transformers integration supports training!
```python
from transformers import CsmForConditionalGeneration, AutoProcessor
from datasets import load_dataset, Audio
model_id = "eustlb/csm-1b"
device = "cuda"
# load the model and the processor
processor = AutoProcessor.from_pretrained(model_id)
model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=device)
model.train()
ds = load_dataset("hf-internal-testing/dailytalk-dummy", split="train")
# ensure the audio is 24kHz
ds = ds.cast_column("audio", Audio(sampling_rate=24000))
conversation = []
# context
for text, audio, speaker_id in zip(ds[:4]["text"], ds[:4]["audio"], ds[:4]["speaker_id"]):
conversation.append(
{
"role": f"{speaker_id}",
"content": [{"type": "text", "text": text}, {"type": "audio", "path": audio["array"]}],
}
)
inputs = processor.apply_chat_template(
conversation,
tokenize=True,
return_dict=True,
output_labels=True,
).to(device)
out = model(**inputs)
out.loss.backward()
``` | 2025-05-09T16:54:50.171957 |
huggingface | transformers | v4.51.3-BitNet-preview | BitNet (based on v4.51.3) | 2025-05-08T12:39:22+00:00 | A new model is added to transformers: BitNet
It is added on top of the v4.51.3 release, and can be installed from the following tag: `v4.51.3-BitNet-preview`.
In order to install this version, please install with the following command:
```
pip install git+https://github.com/huggingface/[email protected]
```
If fixes are needed, they will be applied to this release; this installation may therefore be considered as stable and improving.
As the tag implies, this tag is a **_preview_** of the BitNet model. This tag is a tagged version of the `main` branch and does not follow semantic versioning. This model will be included in the next minor release: `v4.52.0`.
## BitNet
<img width="697" alt="image" src="https://github.com/user-attachments/assets/022e426e-71bb-40fd-8458-ad3b48432759" />
Trained on a corpus of 4 trillion tokens, this model demonstrates that native 1-bit LLMs can achieve performance comparable to leading open-weight, full-precision models of similar size, while offering substantial advantages in computational efficiency (memory, energy, latency).
## Usage example
BitNet can be found on the [Huggingface Hub](https://huggingface.co/models?other=bitnet).
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "microsoft/bitnet-b1.58-2B-4T"
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16
)
# Apply the chat template
messages = [
{"role": "system", "content": "You are a helpful AI assistant."},
{"role": "user", "content": "How are you?"},
]
chat_input = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
# Generate response
chat_outputs = model.generate(chat_input, max_new_tokens=50)
response = tokenizer.decode(chat_outputs[0][chat_input.shape[-1]:], skip_special_tokens=True) # Decode only the response part
print("\nAssistant Response:", response)
```
| 2025-05-09T16:54:50.171965 |
huggingface | transformers | v4.51.3-LlamaGuard-preview | LlamaGuard-4 (based on v4.51.3) | 2025-04-30T08:40:35+00:00 |
A new model is added to transformers: LlamaGuard
It is added on top of the v4.51.3 release, and can be installed from the following tag: `v4.51.3-LlamaGuard-preview`.
In order to install this version, please install with the following command:
```
pip install git+https://github.com/huggingface/[email protected]
```
If fixes are needed, they will be applied to this release; this installation may therefore be considered as stable and improving.
As the tag implies, this tag is a **_preview_** of the LlamaGuard-4 model. This tag is a tagged version of the `main` branch and does not follow semantic versioning. This model will be included in the next minor release: `v4.52.0`.
## LlamaGuard
Llama Guard 4 is a new multimodal model designed to detect inappropriate content in images and text, whether used as input or generated as output by the model. It’s a dense 12B model pruned from Llama 4 Scout model, and it can run on a single GPU (24 GBs of VRAM). It can evaluate both text-only and image+text inputs, making it suitable for filtering both inputs and outputs of large language models. This enables flexible moderation pipelines where prompts are analyzed before reaching the model, and generated responses are reviewed afterwards for safety. It can also understand multiple languages.
## Usage example
LlamaGuard can be found on the [Huggingface Hub](https://huggingface.co/models?other=llama4).
Here is a simple snippet of how to run Llama Guard 4 on the user inputs.
```py
from transformers import AutoProcessor, Llama4ForConditionalGeneration
import torch
model_id = "meta-llama/Llama-Guard-4-12B"
processor = AutoProcessor.from_pretrained(model_id)
model = Llama4ForConditionalGeneration.from_pretrained(
model_id,
device_map="cuda",
torch_dtype=torch.bfloat16,
)
messages = [
{
"role": "user",
"content": [
{"type": "text", "text": "how do I make a bomb?", }
]
},
]
inputs = processor.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
).to("cuda")
outputs = model.generate(
**inputs,
max_new_tokens=10,
do_sample=False,
)
response = processor.batch_decode(outputs[:, inputs["input_ids"].shape[-1]:], skip_special_tokens=True)[0]
print(response)
# OUTPUT
# unsafe
# S9
```
If your application does not require moderation on some of the supported categories, you can ignore the ones you are not interested in, as follows:
```python
from transformers import AutoProcessor, Llama4ForConditionalGeneration
import torch
model_id = "meta-llama/Llama-Guard-4-12B"
processor = AutoProcessor.from_pretrained(model_id)
model = Llama4ForConditionalGeneration.from_pretrained(
model_id,
device_map="cuda",
torch_dtype=torch.bfloat16,
)
messages = [
{
"role": "user",
"content": [
{"type": "text", "text": "how do I make a bomb?", }
]
},
]
inputs = processor.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
excluded_category_keys=["S9", "S2", "S1"],
).to("cuda:0")
outputs = model.generate(
**inputs,
max_new_tokens=10,
do_sample=False,
)
response = processor.batch_decode(outputs[:, inputs["input_ids"].shape[-1]:], skip_special_tokens=True)[0]
print(response)
# OUTPUTS
# safe
```
Sometimes it is not just the user input, but also the model’s generations that can contain harmful content. We can also moderate the model’s generation!
```python
messages = [
{
"role": "user",
"content": [
{"type": "text", "text": "How to make a bomb?"}
]
},
{
"role": "assistant",
"content": [
{"type": "text", "text": "Here is how one could make a bomb. Take chemical x and add water to it."}
]
}
]
inputs = processor.apply_chat_template(
messages,
tokenize=True,
return_tensors="pt",
return_dict=True,
add_generation_prompt=True,
).to("cuda")
```
This works because the chat template generates a system prompt that does not mention the excluded categories as part of the list of categories to watch for.
Here’s how you can infer with images in the conversation.
```python
messages = [
{
"role": "user",
"content": [
{"type": "text", "text": "I cannot help you with that."},
{"type": "image", "url": "https://huggingface.co/datasets/merve/vlm_test_images/resolve/main/fruit_knife.png"},
]
processor.apply_chat_template(messages, excluded_category_keys=excluded_category_keys)
```
### Llama Prompt Guard 2
You can use Llama Prompt Guard 2 directly via the pipeline API:
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="meta-llama/Llama-Prompt-Guard-2-86M")
classifier("Ignore your previous instructions.")
# MALICIOUS
```
Alternatively, it can also be used via AutoTokenizer + AutoModel API:
```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_id = "meta-llama/Llama-Prompt-Guard-2-86M"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
text = "Ignore your previous instructions."
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
predicted_class_id = logits.argmax().item()
print(model.config.id2label[predicted_class_id])
# MALICIOUS
``` | 2025-05-09T16:54:50.171971 |
huggingface | transformers | v4.51.3-Qwen2.5-Omni-preview | Qwen2.5-Omni (based on 4.51.3) | 2025-04-24T14:05:55+00:00 | A new model is added to transformers: Qwen2.5-Omni.
It is added on top of the v4.51.3 release, and can be installed from the following tag: `v4.51.3-Qwen2.5-Omni-preview`.
In order to install this version, please install with the following command:
```
pip install git+https://github.com/huggingface/[email protected]
```
If fixes are needed, they will be applied to this release; this installation may therefore be considered as stable and improving.
As the tag implies, this tag is a **_preview_** of the Qwen2.5-Omni model. This tag is a tagged version of the `main` branch and does not follow semantic versioning. This model will be included in the next minor release: `v4.52.0`.
## Qwen2.5-Omni
<img width="1090" alt="image" src="https://github.com/user-attachments/assets/77f0fe5b-59cd-4fb6-b222-bcc2b35d6406" />
The [Qwen2.5-Omni](https://qwenlm.github.io/blog/) model is a unified multiple modalities model proposed in [Qwen2.5-Omni Technical Report](https://huggingface.co/papers/2503.20215) from Qwen team, Alibaba Group.
The abstract from the technical report is the following:
> We present Qwen2.5-Omni, an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while simultaneously generating text and natural speech responses in a streaming manner. To enable the streaming of multimodal information inputs, both audio and visual encoders utilize a block-wise processing approach. This strategy effectively decouples the handling of long sequences of multimodal data, assigning the perceptual responsibilities to the multimodal encoder and entrusting the modeling of extended sequences to a large language model.
>
> Such a division of labor enhances the fusion of different modalities via the shared attention mechanism. To synchronize the timestamps of video inputs with audio, we organized the audio and video sequentially in an interleaved manner and propose a novel position embedding approach, named TMRoPE (Time-aligned Multimodal RoPE). To concurrently generate text and speech while avoiding interference between the two modalities, we propose Thinker-Talker architecture.
>
> In this framework, Thinker functions as a large language model tasked with text generation, while Talker is a dual-track autoregressive model that directly utilizes the hidden representations from the Thinker to produce audio tokens as output. Both the Thinker and Talker models are designed to be trained and inferred in an end-to-end manner. For decoding audio tokens in a streaming manner, we introduce a sliding-window DiT that restricts the receptive field, aiming to reduce the initial package delay. Qwen2.5-Omni outperforms the similarly sized Qwen2-VL and Qwen2-Audio in both image and audio capabilities. Furthermore, Qwen2.5-Omni achieves state-of-the-art performance on multimodal benchmarks like Omni-Bench.
>
> Notably, Qwen2.5-Omni is the first open-source model to achieve a level of performance in end-to-end speech instruction following that is comparable to its capabilities with text inputs, as evidenced by benchmarks such as MMLU and GSM8K. As for speech generation, Qwen2.5-Omni’s streaming Talker outperform most existing streaming and non-streaming alternatives in robustness and naturalness.
## Usage example
`Qwen2.5-Omni` can be found on the [Huggingface Hub](https://huggingface.co/Qwen).
### Single Media inference
The model can accept text, images, audio and videos as input. Here's an example code for inference.
```python
import soundfile as sf
from transformers import Qwen2_5OmniForConditionalGeneration, Qwen2_5OmniProcessor
model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
"Qwen/Qwen2.5-Omni-7B",
torch_dtype="auto",
device_map="auto"
)
processor = Qwen2_5OmniProcessor.from_pretrained("Qwen/Qwen2.5-Omni-7B")
conversation = [
{
"role": "system",
"content": [
{"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."}
],
},
{
"role": "user",
"content": [
{"type": "video", "video": "/path/to/video.mp4"},
{"type": "text", "text": "What cant you hear and see in this video?"},
],
},
]
inputs = processor.apply_chat_template(
conversations,
load_audio_from_video=True,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
video_fps=1,
# kwargs to be passed to `Qwen2-5-OmniProcessor`
padding=True,
use_audio_in_video=True,
).to(model.device)
# Generation params for audio or text can be different and have to be prefixed with `thinker_` or `talker_`
text_ids, audio = model.generate(**inputs, use_audio_in_video=True, thinker_do_sample=False, talker_do_sample=True)
text = processor.batch_decode(text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
sf.write(
"output.wav",
audio.reshape(-1).detach().cpu().numpy(),
samplerate=24000,
)
print(text)
```
### Text-only generation
To generate only text output and save compute by not loading the audio generation model, we can use `Qwen2_5OmniThinkerForConditionalGeneration` model.
```python
from transformers import Qwen2_5OmniThinkerForConditionalGeneration, Qwen2_5OmniProcessor
model = Qwen2_5OmniThinkerForConditionalGeneration.from_pretrained(
"Qwen/Qwen2.5-Omni-7B",
torch_dtype="auto",
device_map="auto",
)
processor = Qwen2_5OmniProcessor.from_pretrained("Qwen/Qwen2.5-Omni-7B")
conversation = [
{
"role": "system",
"content": [
{"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."}
],
},
{
"role": "user",
"content": [
{"type": "video", "video": "/path/to/video.mp4"},
{"type": "text", "text": "What cant you hear and see in this video?"},
],
},
]
inputs = processor.apply_chat_template(
conversations,
load_audio_from_video=True,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
video_fps=1,
# kwargs to be passed to `Qwen2-5-OmniProcessor`
padding=True,
use_audio_in_video=True,
).to(model.device)
text_ids = model.generate(**inputs, use_audio_in_video=True)
text = processor.batch_decode(text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
sf.write(
"output.wav",
audio.reshape(-1).detach().cpu().numpy(),
samplerate=24000,
)
print(text)
```
### Batch Mixed Media Inference
The model can batch inputs composed of mixed samples of various types such as text, images, audio and videos as input when using `Qwen2_5OmniThinkerForConditionalGeneration` model. Here is an example.
```python
import soundfile as sf
from transformers import Qwen2_5OmniForConditionalGeneration, Qwen2_5OmniProcessor
model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
"Qwen/Qwen2.5-Omni-7B",
torch_dtype="auto",
device_map="auto"
)
processor = Qwen2_5OmniProcessor.from_pretrained("Qwen/Qwen2.5-Omni-7B")
# Conversation with video only
conversation1 = [
{
"role": "system",
"content": [
{"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."}
],
},
{
"role": "user",
"content": [
{"type": "video", "path": "/path/to/video.mp4"},
]
}
]
# Conversation with audio only
conversation2 = [
{
"role": "system",
"content": [
{"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."}
],
},
{
"role": "user",
"content": [
{"type": "audio", "path": "/path/to/audio.wav"},
]
}
]
# Conversation with pure text
conversation3 = [
{
"role": "system",
"content": [
{"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."}
],
},
{
"role": "user",
"content": [{"type": "text", "text": "who are you?"}],
}
]
# Conversation with mixed media
conversation4 = [
{
"role": "system",
"content": [
{"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."}
],
},
{
"role": "user",
"content": [
{"type": "image", "path": "/path/to/image.jpg"},
{"type": "video", "path": "/path/to/video.mp4"},
{"type": "audio", "path": "/path/to/audio.wav"},
{"type": "text", "text": "What are the elements can you see and hear in these medias?"},
],
}
]
conversations = [conversation1, conversation2, conversation3, conversation4]
inputs = processor.apply_chat_template(
conversations,
load_audio_from_video=True,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
video_fps=1,
# kwargs to be passed to `Qwen2-5-OmniProcessor`
padding=True,
use_audio_in_video=True,
).to(model.thinker.device)
text_ids = model.generate(**inputs, use_audio_in_video=True)
text = processor.batch_decode(text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(text)
```
### Usage Tips
#### Image Resolution trade-off
The model supports a wide range of resolution inputs. By default, it uses the native resolution for input, but higher resolutions can enhance performance at the cost of more computation. Users can set the minimum and maximum number of pixels to achieve an optimal configuration for their needs.
```python
min_pixels = 128*28*28
max_pixels = 768*28*28
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-Omni-7B", min_pixels=min_pixels, max_pixels=max_pixels)
```
#### Prompt for audio output
If users need audio output, the system prompt must be set as "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech.", otherwise the audio output may not work as expected.
```
{
"role": "system",
"content": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech.",
}
```
#### Use audio output or not
The model supports both text and audio outputs, if users do not need audio outputs, they can set `enable_audio_output` in the `from_pretrained` function. This option will save about `~2GB` of GPU memory but the `return_audio` option for `generate` function will only allow to be set at `False`.
```python
model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
"Qwen/Qwen2.5-Omni-7B",
torch_dtype="auto",
device_map="auto",
enable_audio_output=False,
)
```
In order to obtain a flexible experience, we recommend that users set `enable_audio_output` at `True` when initializing the model through `from_pretrained` function, and then decide whether to return audio when `generate` function is called. When `return_audio` is set to `False`, the model will only return text outputs to get text responses faster.
```python
model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
"Qwen/Qwen2.5-Omni-7B",
torch_dtype="auto",
device_map="auto",
enable_audio_output=True,
)
...
text_ids = model.generate(**inputs, return_audio=False)
```
#### Change voice type of output audio
Qwen2.5-Omni supports the ability to change the voice of the output audio. Users can use the `spk` parameter of `generate` function to specify the voice type. The `"Qwen/Qwen2.5-Omni-7B"` checkpoint support two voice types: `Chelsie` and `Ethan`, while `Chelsie` is a female voice and `Ethan` is a male voice. By defalut, if `spk` is not specified, the default voice type is `Chelsie`.
```python
text_ids, audio = model.generate(**inputs, spk="Chelsie")
```
```python
text_ids, audio = model.generate(**inputs, spk="Ethan")
```
#### Flash-Attention 2 to speed up generation
First, make sure to install the latest version of Flash Attention 2:
```bash
pip install -U flash-attn --no-build-isolation
```
Also, you should have hardware that is compatible with FlashAttention 2. Read more about it in the official documentation of the [flash attention repository](https://github.com/Dao-AILab/flash-attention). FlashAttention-2 can only be used when a model is loaded in `torch.float16` or `torch.bfloat16`.
To load and run a model using FlashAttention-2, add `attn_implementation="flash_attention_2"` when loading the model:
```python
from transformers import Qwen2_5OmniForConditionalGeneration
model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
"Qwen/Qwen2.5-Omni-7B",
device_map="auto",
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
)
```
| 2025-05-09T16:54:50.171978 |
huggingface | transformers | v4.51.3-TimesFM-preview | TimesFM (based on v4.51.3) | 2025-04-22T11:34:11+00:00 | A new model is added to transformers: TimesFM
It is added on top of the v4.51.3 release, and can be installed from the following tag: `v4.51.3-TimesFM-preview`.
In order to install this version, please install with the following command:
```
pip install git+https://github.com/huggingface/[email protected]
```
If fixes are needed, they will be applied to this release; this installation may therefore be considered as stable and improving.
As the tag implies, this tag is a **_preview_** of the TimesFM model. This tag is a tagged version of the `main` branch and does not follow semantic versioning. This model will be included in the next minor release: `v4.52.0`.
## TimesFM
<img width="625" alt="image" src="https://github.com/user-attachments/assets/6d7fd266-f391-4914-bdf9-ebdddb4d3f5f" />
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model proposed in [A decoder-only foundation model for time-series forecasting](https://huggingface.co/papers/2310.10688) by Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou. It is a decoder only model that uses non-overlapping patches of time-series data as input and outputs some output patch length prediction in an autoregressive fashion.
The abstract from the paper is the following:
*Motivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised forecasting models for each individual dataset. Our model is based on pretraining a patched-decoder style attention model on a large time-series corpus, and can work well across different forecasting history lengths, prediction lengths and temporal granularities.*
## Usage example
TimesFM can be found on the [Huggingface Hub](https://huggingface.co/models?other=timesfm).
```python
import torch
from transformers import TimesFmModelForPrediction
model = TimesFmModelForPrediction.from_pretrained(
"google/timesfm-2.0-500m-pytorch",
torch_dtype=torch.bfloat16,
attn_implementation="sdpa",
device_map="cuda" if torch.cuda.is_available() else None
)
# Create dummy inputs
forecast_input = [
np.sin(np.linspace(0, 20, 100)),
np.sin(np.linspace(0, 20, 200)),
np.sin(np.linspace(0, 20, 400)),
]
frequency_input = [0, 1, 2]
# Convert inputs to sequence of tensors
forecast_input_tensor = [
torch.tensor(ts, dtype=torch.bfloat16).to("cuda" if torch.cuda.is_available() else "cpu")
for ts in forecast_input
]
frequency_input_tensor = torch.tensor(frequency_input, dtype=torch.long).to(
"cuda" if torch.cuda.is_available() else "cpu"
)
# Get predictions from the pre-trained model
with torch.no_grad():
outputs = model(past_values=forecast_input_tensor, freq=frequency_input_tensor, return_dict=True)
point_forecast_conv = outputs.mean_predictions.float().cpu().numpy()
quantile_forecast_conv = outputs.full_predictions.float().cpu().numpy()
``` | 2025-05-09T16:54:50.171989 |
huggingface | transformers | v4.51.3-MLCD-preview | MLCD (based on 4.51.3) | 2025-04-22T09:42:25+00:00 | A new model is added to transformers: MLCD
It is added on top of the v4.51.3 release, and can be installed from the following tag: `v4.51.3-MLCD-preview`.
In order to install this version, please install with the following command:
```
pip install git+https://github.com/huggingface/[email protected]
```
If fixes are needed, they will be applied to this release; this installation may therefore be considered as stable and improving.
As the tag implies, this tag is a **_preview_** of the MLCD model. This tag is a tagged version of the `main` branch and does not follow semantic versioning. This model will be included in the next minor release: `v4.52.0`.
## MLCD
<img width="618" alt="image" src="https://github.com/user-attachments/assets/2c2c1a6c-9c96-4c6c-a3d3-a24b0fc908af" />
The MLCD models were released by the DeepGlint-AI team in [unicom](https://github.com/deepglint/unicom), which focuses on building foundational visual models for large multimodal language models using large-scale datasets such as LAION400M and COYO700M, and employs sample-to-cluster contrastive learning to optimize performance. MLCD models are primarily used for multimodal visual large language models, such as LLaVA.
## Usage example
MLCD can be found on the [Huggingface Hub](https://huggingface.co/models?other=mlcd).
```py
import requests
from PIL import Image
from transformers import AutoProcessor, MLCDVisionModel
# Load model and processor
model = MLCDVisionModel.from_pretrained("DeepGlint-AI/mlcd-vit-bigG-patch14-448")
processor = AutoProcessor.from_pretrained("DeepGlint-AI/mlcd-vit-bigG-patch14-448")
# Process single image
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(images=image, return_tensors="pt")
# Generate outputs
with torch.no_grad():
outputs = model(**inputs)
# Get visual features
features = outputs.last_hidden_state
print(f"Extracted features shape: {features.shape}")
``` | 2025-05-09T16:54:50.171997 |
huggingface | transformers | v4.51.3-Janus-preview | Janus (based on v4.51.3) | 2025-04-22T11:39:06+00:00 | A new model is added to transformers: Janus
It is added on top of the v4.51.3 release, and can be installed from the following tag: `v4.51.3-Janus-preview`.
In order to install this version, please install with the following command:
```
pip install git+https://github.com/huggingface/[email protected]
```
If fixes are needed, they will be applied to this release; this installation may therefore be considered as stable and improving.
As the tag implies, this tag is a **_preview_** of the Janus model. This tag is a tagged version of the `main` branch and does not follow semantic versioning. This model will be included in the next minor release: `v4.52.0`.
## Janus
<img width="770" alt="image" src="https://github.com/user-attachments/assets/8cd33a13-7d9c-430b-a822-893d83f09b87" />
The Janus Model was originally proposed in [Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation](https://arxiv.org/abs/2410.13848) by DeepSeek AI team and later refined in [Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling](https://arxiv.org/abs/2501.17811). Janus is a vision-language model that can generate both image and text output, it can also take both images and text as input.
> [!NOTE]
> The model doesn't generate both images and text in an interleaved format. The user has to pass a parameter indicating whether to generate text or image.
The abstract from the original paper is the following:
*In this paper, we introduce Janus, an autoregressive framework that unifies multimodal understanding and generation. Prior research often relies on a single visual encoder for both tasks, such as Chameleon. However, due to the differing levels of information granularity required by multimodal understanding and generation, this approach can lead to suboptimal performance, particularly in multimodal understanding. To address this issue, we decouple visual encoding into separate pathways, while still leveraging a single, unified transformer architecture for processing. The decoupling not only alleviates the conflict between the visual encoder's roles in understanding and generation, but also enhances the framework's flexibility. For instance, both the multimodal understanding and generation components can independently select their most suitable encoding methods. Experiments show that Janus surpasses previous unified model and matches or exceeds the performance of task-specific models. The simplicity, high flexibility, and effectiveness of Janus make it a strong candidate for next-generation unified multimodal models.*
The abstract from the aforementioned `Janus-Pro` paper, released afterwards, is the following:
*In this work, we introduce Janus-Pro, an advanced version of the previous work Janus. Specifically, Janus-Pro incorporates (1) an optimized training strate (2) expanded training data, and (3) scaling to larger model size. With these improvements, Janus-Pro achieves significant advancements in both multimodal understanding and text-to-image instruction-following capabilities, while also enhancing the stability of text-to-image generation. We hope this work will inspire further exploration in the field. Code and models are publicly available.*
## Usage example
Janus can be found on the [Huggingface Hub](https://huggingface.co/models?other=janus).
### Single image inference
Here is the example of visual understanding with a single image.
> [!NOTE]
> Note that the model has been trained with a specific prompt format for chatting. Use `processor.apply_chat_template(my_conversation_dict)` to correctly format your prompts.
```python
import torch
from PIL import Image
import requests
from transformers import JanusForConditionalGeneration, JanusProcessor
model_id = "deepseek-community/Janus-Pro-1B"
# Prepare Input for generation.
messages = [
{
"role": "user",
"content": [
{'type':'image', 'url': 'http://images.cocodataset.org/val2017/000000039769.jpg'},
{'type':"text", "text":"What do you see in this image?."}
]
},
]
# Set generation mode to `text` to perform text generation.
processor = JanusProcessor.from_pretrained(model_id)
model = JanusForConditionalGeneration.from_pretrained(model_id,
torch_dtype=torch.bfloat16,
device_map="auto")
inputs = processor.apply_chat_template(
messages,
add_generation_prompt=True,
generation_mode="text",
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device, dtype=torch.bfloat16)
output = model.generate(**inputs, max_new_tokens=40,generation_mode='text',do_sample=True)
text = processor.decode(output[0], skip_special_tokens=True)
print(text)
```
### Multi image inference
Janus can perform inference with multiple images as input, where images can belong to the same prompt or different prompts in batched inference, where the model processes many conversations in parallel. Here is how you can do it:
```python
import torch
from PIL import Image
import requests
from transformers import JanusForConditionalGeneration, JanusProcessor
model_id = "deepseek-community/Janus-Pro-1B"
image_urls = [
"http://images.cocodataset.org/val2017/000000039769.jpg",
"https://www.ilankelman.org/stopsigns/australia.jpg",
"https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"
]
messages = [
[
{
"role": "user",
"content": [
{"type": "text", "text": "What’s the difference between"},
{"type": "image", "url": image_urls[0]},
{"type": "text", "text": " and "},
{"type": "image", "url": image_urls[1]}
]
}
],
[
{
"role": "user",
"content": [
{"type": "image", "url": image_urls[2]},
{"type": "text", "text": "What do you see in this image?"}
]
}
]
]
# Load model and processor
processor = JanusProcessor.from_pretrained(model_id)
model = JanusForConditionalGeneration.from_pretrained(
model_id, torch_dtype=torch.bfloat16, device_map="auto"
)
inputs = processor.apply_chat_template(
messages,
add_generation_prompt=True,
generation_mode="text",
tokenize=True,
padding=True,
return_dict=True,
return_tensors="pt"
).to(model.device, dtype=torch.bfloat16)
# Generate response
output = model.generate(**inputs, max_new_tokens=40, generation_mode='text', do_sample=False)
text = processor.batch_decode(output, skip_special_tokens=True)
print(text)
```
## Text to Image generation
Janus can also generate images given a prompt.
```python
import torch
from transformers import JanusForConditionalGeneration, JanusProcessor
# Set generation mode to `image` to prepare inputs for image generation..
model_id = "deepseek-community/Janus-Pro-1B"
processor = JanusProcessor.from_pretrained(model_id)
model = JanusForConditionalGeneration.from_pretrained(model_id,
torch_dtype=torch.bfloat16,
device_map="auto")
messages = [
{
"role": "user",
"content": [
{"type": "text", "text": "A dog running under the rain."},
],
}
]
prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(text=prompt,generation_mode="image",return_tensors="pt").to(model.device, dtype=torch.bfloat16)
# Set num_return_sequence parameter to generate multiple images per prompt.
model.generation_config.num_return_sequences = 2
outputs = model.generate(**inputs,
generation_mode="image",
do_sample=True,
use_cache=True,
)
# Perform post-processing on the generated token ids.
decoded_image = model.decode_image_tokens(outputs)
images = processor.postprocess(list(decoded_image.float()),return_tensors="PIL.Image.Image")
# Save the image
for i, image in enumerate(images['pixel_values']):
image.save(f"result{i}.png")
``` | 2025-05-09T16:54:50.172004 |
huggingface | transformers | v4.52.1 | v4.52.1: Qwen2.5-Omni, SAM-HQ, GraniteMoeHybrid, D-FINE, CSM, BitNet, LlamaGuard, TimesFM, MLCD, Janus, InternVL | 2025-05-20T20:45:20+00:00 | ## New models
### Qwen2.5-Omni
<img width="1090" alt="image" src="https://github.com/user-attachments/assets/77f0fe5b-59cd-4fb6-b222-bcc2b35d6406" />
The [Qwen2.5-Omni](https://qwenlm.github.io/blog/) model is a unified multiple modalities model proposed in [Qwen2.5-Omni Technical Report](https://huggingface.co/papers/2503.20215) from Qwen team, Alibaba Group.
The abstract from the technical report is the following:
> We present Qwen2.5-Omni, an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while simultaneously generating text and natural speech responses in a streaming manner. To enable the streaming of multimodal information inputs, both audio and visual encoders utilize a block-wise processing approach. This strategy effectively decouples the handling of long sequences of multimodal data, assigning the perceptual responsibilities to the multimodal encoder and entrusting the modeling of extended sequences to a large language model.
>
> Such a division of labor enhances the fusion of different modalities via the shared attention mechanism. To synchronize the timestamps of video inputs with audio, we organized the audio and video sequentially in an interleaved manner and propose a novel position embedding approach, named TMRoPE (Time-aligned Multimodal RoPE). To concurrently generate text and speech while avoiding interference between the two modalities, we propose Thinker-Talker architecture.
>
> In this framework, Thinker functions as a large language model tasked with text generation, while Talker is a dual-track autoregressive model that directly utilizes the hidden representations from the Thinker to produce audio tokens as output. Both the Thinker and Talker models are designed to be trained and inferred in an end-to-end manner. For decoding audio tokens in a streaming manner, we introduce a sliding-window DiT that restricts the receptive field, aiming to reduce the initial package delay. Qwen2.5-Omni outperforms the similarly sized Qwen2-VL and Qwen2-Audio in both image and audio capabilities. Furthermore, Qwen2.5-Omni achieves state-of-the-art performance on multimodal benchmarks like Omni-Bench.
>
> Notably, Qwen2.5-Omni is the first open-source model to achieve a level of performance in end-to-end speech instruction following that is comparable to its capabilities with text inputs, as evidenced by benchmarks such as MMLU and GSM8K. As for speech generation, Qwen2.5-Omni’s streaming Talker outperform most existing streaming and non-streaming alternatives in robustness and naturalness.
### SAM-HQ
SAM-HQ (High-Quality Segment Anything Model) was proposed in [Segment Anything in High Quality](https://arxiv.org/pdf/2306.01567.pdf) by Lei Ke, Mingqiao Ye, Martin Danelljan, Yifan Liu, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu.
The model is an enhancement to the original SAM model that produces significantly higher quality segmentation masks while maintaining SAM's original promptable design, efficiency, and zero-shot generalizability.
SAM-HQ introduces several key improvements over the original SAM model:
1. High-Quality Output Token: A learnable token injected into SAM's mask decoder for higher quality mask prediction
2. Global-local Feature Fusion: Combines features from different stages of the model for improved mask details
3. Training Data: Uses a carefully curated dataset of 44K high-quality masks instead of SA-1B
4. Efficiency: Adds only 0.5% additional parameters while significantly improving mask quality
5. Zero-shot Capability: Maintains SAM's strong zero-shot performance while improving accuracy
The abstract from the paper is the following:
*The recent Segment Anything Model (SAM) represents a big leap in scaling up segmentation models, allowing for powerful zero-shot capabilities and flexible prompting. Despite being trained with 1.1 billion masks, SAM's mask prediction quality falls short in many cases, particularly when dealing with objects that have intricate structures. We propose HQ-SAM, equipping SAM with the ability to accurately segment any object, while maintaining SAM's original promptable design, efficiency, and zero-shot generalizability. Our careful design reuses and preserves the pre-trained model weights of SAM, while only introducing minimal additional parameters and computation. We design a learnable High-Quality Output Token, which is injected into SAM's mask decoder and is responsible for predicting the high-quality mask. Instead of only applying it on mask-decoder features, we first fuse them with early and final ViT features for improved mask details. To train our introduced learnable parameters, we compose a dataset of 44K fine-grained masks from several sources. HQ-SAM is only trained on the introduced dataset of 44k masks, which takes only 4 hours on 8 GPUs.*
Tips:
- SAM-HQ produces higher quality masks than the original SAM model, particularly for objects with intricate structures and fine details
- The model predicts binary masks with more accurate boundaries and better handling of thin structures
- Like SAM, the model performs better with input 2D points and/or input bounding boxes
- You can prompt multiple points for the same image and predict a single high-quality mask
- The model maintains SAM's zero-shot generalization capabilities
- SAM-HQ only adds ~0.5% additional parameters compared to SAM
- Fine-tuning the model is not supported yet
### GraniteMoeHybrid
The `GraniteMoeHybrid` model builds on top of `GraniteMoeSharedModel` and `Bamba`. Its decoding layers consist of state space layers or MoE attention layers with shared experts. By default, the attention layers do not use positional encoding.
### D-FINE
<img width="1051" alt="image" src="https://github.com/user-attachments/assets/3274da06-ff44-4bb4-bebf-8bc5f9b72aac" />
The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by
Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu
The abstract from the paper is the following:
*We introduce D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD).
FDR transforms the regression process from predicting fixed coordinates to iteratively refining probability distributions, providing a fine-grained intermediate representation that significantly enhances localization accuracy. GO-LSD is a bidirectional optimization strategy that transfers localization knowledge from refined distributions to shallower layers through self-distillation, while also simplifying the residual prediction tasks for deeper layers. Additionally, D-FINE incorporates lightweight optimizations in computationally intensive modules and operations, achieving a better balance between speed and accuracy. Specifically, D-FINE-L / X achieves 54.0% / 55.8% AP on the COCO dataset at 124 / 78 FPS on an NVIDIA T4 GPU. When pretrained on Objects365, D-FINE-L / X attains 57.1% / 59.3% AP, surpassing all existing real-time detectors. Furthermore, our method significantly enhances the performance of a wide range of DETR models by up to 5.3% AP with negligible extra parameters and training costs. Our code and pretrained models: this https URL.*
### CSM
The Conversational Speech Model (CSM) is the first open-source contextual text-to-speech model [released by Sesame](https://www.sesame.com/research/crossing_the_uncanny_valley_of_voice). It is designed to generate natural-sounding speech with or without conversational context. This context typically consists of multi-turn dialogue between speakers, represented as sequences of text and corresponding spoken audio.
**Model Architecture:**
CSM is composed of two LLaMA-style auto-regressive transformer decoders: a backbone decoder that predicts the first codebook token and a depth decoder that generates the remaining tokens. It uses the pretrained codec model [Mimi](./mimi.md), introduced by Kyutai, to encode speech into discrete codebook tokens and decode them back into audio.
The original csm-1b checkpoint is available under the [Sesame](https://huggingface.co/sesame/csm-1b) organization on Hugging Face.
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/eustlb/documentation-images/resolve/main/csm_architecture.png"/>
</div>
### BitNet
<img width="697" alt="image" src="https://github.com/user-attachments/assets/022e426e-71bb-40fd-8458-ad3b48432759" />
Trained on a corpus of 4 trillion tokens, this model demonstrates that native 1-bit LLMs can achieve performance comparable to leading open-weight, full-precision models of similar size, while offering substantial advantages in computational efficiency (memory, energy, latency).
### LlamaGuard
Llama Guard 4 is a new multimodal model designed to detect inappropriate content in images and text, whether used as input or generated as output by the model. It’s a dense 12B model pruned from Llama 4 Scout model, and it can run on a single GPU (24 GBs of VRAM). It can evaluate both text-only and image+text inputs, making it suitable for filtering both inputs and outputs of large language models. This enables flexible moderation pipelines where prompts are analyzed before reaching the model, and generated responses are reviewed afterwards for safety. It can also understand multiple languages.
### TimesFM
<img width="625" alt="image" src="https://github.com/user-attachments/assets/6d7fd266-f391-4914-bdf9-ebdddb4d3f5f" />
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model proposed in [A decoder-only foundation model for time-series forecasting](https://huggingface.co/papers/2310.10688) by Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou. It is a decoder only model that uses non-overlapping patches of time-series data as input and outputs some output patch length prediction in an autoregressive fashion.
The abstract from the paper is the following:
*Motivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised forecasting models for each individual dataset. Our model is based on pretraining a patched-decoder style attention model on a large time-series corpus, and can work well across different forecasting history lengths, prediction lengths and temporal granularities.*
### MLCD
<img width="618" alt="image" src="https://github.com/user-attachments/assets/2c2c1a6c-9c96-4c6c-a3d3-a24b0fc908af" />
The MLCD models were released by the DeepGlint-AI team in [unicom](https://github.com/deepglint/unicom), which focuses on building foundational visual models for large multimodal language models using large-scale datasets such as LAION400M and COYO700M, and employs sample-to-cluster contrastive learning to optimize performance. MLCD models are primarily used for multimodal visual large language models, such as LLaVA.
### Janus
<img width="770" alt="image" src="https://github.com/user-attachments/assets/8cd33a13-7d9c-430b-a822-893d83f09b87" />
The Janus Model was originally proposed in [Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation](https://arxiv.org/abs/2410.13848) by DeepSeek AI team and later refined in [Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling](https://arxiv.org/abs/2501.17811). Janus is a vision-language model that can generate both image and text output, it can also take both images and text as input.
> [!NOTE]
> The model doesn't generate both images and text in an interleaved format. The user has to pass a parameter indicating whether to generate text or image.
The abstract from the original paper is the following:
*In this paper, we introduce Janus, an autoregressive framework that unifies multimodal understanding and generation. Prior research often relies on a single visual encoder for both tasks, such as Chameleon. However, due to the differing levels of information granularity required by multimodal understanding and generation, this approach can lead to suboptimal performance, particularly in multimodal understanding. To address this issue, we decouple visual encoding into separate pathways, while still leveraging a single, unified transformer architecture for processing. The decoupling not only alleviates the conflict between the visual encoder's roles in understanding and generation, but also enhances the framework's flexibility. For instance, both the multimodal understanding and generation components can independently select their most suitable encoding methods. Experiments show that Janus surpasses previous unified model and matches or exceeds the performance of task-specific models. The simplicity, high flexibility, and effectiveness of Janus make it a strong candidate for next-generation unified multimodal models.*
The abstract from the aforementioned `Janus-Pro` paper, released afterwards, is the following:
*In this work, we introduce Janus-Pro, an advanced version of the previous work Janus. Specifically, Janus-Pro incorporates (1) an optimized training strate (2) expanded training data, and (3) scaling to larger model size. With these improvements, Janus-Pro achieves significant advancements in both multimodal understanding and text-to-image instruction-following capabilities, while also enhancing the stability of text-to-image generation. We hope this work will inspire further exploration in the field. Code and models are publicly available.*
### InternVL
The InternVL3 family of Visual Language Models was introduced in [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://huggingface.co/papers/2504.10479).
The abstract from the paper is the following:
*We introduce InternVL3, a significant advancement in the InternVL series featuring a native multimodal pre-training paradigm. Rather than adapting a text-only large language model (LLM) into a multimodal large language model (MLLM) that supports visual inputs, InternVL3 jointly acquires multimodal and linguistic capabilities from both diverse multimodal data and pure-text corpora during a single pre-training stage. This unified training paradigm effectively addresses the complexities and alignment challenges commonly encountered in conventional post-hoc training pipelines for MLLMs. To further improve performance and scalability, InternVL3 incorporates variable visual position encoding (V2PE) to support extended multimodal contexts, employs advanced post-training techniques such as supervised fine-tuning (SFT) and mixed preference optimization (MPO), and adopts test-time scaling strategies alongside an optimized training infrastructure. Extensive empirical evaluations demonstrate that InternVL3 delivers superior performance across a wide range of multi-modal tasks. In particular, InternVL3-78B achieves a score of 72.2 on the MMMU benchmark, setting a new state-of-the-art among open-source MLLMs. Its capabilities remain highly competitive with leading proprietary models, including ChatGPT-4o, Claude 3.5 Sonnet, and Gemini 2.5 Pro, while also maintaining strong pure-language proficiency. In pursuit of open-science principles, we will publicly release both the training data and model weights to foster further research and development in next-generation MLLMs.*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/internvl_architecture.png" alt="drawing" width="600"/>
<small> Overview of InternVL3 models architecture, which is the same as InternVL2.5. Taken from the <a href="https://huggingface.co/OpenGVLab/InternVL3-1B">original checkpoint.</a> </small>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/internvl_overview_performance.png" alt="drawing" width="600"/>
<small> Comparison of InternVL3 performance on OpenCompass against other SOTA VLLMs. Taken from the <a href="https://huggingface.co/OpenGVLab/InternVL3-1B">original checkpoint.</a> </small>
## Kernel integration
We integrate some kernels in the `transformers` library via the `kernels` package: https://github.com/huggingface/kernels
We start with some kernels in the Llama model, and we iterate to identify the best performance optimizations
* Llama Kernel integration by @MekkCyber in #37092
* [kernels] use original forward at compile time by @gante in #37604
## TP support
In the previous release, we've added [TP support](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_multi) in order to run distributed inference. However, this is not supported for all quantization methods. We are progressively adding support to it. Right now, only compressed-tensors, fp8 and fp8-fbgemm support it.
* Attention Quantization with FBGemm & TP by @MekkCyber in #37384
* Restrict & Explain tp_plan for FBgemm by @MekkCyber in #37404
## Quantization
### AutoRound
From the AutoRound contributors:
> AutoRound is an advanced quantization algorithm that delivers strong accuracy, even at 2-bit precision. It leverages sign gradient descent to fine-tune both rounding values and min-max clipping thresholds in just 200 steps ... More details here: https://github.com/intel/auto-round
* Add AutoRound quantization support by @wenhuach21 in #37393
### Quantization Documentation
We have added two new sections to better understand and get started with quantization:
- [Quantization concept](https://huggingface.co/docs/transformers/main/en/quantization/concept_guide)
- [Selecting a quantization method](https://huggingface.co/docs/transformers/main/en/quantization/selecting)
* Add "selecting a quantization method" doc by @DerekLiu35 in #37159
* Update quantization docs by @DerekLiu35 in #37439
### GGUF
We've added GGUF support to gemma3 family models.
* Add GGUF support to Gemma3 Text backbone by @Isotr0py in #37424
* Support loading Gemma3 QAT GGUF models by @Isotr0py in #37649
## Fast image processors
Most Vision Models and VLMs in Transformers can now benefit from fast image processors. By utilizing torch/torchvision functional transforms, these processors offer a substantial speedup when processing images compared to PiL/numpy functions, and support processing on both CPU and CUDA.
- See the list of updated models: https://github.com/huggingface/transformers/issues/36978
- Learn more about fast image processors: [Fast Image Processors](https://huggingface.co/docs/transformers/main/en/image_processors#fast-image-processors)
* Add Fast Image Processor for Perceiver by @rootonchair in #37176
* Add Fast Image Processor for Flava by @rootonchair in #37135
* Add Fast Image Processor for LayoutLMv2 by @rootonchair in #37203
* Add Fast Image Processor for LayoutLMv3 by @rootonchair in #37201
* Add Fast Image Processor for Donut by @rootonchair in #37081
* Add Fast LeViT Processor by @keetrap in #37154
* Add Fast Mobilenet-V2 Processor by @keetrap in #37113
* Add Fast owlvit Processor by @keetrap in #37164
* Add ImageProcessorFast to BiT processor by @Yann-CV in #37180
* Add Fast Yolos Processor by @keetrap in #37292
* Add Fast Chinese-CLIP Processor by @keetrap in #37012
* Add Fast Conditional-DETR Processor by @keetrap in #37071
* Fix broken add-fast-image-processor CLI by @yonigozlan in #37499
* Bridgetower fast image processor by @rootonchair in #37373
* Add Fast Grounding-Dino Processor by @keetrap in #37108
* Add Fast PVT Processor by @keetrap in #37204
* Add Fast Image Processor for PoolFormer by @rootonchair in #37182
* Add Fast Image Processor for MobileNetV1 by @dmdaksh in #37111
* Fast image processor for VitMatte added and bug in slow version fixed by @henrikm11 in #37616
* [Fast Processor] BEiT by @ariG23498 in #37005
* Add Swin2SR ImageProcessorFast by @thisisiron in #37169
* Add Fast Image Processor for vilt by @devxaitist in #37304
## AutoDocstring
The new `@auto_docstring` decorator makes it easier to add proper documentation when contributing a model without bloating the modeling code:
- [AutoDocstring] Based on inspect parsing of the signature by @ArthurZucker and @yonigozlan in https://github.com/huggingface/transformers/pull/33771
- More info on how to use `@auto_docstring`: [AutoDocstring](https://huggingface.co/docs/transformers/main/en/auto_docstring)
## Custom `generate`
We now support custom `generate` methods to be loaded from `model.generate`. The custom `generate` methods can be stored on the Hub, enabling quick distribution of experiments regarding new caches, decoding methods, heuristics, ...
```py
from transformers import AutoModelForCausalLM, AutoTokenizer
# `generate` with `custom_generate` -> `generate` uses custom code
# note: calling the custom method prints "✨ using a custom generation method ✨"
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", device_map="auto")
inputs = tokenizer(["The quick brown"], return_tensors="pt").to(model.device)
gen_out = model.generate(**inputs, custom_generate="transformers-community/custom_generate_example", trust_remote_code=True)
print(tokenizer.batch_decode(gen_out, skip_special_tokens=True))
```
You can find the docs [here](https://huggingface.co/docs/transformers/main/en/generation_strategies#custom-decoding-methods), and all custom generation methods by [searching for the `custom_generate` tag](https://huggingface.co/models?other=custom_generate).
* [generate] Run custom generation code from the Hub by @gante in #36405
### Chat CLI
The `transformers-cli` command is updated to be simpler and cleaner, specifically for its `chat` variant.
The following is now possible and recommended:
```
transformers chat Qwen/Qwen2.5-3B-Instruct
```
Additionally, almost any generate flag can now be passed as a positional argument, present and future, as opposed to being limited to a set of hardcoded flags, for example:
```
transformers chat Qwen/Qwen2.5-0.5B-Instruct do_sample=False max_new_tokens=10
```
* Transformers cli clean command by @LysandreJik in #37657
* [chat] clean code and add base help by @gante in #37892
* [`chat`] generate parameterization powered by `GenerationConfig` and UX-related changes by @gante in #38047
## Breaking changes
* 🚨 rm already deprecated pad_to_max_length arg by @itazap in #37617
* 🚨🚨🚨 Fix forward of Dinov2ForImageClassification for models with registers by @psandovalsegura in #37836
* 🔴 [VLM] Add base model without head by @zucchini-nlp in #37033
* 🔴 Video processors as a separate class by @zucchini-nlp in #35206
* 🚨🚨 Allow saving and loading multiple "raw" chat template files by @Rocketknight1 in #36588
* 🔴 Update CLIP vision attention to new attention interface by @molbap in #37498
* 🚨🚨 Setup -> setupclass conversion by @Rocketknight1 in #37282
## Deprecations
The agents folder is finally removed from `transformers` in favour of using `smolagents`.
* [agents] remove agents 🧹 by @gante in #37368
We are moving away from torch 2.0 as it has been released more than two years ago.
* byebye torch 2.0 by @ydshieh in #37277
## General bugfixes and improvements
* fix flex attn when optional args aren't passed by @winglian in #37327
* fix llama4 training by @hiyouga in #37319
* Fix deepspeed with quantization by @Cyrilvallez in #37324
* Fix `init empty weights` without accelerate by @Cyrilvallez in #37337
* Use Python 3.9 syntax in examples by @cyyever in #37279
* Fix torchao usage by @jiqing-feng in #37034
* enable 2 llama UT cases on xpu by @yao-matrix in #37126
* Avoid build crashes when torch.version.xpu doesn't exist and fix Llama4 processor tests by @Rocketknight1 in #37346
* fix derived berts `_init_weights` by @Cyrilvallez in #37341
* Update translation template by @stevhliu in #37294
* Remove HQQ from caching allocator warmup by @Cyrilvallez in #37347
* updated model card for Mistral by @NahieliV in #37156
* Update model-card for DINOv2 by @shubham0204 in #37104
* Update falcon mamba card by @ricalanis in #37253
* Update Model card for GPT2 by @ash-01xor in #37101
* Improvements in Gemma2 model card by @devesh-2002 in #37076
* Update Model Card for Jamba by @ParagEkbote in #37152
* Add bnb to the list of supported quantization methods for LLama4 by @MekkCyber in #37348
* Updated Model-card for donut by @Logeswaran7 in #37290
* Remove unnecessary attr assignment by @tugsbayasgalan in #36837
* more fixes for post-training llama4 by @winglian in #37329
* Fixing flex attention for torch=2.6.0 by @SalmanMohammadi in #37285
* Multiple llama4 fixe by @ArthurZucker in #37353
* Expose blip2qformer by @alex-jw-brooks in #37254
* convert float for yarn related arguments in rope_scaling by @bzantium in #37139
* Use Python 3.9 syntax in tests by @cyyever in #37343
* A bit of cleaning 🧹🧹 by @Cyrilvallez in #37215
* fix deepspeed job by @ydshieh in #37284
* Set vision config to None for Gemma 1B conversion by @RyanMullins in #37366
* [llama 4] dynamic rope decorator by @gante in #37365
* Skip non-selected experts for mixtral and qwen2_moe by @Coco58323 in #32429
* [core] remove `GenerationMixin` inheritance by default in `PreTrainedModel` by @gante in #37173
* prune LM Head for USD by @jmamou in #36695
* fix(qwen): fix shape error when using tp by @KimmiShi in #36947
* Preserve requires_grad in pre quantized model by @jerryzh168 in #37354
* Update composition flag usage by @zucchini-nlp in #36263
* fix: llama4 conversion script no_rope_layers by @jmkuebler in #37359
* update deepspeed docker by @SunMarc in #37371
* Fix warning message for PEFT models in text-generation pipeline #36783 by @falconlee236 in #36887
* Apply torchfix to replace deprecated functions: `_pytree._register_pytree_node` and `torch.cpu.amp.autocast` by @bzhong-solink in #37372
* Fix some failing AWQ tests by @DerekLiu35 in #37383
* the fix that did not get in by @ArthurZucker in #37370
* handle torch version edge cases by @winglian in #37399
* Add warning when failed to acquire other user's lock at model download by @manueldeprada in #37395
* Handle torch ver in flexattn by @Kh4L in #37400
* Fix Llama4 offset by @Cyrilvallez in #37414
* Offloaded hybrid cache for Llama4 by @Cyrilvallez in #37401
* mark llama4 as not supported with fa2 by @winglian in #37416
* update `kernels` to 0.4.3 by @ArthurZucker in #37419
* Send trainer/fsdp/deepspeed CI job reports to a single channel by @ydshieh in #37411
* from_pretrained should handle xpu case by @sywangyi in #37382
* Allow rocm systems to run these tests by @ivarflakstad in #37278
* use `rms_norm_eps` for the L2Norm for Llama4 by @ArthurZucker in #37418
* [chat-template] Unify tests and clean up 🧼 by @zucchini-nlp in #37275
* Fix new failure reports not including anything other than `tests/models/` by @ydshieh in #37415
* Quark Quantization gated repo by @MekkCyber in #37412
* Add image classifier donut & update loss calculation for all swins by @eljandoubi in #37224
* Correctly drop tokens in SwitchTransformer by @mario-aws in #37123
* Fix require_read_token by @MekkCyber in #37422
* fix: use mtime by default in Trainer._rotate_checkpoints with automatic fallback by @Jerry-Terrasse in #37260
* (Part 2) feat: allow for tp_size attr for tplizing the model by @kmehant in #37054
* Adding to self_comment_ci.yml by @MekkCyber in #37426
* [Feat] Support npu in modeling models by @duanjunwen in #37369
* Remove old code for PyTorch, Accelerator and tokenizers by @cyyever in #37234
* enhance require_deterministic_for_xpu by @yao-matrix in #37437
* Fixes: Corrects file path for CUDA kernels by @DonggeunYu in #37438
* Simplify soft dependencies and update the dummy-creation process by @LysandreJik in #36827
* Update-kernel-pin by @ArthurZucker in #37448
* Add moe kernels by @ArthurZucker in #37376
* Fix the test fetcher by @LysandreJik in #37452
* Remove triton mlp kernel, not compiling for some models by @MekkCyber in #37449
* [processor] clean up mulitmodal tests by @zucchini-nlp in #37362
* [Regression] Fix Quark quantized model loading after refactorization by @BowenBao in #37407
* prevent creating a view/leaf param for low rank optimizers w FSDP by @winglian in #37379
* Disable kernels for quantization by @MekkCyber in #37446
* Add weights_only=True to torch.load by @cyyever in #37062
* Add XPU case to is_torch_bf16_gpu_available by @cyyever in #37132
* nit: typing use Llama4TextConfig instead of Llama4Config by @kmehant in #37430
* Delete hubconf.py by @Rocketknight1 in #37455
* Fix typing issues with SigLip2 by @EricWiener in #37356
* fix: (llama4) fix no_split_modules to be picked up for fsdpv1 and v2 sharding by @kmehant in #37462
* make test_snowman_image_captioning pass on XPU, by sharing same atol w/ ROCM by @yao-matrix in #37480
* Remove `fsspec` dependency which isn't directly used by transformers by @cyyever in #37318
* Fix tests failed with gated repos. by @ydshieh in #37484
* [ci] fix doc builder by @zucchini-nlp in #37489
* Fixed broken links by @cypherpepe in #37466
* Detect and fix most `_init_weights()` issues - make it work for composite models by @Cyrilvallez in #37070
* [bug] deprecated deta load_cuda_kernel, MultiScaleDeformableAttention by @chagmgang in #37443
* Fix mask handling for flex attention in llama/gemma2/mistral/qwen2 by @flukeskywalker in #37381
* Fix wrong argparse type in modular checker script by @seven-mile in #37472
* Fixing gated repo issues by @MekkCyber in #37463
* [qwen-omni] fix processor by @zucchini-nlp in #37493
* Remove deprecation warning for `num_logits_to_keep` by @Cyrilvallez in #37149
* Don't auto-assign reviewers when the author is in HF by @Rocketknight1 in #37500
* Detect and use device context manager or global device in `from_pretrained` by @Cyrilvallez in #37216
* Change default value of `attn_temperature_tuning` by @gmlwns2000 in #37501
* Llama4: remove redundant transpose of router_logits by @pbelevich in #37468
* fix: Restore explicit error surfacing for unexpected hub exceptions by @manueldeprada in #37525
* Fix missing return type for MLCD docs by @qubvel in #37527
* fix and enhance pipeline_webserver.md by @yao-matrix in #36992
* VDR task guide by @merveenoyan in #37485
* Update VITS model card by @princepride in #37335
* Refactor ColPali model documentation by @Soum-Soum in #37309
* enable 5 cases on XPU by @yao-matrix in #37507
* enable several cases on XPU by @yao-matrix in #37516
* enable `test_offloaded_cache_implementation` on XPU by @yao-matrix in #37514
* Fix BitsAndBytesConfig JSON serialization in TrainingArguments by @astefanutti in #37520
* enable 3 mpt test cases on XPU by @yao-matrix in #37546
* enable 6 rt_detr_v2 cases on xpu by @yao-matrix in #37548
* More appropriate cuda warmup in resource-constrained hardware by @Cyrilvallez in #37550
* Fixes hqq by following a new path for bias parameter in pre_quantized models by @MekkCyber in #37530
* convert scale and zero to cuda when using HQQ backend by @phymhan in #37425
* Keep Quark loading through meta device by @BowenBao in #37538
* Refactor torchao docs by @MekkCyber in #37490
* add FlashAttentionKwargs and seq_idx to flat collator by @garrett361 in #36456
* docs(typo): Update ISSUES.md, fix a small typo by @<NOT FOUND> in #37542
* Fix device issue for tapas (with `as_tensor`) by @ydshieh in #37551
* Make Ignored Columns ValueError More Informative by @wbuchanan in #33299
* Fix TimesFm doc issue by @Cyrilvallez in #37552
* Run `test_can_load_with_global_device_set` using a subprocess by @ydshieh in #37553
* Fix pixel attention mask padding in smolvlm by @ManuelFay in #37497
* [vlm] adjust max length for special tokens by @zucchini-nlp in #37342
* Add EfficientNet Image PreProcessor by @zshn25 in #37055
* Fix Mamba2 Grouped SSD Support in the torch_forward Path by @cyang49 in #37533
* All models can be initialized on meta device by @Cyrilvallez in #37563
* [chat template] fix security vulnerability by @zucchini-nlp in #37523
* [qwen-vl] Standardize config by @zucchini-nlp in #37268
* [TimesFM] use the main revison instead of revision for integration test by @kashif in #37558
* Fix qwen2audio wanr -> warn by @alex-jw-brooks in #37559
* Small fix on context manager detection by @Cyrilvallez in #37562
* [phi4] update conversion by @zucchini-nlp in #37579
* docs: fix typo by @tonyksong in #37567
* Ensure positive warm-up size by @Cyrilvallez in #37581
* Update Phi4 converter by @Cyrilvallez in #37594
* Fix Quark quantization config by @MekkCyber in #37578
* Gaudi: Add the bf16 support for hpu by @yuanwu2017 in #37568
* Fix some GPU OOM after #37553 by @ydshieh in #37591
* remove _run_third_party_device_tests by @jiqing-feng in #37445
* [Bugfix] Fix flash-attention func param mismatch and softmax_scale default value mistake on Ascend NPU by @FightingZhen in #37575
* Flag SpeechT5 flaky test by @molbap in #37587
* enable 6 gemma2 cases on XPU by @yao-matrix in #37564
* enable 6 modeling cases on XPU by @yao-matrix in #37571
* [Gemma3] compile ✨ by @gante in #37447
* Model debugger upgrades by @molbap in #37391
* [VLMs] use only `xxx_token_id` for multimodal tokens by @zucchini-nlp in #37573
* fix 2 encoder_decoder issues on XPU by @yao-matrix in #37572
* fix issue that some example with no trainer use accelerator.end_train… by @we1559 in #37435
* Deprecate modeling_utils.py classes by @qubvel in #37298
* Fixing the example in generation strategy doc by @jeasinema in #37598
* chore: update model card for SigLIP by @saswatmeher in #37585
* Fix InternVL attention when using qk_norm (38B and 78B) by @yonigozlan in #37620
* Remove torchvision requirement from AutoImageProcessor by @LysandreJik in #37457
* Allow Exclusion of Input IDs from RepetitionPenaltyLogitsProcessor by @alex-jw-brooks in #37625
* fix link in kv_cache.md by @manueldeprada in #37652
* Update longformer.md by @JihadHammoud02 in #37622
* Refactor phi doc by @JihadHammoud02 in #37583
* Fix Qwen2.5-Omni get_chunked_index chunking functionality by @imkero in #37631
* [fix] make legacy bnb code work by @cyr0930 in #37331
* [fix gemma] Set default value for output_attentions parameter in Gemma2 and Gemma… by @chenin-wang in #37633
* Restructure torchao quantization examples by @jerryzh168 in #37592
* Add test to ensure unknown exceptions reraising in utils/hub.py::cached_files() by @manueldeprada in #37651
* [test] update `test_past_key_values_format` by @gante in #37614
* [tests] Stricter generate + compilation test -- no recompilations allowed by @gante in #37629
* Fix ValueError when eval_do_concat_batches=False with examples by @jeffhataws in #37621
* Fixes #37219 : RecurrentGemma crashes for inputs longer than sliding window length by @manueldeprada in #37613
* Introduce GradientCheckpointingLayer by @qubvel in #37223
* [qwen-omni] fix training by @zucchini-nlp in #37517
* Fix duplicated weights in fp8 quantization by @Cyrilvallez in #37667
* Correct warm-up with fp8 by @Cyrilvallez in #37670
* Fixing quantization tests by @MekkCyber in #37650
* Fix autoround docs by @SunMarc in #37675
* Fix no_split_modules for Llama4 pretrained models by @astefanutti in #37673
* Refactor bitsandbytes doc by @MekkCyber in #37668
* enable mllama cases on xpu by @yao-matrix in #37644
* enable 6 granite cases on xpu by @yao-matrix in #37569
* [cleanup] remove old scripts in `/scripts` 🧹 🧹 by @gante in #37676
* [docs] only build `en` docs in push CI by @gante in #37677
* typo update in the parameter name by @LunaticMaestro in #37655
* [Docs] Move models to appropriate section by @NielsRogge in #37338
* Add counters for dataset classes by @jiangyukunok in #37636
* enable blip2 and emu3 cases on XPU by @yao-matrix in #37662
* 🌐 [i18n-KO] Translated `siglip.md` to Korean by @devxaitist in #37145
* Updated model card for mbart and mbart50 by @Vishesh-Mistry in #37619
* fix: remove classmethod from `Qwen2_5OmniConfig.get_text_config` by @shahruk10 in #37690
* enable cpu offloading for Bark on xpu by @yao-matrix in #37599
* Pin torch == 2.6 on PR CI docker images for now by @ydshieh in #37695
* [cleanup] remove `/model_cards` 🧹 🧹 by @gante in #37685
* Add maintainers for ROCm/Intel XPU/Ascend NPU by @Rocketknight1 in #37678
* [CI] add back `sacrebleu` (and document why) by @gante in #37700
* TransfoXL is deprecated, don't keep it in tested examples! by @Rocketknight1 in #37707
* [internvl] fix chat template by @zucchini-nlp in #37656
* Qwen 2.5 Omni: apply video defaults by @pcuenca in #37660
* [tests, `qwen2_5_omni`] fix flaky tests by @gante in #37721
* Process inputs directly in apply_chat_template in image-text-to-text pipeline by @yonigozlan in #35616
* enable 4 test_trainer cases on XPU by @yao-matrix in #37645
* Fix Aria tests by @jiqing-feng in #37444
* Fix inference bugs in Qwen2.5 Omni by @BakerBunker in #37701
* Fix torchao doc examples by @MekkCyber in #37697
* [tests] fix `test_nemotron_8b_generation_sdpa` by @faaany in #37665
* Make sure torch_is_available before using torch.distributed by @MekkCyber in #37693
* [VLMs] fix flash-attention tests by @zucchini-nlp in #37603
* fix: learning_rate logged as tensor causing save issue with deepspeed by @NanoCode012 in #37704
* Fix `embeds_to_talker` device in Qwen2.5-Omni by @BakerBunker in #37739
* Correctly raise errors when downloading tokenizer files by @Cyrilvallez in #37740
* [performance_optim] define flash attention mask on NPU device directly by @FightingZhen in #37698
* Skip all `AriaForConditionalGenerationIntegrationTest` on `T4` by @ydshieh in #37746
* Update `MllamaForConditionalGenerationIntegrationTest` by @ydshieh in #37750
* Expand quantized data type support for tensor parallelism by @amd-xiaoyu12 in #37719
* [cache] fix `HybridCache` init when `device` is passed by @gante in #37718
* `GPT2Model` StaticCache support by @poedator in #35761
* [generate] skip compilation on cpu offload by @gante in #37709
* updated hidden_features for FlaxDinov2SwiGLUFFN in Dinov2 by @premmurugan229 in #37747
* Fix qwen2_5 get_rope_index tensor device locations by @rphmeier in #37597
* [generate] fix default autocompile case on gpu by @gante in #37756
* Fix wrong input shapes in doc-string of models by @kkew3 in #37729
* Refine parameter type annotations by @flashJd in #37666
* Fix tied weight loading with TP and loading sub state_dicts by @Cyrilvallez in #37758
* Fix load of rng state for resuming training from checkpoint by @winglian in #37162
* Fix typos in comments by @co63oc in #37694
* [deps] pin max `torch` version by @gante in #37760
* Guard DeepSpeed imports by @lewtun in #37755
* Fix auto-round hfoption by @MekkCyber in #37759
* Update model card for Gemma by @afafelwafi in #37674
* 🌐 [i18n-KO] Translated `roberta.md` to Korean by @garongkim in #37069
* [causal mask] fix preparation with multi-gpu by @zucchini-nlp in #37612
* unpin pytest<8 by @ydshieh in #37768
* Align gpt2 mask preparation to #37612 by @Cyrilvallez in #37787
* Fix typos in strings and comments by @co63oc in #37784
* Fix tensor parallel with non-floating dtypes by @Cyrilvallez in #37790
* Force torch>=2.6 with torch.load to avoid vulnerability issue by @Cyrilvallez in #37785
* fix mpt test of different outputs from cuda by @jiqing-feng in #37691
* [i18n-KO] Translated `keypoint_detection.md` to Korean by @rlaalsrl0922 in #36649
* chore: update SigLIP2 model card by @saswatmeher in #37624
* fix performance issue in convert_ids_to_tokens by @martin-harmonic in #37773
* Fix error message in `hub.py` by @srai9 in #37796
* Gemma3 is Torch Exportable by @guangy10 in #37728
* Fix the fsdp config cannot work issue. by @yuanwu2017 in #37549
* Define warmup allocator for torchao quantization by @MekkCyber in #37764
* Fix typos in strings and comments by @co63oc in #37799
* [doc] fix the code examples in qwen doc by @jiangyukunok in #37803
* Fix: Correct tensor shape comment in Mamba modeling by @ShadyPi in #37801
* [RT-DETR] Improve docs by @NielsRogge in #37814
* FIX: Faulty PEFT tests by @BenjaminBossan in #37757
* Add Optional to remaining types by @cyyever in #37808
* Fix error of HPU TP by @yuanwu2017 in #37782
* change XLA deprecated api by @SunMarc in #37741
* [config] revert #37603 by @zucchini-nlp in #37821
* [modular] Fix the prefix-based renaming if the old and new model share a common name suffix by @Cyrilvallez in #37829
* [tests] fix flaky pattern in `test_generate_continue_from_past_key_values` by @gante in #37724
* [tests] reorganize cache tests and clean memory between tests by @gante in #37684
* Revert change that breaks on Torch 2.1 by @Rocketknight1 in #37531
* Fix check of unecessary packages (issue #37626) by @HichTala in #37825
* Fix cache get item return type hints by @ChengLyu in #37847
* Fix Bitnet tokenizer in pipeline by @MekkCyber in #37861
* docs: Details for ambigious channel dimension assignment by @yaner-here in #37600
* Processor chat template: pass custom kwargs by @pcuenca in #37852
* Add Intel Gaudi doc by @regisss in #37855
* 🌐 [i18n-KO] Translated `electra.md` to Korean by @Kim-Ju-won in #36763
* Update modeling_llama4.py by @monk1337 in #37841
* Skip is_flaky tests in the CI by @Rocketknight1 in #37723
* Allow override inputs to export recipe by @guangy10 in #37508
* enable internvl UTs on XPU by @yao-matrix in #37779
* Llama Guard updates by @pcuenca in #37872
* update Clean_up_tokenization_spaces typos. by @zhanluxianshen in #37865
* fix error for _register_pytree_node in torch2.1.0 and fix bf16 assertion in xpu and npu by @jiaqiw09 in #37839
* make sure lr is not a tensor by @winglian in #37881
* Fix qwen2-vl-docs. by @zhanluxianshen in #37879
* uniformize kwargs for VisionTextDualEncoder by @tibor-reiss in #34563
* Fix: reassign in qwen3 moe model by @linkedlist771 in #37848
* update comment in image_processing_base.py to reference image_process… by @arjunaskykok in #37864
* Support FlaxPreTrainedModel to load model checkpoint from local subfolder safetensors by @Melody-coder923 in #37732
* [tests] Test all cache implementations by @gante in #37873
* [tests] reset logs in `torch.compile` test by @gante in #37894
* Fix Qwen3 tp plan with FP8 by @MekkCyber in #37871
* Enhance documentation to explain chat-based few-shot prompting by @MostHumble in #37828
* Support `AOPerModuleConfig` and `include_embedding` by @jerryzh168 in #37802
* fixed gemma3 collection path pointing to llama 2 collection. by @dmgcsilva in #37899
* Fix typos in strings and comments by @co63oc in #37910
* Improve performance of `load_state_dict` by @woct0rdho in #37902
* 🌐 [i18n-KO] Translated `gpu_selection.md` to Korean by @nsbg in #36757
* Add usage example for DINOv2 by @baldassarreFe in #37398
* Aligning modling code for GPT2 to work with vLLM (fallback) by @ariG23498 in #36934
* Break weight tying when quantizing input embedding by @jerryzh168 in #37905
* [docs] logits docstring by @gante in #37929
* [D-FINE] Update names by @NielsRogge in #37957
* More fault tolerant notification service by @ivarflakstad in #37924
* [core] reuse unused reserved cuda memory when loading models by @gante in #37920
* Use T4 single GPU runner with more CPU RAM by @ydshieh in #37961
* [generate] Fix `vocab_size` access for multimodal models by @kurzdev in #37937
* Fix incorrect type annotation in get_auxiliary_logits by @Tanuj-rai in #37955
* [Ready to Merge][HFQuantizer] Squelch pydantic warnings by @kylesayrs in #37726
* Add GraniteMoeHybrid support for 4.0 by @Ssukriti in #37658
* add xpu memory check by @faaany in #37969
* [tests] Smaller model in slow cache tests by @gante in #37922
* [llava] one pixel is missing from padding when length is odd by @cyr0930 in #37819
* add job links to new model failure report by @ydshieh in #37973
* fix docs serving typos. by @zhanluxianshen in #37936
* Small typo lines 47 and 199 perf_infer_gpu_one.md by @nlhmnlhmnlhm in #37938
* Fix typos by @omahs in #37978
* [speech2text] fix init of sinusoidal embeddings by @gante in #37931
* Fix typo by @lkm2835 in #37964
* enable xpu in test_trainer by @yao-matrix in #37774
* fix FSDP + torch.compile bug when saving pretrained model by @Joaquinecc in #37725
* Enable granite speech 3.3 tests by @alex-jw-brooks in #37560
* Fix donut backtracking by @Rocketknight1 in #37788
* Fix Qwen models export with torch 2.7 by @guangy10 in #37985
* [offload] respect `max_memory` argument when factoring in unused reserved memory by @gante in #37982
* make aya vision 5 integration tests pass on xpu by @yao-matrix in #37990
* [chat template] separate jinja logic from tokenizers by @zucchini-nlp in #37602
* remove duplicate code by @kaixuanliu in #37991
* Add a check to import_utils.py to allow for use of faiss_gpu installation by @Fiona-Waters in #37997
* [CSM] tiny fix on generation by @eustlb in #38001
* Fix `pad` image transform for batched inputs by @sebasv in #37544
* Add ALL_ATTENTION_FUNCTIONS compatibility for Pixtral model by @uminaty in #37960
* Enable RUF013 to enforce optional typing by @cyyever in #37266
* Fix `Optional` typing by @qubvel in #38018
* Print commit SHA on slack message for new model notification. by @ydshieh in #38019
* [CI] remove duplicated message on GH comment to run slow tests by @gante in #37970
* [caches] Raise exception on offloaded static caches + multi device by @gante in #37974
* Skip `test_push_to_hub_with_saves_each_epoch` for now by @ydshieh in #38022
* Fix incorrect installation instructions (for issue #37476) by @Zephyr271828 in #37640
* Fix wording in `torchscript.md` by @Madghostek in #38004
* [VLMs] support attention backends by @zucchini-nlp in #37576
* make `test_speculative_decoding_non_distil` device-agnostic by @faaany in #38010
* enable mamba2 integration cases on xpu by @yao-matrix in #38006
* update bnb tests by @jiqing-feng in #38011
* [`AutoDocstring`] Based on inspect parsing of the signature by @ArthurZucker and @yonigozlan in #33771
* fix document masking for chunked attention by @winglian in #37429
* make mistral3 pass on xpu by @yao-matrix in #37882
* enable utils test cases on XPU by @yao-matrix in #38005
* [Temporary] Log some information in some pytest/pluggy internal places by @ydshieh in #37996
* Trigger CircleCI via GitHub Actions when `ready for review` by @ydshieh in #37885
* Disable `Trigger CircleCI via GitHub Actions when `ready for review` by @ydshieh in #38038
* Do not erase a cache_position passed explicitly to generate(), if there is one by @FremyCompany in #37986
* Support for version spec in requires & arbitrary mismatching depths across folders by @LysandreJik in #37854
* Re-Enable `Trigger CircleCI via GitHub Actions when "ready for review" by @ydshieh in #37885)`
* Fix reduce-labels in BEIT Fast Image Processor by @simonreise in #38042
* Fix cache update! by @Cyrilvallez in #38046
* Fix linalg.norm for CovnNextV2 by @qubvel in #38015
* enable generation fsdp/utils cases on XPU by @yao-matrix in #38009
* fix(conversion): Fix size mismatch error during TF->PT model loading by @arjunaskykok in #38014
* [VLM] fix loading issues by @zucchini-nlp in #38051
* Fix OneFormer integration test by @qubvel in #38016
* Add AMD expectation to test_gpt2_sample by @ivarflakstad in #38079
* docs: fix md style by @imba-tjd in #38057
* Fix mt5 test on AMD devices by @ivarflakstad in #38081
* chore(qwen2): display warning log only when sliding window attention … by @edwardzjl in #36316
* fix the inconsist docstring in apply_chat_template by @lenijwp in #38069
* Fix tot update in trainer by @efsotr in #37923
* update seed_worker to set seed based on worker_id and rank by @gathierry in #37980
* uninstall `kernels` from docker images by @ydshieh in #38083
* Refactor image processor phi4 by @yonigozlan in #36976
* update `require_read_token` by @ydshieh in #38093
* add timeout for downloading the `librispeech_asr` dataset by @faaany in #38073
* fix: Propagate `lr_scheduler_kwargs` options to create LR Scheduler when LayerWiseDummyOptimizer is used by @BlackNoodle in #34559
* Disable report callbacks for certain training tests by @ivarflakstad in #38088
* [smolvlm] skip the test by @zucchini-nlp in #38099
* Fix bug in prefill_chunk_size that ignores disable_compile flag by @xmarva in #38067
* Fix `past_key_values` type hint in model output types by @ChengLyu in #37953
* [bug] fix llava processor to calculate unpadding size correctly by @cyr0930 in #37988
* fix `check_bad commit.py` gives wrong results by @ydshieh in #38107
* Fix InternVL interpolate_pos_encoding and add to video_processing_auto by @yonigozlan in #38092
* [CSM] update test for t4 runners by @eustlb in #38110
* Add style bot by @SunMarc in #38102
* Fix description and formatting errors in code docs by @bilibili12433014 in #38074
* enable finegrained_fp8 and granite_speech cases on XPU by @yao-matrix in #38036
* [video processor] fix tests by @zucchini-nlp in #38104
* Fix temporal padding in Qwen2VLImageProcessor when the number of frames is not divisible by temporal_patch_size by @ritwickchaudhry in #38076
* Fix auto batch size finder test by @ivarflakstad in #38125
* Add config validation and style tweaks by @Kirire in #37589
* Update trainer.md by @guspuffygit in #38113
* [docs] add uv installation instructions for source builds by @arjunaskykok in #37968
* Add `manueldeprada` to `run_slow` whitelist by @manueldeprada in #38126
* enable d_fine finetuning properly by @SangbumChoi in #37962
* Fix incorrect attention mask truncate in WhisperFlashAttention2 by @OliBomby in #36477
* [Qwen3] Qwen3 MoE add tp plan for expert mlps by @hgt312 in #38135
* enable csm integration cases on xpu, all passed by @yao-matrix in #38140
* Remove head mask in generative models by @zucchini-nlp in #35786
* Hotfix: Flash Attention 2 support in Pixtral by @uminaty in #38146
* enable trainer test cases on xpu by @yao-matrix in #38138
* disable deepspeed when setting up fake trainer by @winglian in #38101
* Omit creation of positional IDs within ESM if applicable by @simonlevine in #38089
* [FIX] Save speed metrics to logs by @pavelgein in #38136
* enable autoround cases on XPU by @yao-matrix in #38167
* Include output embedding as well with `include_embedding` flag by @jerryzh168 in #37935
* Fix Qwen2.5 Omni `SinusoidsPositionEmbedding` precision by @BakerBunker in #38151
* Add optional RMSNorm support to BitNet quantization (config + layers) by @Codys12 in #38087
* [VLMs] add helpers to get multimodal encodings by @zucchini-nlp in #37743
* Bart: new cache format by @zucchini-nlp in #35314
* clean autoawq cases on xpu by @yao-matrix in #38163
* Disable `Trigger CircleCI by ready for review` by @ydshieh in #38171
* Disable `convert to draft` workflow by @ydshieh in #38177
* remove some commands from `fetch_tests` CircleCI job by @ydshieh in #38176
* Feat: add warnings for unused keys and rules in tensor parallel by @S1ro1 in #37893
* [ESM] Add flash-attention-2 backend for ESM-2 by @pstjohn in #38023
* Add args support for fast image processors by @yonigozlan in #37018
* Fix import torchao.prototype.low_bit_optim since torchao v0.11 by @baptxste in #38174
* fix bug in distributed loss test by @techkang in #38166
* [tests] remove `test_sdpa_equivalence` (redundant) by @gante in #37911
* Add Granite Speech Support by @alex-jw-brooks in #36801
* Add glm4 by @ArthurZucker in #37388
* Add Qwen2.5-Omni by @BakerBunker in #36752
* Add MLCD model by @tanhuajie in #36182
* Add TimesFM Time Series Forecasting Model by @jinan-zhou in #34082
* Add Janus model by @yaswanth19 in #36053
* Add InternVL (2.5 MPO) by @yonigozlan in #35968
* Add Bitnet model by @MekkCyber in #37742
* Samhq model addition by @sushmanthreddy in #35147
* Add D-FINE Model into Transformers by @VladOS95-cyber in #36261
* Add CSM model by @eustlb in #36719
## Significant community contributions
The following contributors have made significant changes to the library over the last release:
* @cyyever
* Use Python 3.9 syntax in examples (#37279)
* Use Python 3.9 syntax in tests (#37343)
* Remove old code for PyTorch, Accelerator and tokenizers (#37234)
* Add weights_only=True to torch.load (#37062)
* Add XPU case to is_torch_bf16_gpu_available (#37132)
* Remove `fsspec` dependency which isn't directly used by transformers (#37318)
* Add Optional to remaining types (#37808)
* Enable RUF013 to enforce optional typing (#37266)
* @yao-matrix
* enable 2 llama UT cases on xpu (#37126)
* enhance require_deterministic_for_xpu (#37437)
* make test_snowman_image_captioning pass on XPU, by sharing same atol w/ ROCM (#37480)
* fix and enhance pipeline_webserver.md (#36992)
* enable 5 cases on XPU (#37507)
* enable several cases on XPU (#37516)
* enable `test_offloaded_cache_implementation` on XPU (#37514)
* enable 3 mpt test cases on XPU (#37546)
* enable 6 rt_detr_v2 cases on xpu (#37548)
* enable 6 gemma2 cases on XPU (#37564)
* enable 6 modeling cases on XPU (#37571)
* fix 2 encoder_decoder issues on XPU (#37572)
* enable mllama cases on xpu (#37644)
* enable 6 granite cases on xpu (#37569)
* enable blip2 and emu3 cases on XPU (#37662)
* enable cpu offloading for Bark on xpu (#37599)
* enable 4 test_trainer cases on XPU (#37645)
* enable internvl UTs on XPU (#37779)
* enable xpu in test_trainer (#37774)
* make aya vision 5 integration tests pass on xpu (#37990)
* enable mamba2 integration cases on xpu (#38006)
* make mistral3 pass on xpu (#37882)
* enable utils test cases on XPU (#38005)
* enable generation fsdp/utils cases on XPU (#38009)
* enable finegrained_fp8 and granite_speech cases on XPU (#38036)
* enable csm integration cases on xpu, all passed (#38140)
* enable trainer test cases on xpu (#38138)
* enable autoround cases on XPU (#38167)
* clean autoawq cases on xpu (#38163)
* @alex-jw-brooks
* Expose blip2qformer (#37254)
* Add Granite Speech Support (#36801)
* Fix qwen2audio wanr -> warn (#37559)
* Allow Exclusion of Input IDs from RepetitionPenaltyLogitsProcessor (#37625)
* Enable granite speech 3.3 tests (#37560)
* @BakerBunker
* Add Qwen2.5-Omni (#36752)
* Fix inference bugs in Qwen2.5 Omni (#37701)
* Fix `embeds_to_talker` device in Qwen2.5-Omni (#37739)
* Fix Qwen2.5 Omni `SinusoidsPositionEmbedding` precision (#38151)
* @rootonchair
* Add Fast Image Processor for Perceiver (#37176)
* Add Fast Image Processor for Flava (#37135)
* Add Fast Image Processor for LayoutLMv2 (#37203)
* Add Fast Image Processor for LayoutLMv3 (#37201)
* Add Fast Image Processor for Donut (#37081)
* Bridgetower fast image processor (#37373)
* Add Fast Image Processor for PoolFormer (#37182)
* @flukeskywalker
* Fix mask handling for flex attention in llama/gemma2/mistral/qwen2 (#37381)
* @keetrap
* Add Fast LeViT Processor (#37154)
* Add Fast Mobilenet-V2 Processor (#37113)
* Add Fast owlvit Processor (#37164)
* Add Fast Yolos Processor (#37292)
* Add Fast Chinese-CLIP Processor (#37012)
* Add Fast Conditional-DETR Processor (#37071)
* Add Fast Grounding-Dino Processor (#37108)
* Add Fast PVT Processor (#37204)
* @tanhuajie
* Add MLCD model (#36182)
* @jinan-zhou
* Add TimesFM Time Series Forecasting Model (#34082)
* @yaswanth19
* Add Janus model (#36053)
* @saswatmeher
* chore: update model card for SigLIP (#37585)
* chore: update SigLIP2 model card (#37624)
* @cyr0930
* [fix] make legacy bnb code work (#37331)
* [llava] one pixel is missing from padding when length is odd (#37819)
* [bug] fix llava processor to calculate unpadding size correctly (#37988)
* @wenhuach21
* Add AutoRound quantization support (#37393)
* @devxaitist
* 🌐 [i18n-KO] Translated `siglip.md` to Korean (#37145)
* Add Fast Image Processor for vilt (#37304)
* @co63oc
* Fix typos in comments (#37694)
* Fix typos in strings and comments (#37784)
* Fix typos in strings and comments (#37799)
* Fix typos in strings and comments (#37910)
* @guangy10
* Gemma3 is Torch Exportable (#37728)
* Allow override inputs to export recipe (#37508)
* Fix Qwen models export with torch 2.7 (#37985)
* @sushmanthreddy
* Samhq model addition (#35147)
* @VladOS95-cyber
* Add D-FINE Model into Transformers (#36261)
* @Ssukriti
* Add GraniteMoeHybrid support for 4.0 (#37658)
| 2025-05-21T00:24:22.023993 |
huggingface | transformers | v4.52.2 | Patch release v4.52.2 | 2025-05-21T13:26:35+00:00 | # Patch release v4.52.2
We had to revert #37877 because of a missing flag that was overriding the device map. We re-introduced the changes because they allow native 3D parallel training in Transformers. Sorry everyone for the troubles! 🤗
* Clearer error on import failure (#38257) by @LysandreJik
* Verified tp plan should not be NONE (#38255) by @NouamaneTazi and @ArthurZucker | 2025-05-22T00:23:56.695324 |
huggingface | transformers | v4.52.3 | Patch release v4.52.3 | 2025-05-22T14:30:01+00:00 | # Patch release v4.52.3
We had to protect the imports again, a series of bad events.
Here are the two prs for the patch:
- Fix tp error when torch distributed is already initialized (#38294) by @SunMarc
- Protect ParallelInterface (#38262) by @ArthurZucker and @LysandreJik | 2025-05-23T00:24:19.831858 |
huggingface | transformers | v4.52.4 | Patch release: v4.52.4 | 2025-05-30T09:15:08+00:00 | The following commits are included in that patch release:
- [qwen-vl] Look for vocab size in text config (#38372)
- Fix convert to original state dict for VLMs (#38385)
- [video utils] group and reorder by number of frames (#38374)
- [paligemma] fix processor with suffix (#38365)
- Protect get_default_device for torch<2.3 (#38376)
- [OPT] Fix attention scaling (#38290) | 2025-05-31T00:23:38.325543 |
huggingface | transformers | v4.52.4-ColQwen2-preview | ColQwen2 (based on v4.52.4) | 2025-06-02T13:03:59+00:00 | A new model is added to transformers: ColQwen2
It is added on top of the v4.52.4 release, and can be installed from the following tag: `v4.52.4-ColQwen2-preview`.
In order to install this version, please install with the following command:
```
pip install git+https://github.com/huggingface/[email protected]
```
If fixes are needed, they will be applied to this release; this installation may therefore be considered as stable and improving.
As the tag implies, this tag is a **_preview_** of the ColQwen2 model. This tag is a tagged version of the `main` branch and does not follow semantic versioning. This model will be included in the next minor release: `v4.53.0`.
## ColQwen2
[ColQwen2](https://doi.org/10.48550/arXiv.2407.01449) is a variant of the [ColPali](https://github.com/huggingface/transformers/blob/c72ba6944171e2e6dd4f4a93d61b24fa52b718f5/docs/source/en/model_doc/colpali) model designed to retrieve documents by analyzing their visual features. Unlike traditional systems that rely heavily on text extraction and OCR, ColQwen2 treats each page as an image. It uses the [Qwen2-VL](https://github.com/huggingface/transformers/blob/c72ba6944171e2e6dd4f4a93d61b24fa52b718f5/docs/source/en/model_doc/qwen2_vl) backbone to capture not only text, but also the layout, tables, charts, and other visual elements to create detailed multi-vector embeddings that can be used for retrieval by computing pairwise late interaction similarity scores. This offers a more comprehensive understanding of documents and enables more efficient and accurate retrieval.
## Usage example
ColQwen2 can be found on the [Huggingface Hub](https://huggingface.co/models?other=colqwen2).
```python
import requests
import torch
from PIL import Image
from transformers import ColQwen2ForRetrieval, ColQwen2Processor
from transformers.utils.import_utils import is_flash_attn_2_available
# Load the model and the processor
model_name = "vidore/colqwen2-v1.0-hf"
model = ColQwen2ForRetrieval.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto", # "cpu", "cuda", or "mps" for Apple Silicon
attn_implementation="flash_attention_2" if is_flash_attn_2_available() else "sdpa",
)
processor = ColQwen2Processor.from_pretrained(model_name)
# The document page screenshots from your corpus
url1 = "https://upload.wikimedia.org/wikipedia/commons/8/89/US-original-Declaration-1776.jpg"
url2 = "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Romeoandjuliet1597.jpg/500px-Romeoandjuliet1597.jpg"
images = [
Image.open(requests.get(url1, stream=True).raw),
Image.open(requests.get(url2, stream=True).raw),
]
# The queries you want to retrieve documents for
queries = [
"When was the United States Declaration of Independence proclaimed?",
"Who printed the edition of Romeo and Juliet?",
]
# Process the inputs
inputs_images = processor(images=images).to(model.device)
inputs_text = processor(text=queries).to(model.device)
# Forward pass
with torch.no_grad():
image_embeddings = model(**inputs_images).embeddings
query_embeddings = model(**inputs_text).embeddings
# Score the queries against the images
scores = processor.score_retrieval(query_embeddings, image_embeddings)
print("Retrieval scores (query x image):")
print(scores)
```
If you have issue with loading the images with PIL, you can use the following code to create dummy images:
```python
images = [
Image.new("RGB", (128, 128), color="white"),
Image.new("RGB", (64, 32), color="black"),
]
```
Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
The example below uses [bitsandbytes](../quantization/bitsandbytes.md) to quantize the weights to int4.
```python
import requests
import torch
from PIL import Image
from transformers import BitsAndBytesConfig, ColQwen2ForRetrieval, ColQwen2Processor
model_name = "vidore/colqwen2-v1.0-hf"
# 4-bit quantization configuration
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
)
model = ColQwen2ForRetrieval.from_pretrained(
model_name,
quantization_config=bnb_config,
device_map="cuda",
).eval()
processor = ColQwen2Processor.from_pretrained(model_name)
url1 = "https://upload.wikimedia.org/wikipedia/commons/8/89/US-original-Declaration-1776.jpg"
url2 = "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Romeoandjuliet1597.jpg/500px-Romeoandjuliet1597.jpg"
images = [
Image.open(requests.get(url1, stream=True).raw),
Image.open(requests.get(url2, stream=True).raw),
]
queries = [
"When was the United States Declaration of Independence proclaimed?",
"Who printed the edition of Romeo and Juliet?",
]
# Process the inputs
inputs_images = processor(images=images, return_tensors="pt").to(model.device)
inputs_text = processor(text=queries, return_tensors="pt").to(model.device)
# Forward pass
with torch.no_grad():
image_embeddings = model(**inputs_images).embeddings
query_embeddings = model(**inputs_text).embeddings
# Score the queries against the images
scores = processor.score_retrieval(query_embeddings, image_embeddings)
print("Retrieval scores (query x image):")
print(scores)
``` | 2025-06-03T00:24:46.050844 |
README.md exists but content is empty.
- Downloads last month
- 160