HuggingFaceH4
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vsft-llava-1.5-7b-hf-trl

@@ -1,199 +1,120 @@
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-library_name: transformers
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-# Model Card for Model ID
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 ---
+language:
+- en
+pipeline_tag: image-to-text
+inference: false
+arxiv: 2304.08485
+datasets:
+- HuggingFaceH4/llava-instruct-mix-vsft
 ---
+# Model Card
+HuggingFaceH4/vsft-llava-1.5-7b-hf-trl is a Vision Language Model, created by performing VSFT on the [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf) model
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6200d0a443eb0913fa2df7cc/q5GXv6Om4Hf2n6IB3e7DQ.png)
+Below is the model card of Llava model 7b, which is copied from the original Llava model card that you can find [here](https://huggingface.co/liuhaotian/llava-v1.5-13b).
+Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1qsl6cd2c8gGtEW1xV5io7S8NHh-Cp1TV?usp=sharing)
+Or check out our Spaces demo! [![Open in Spaces](https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-md-dark.svg)](https://huggingface.co/spaces/llava-hf/llava-4bit)
+## Model details
+**Model type:**
+LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
+It is an auto-regressive language model, based on the transformer architecture.
+**Model date:**
+The model was trained on April the 11th 2024
+**Example training script**
+https://github.com/huggingface/trl/blob/main/examples/scripts/vsft_llava.py
+## How to use the model
+The model supports multi-image and multi-prompt generation. Meaning that you can pass multiple images in your prompt. Make sure also to follow the correct prompt template (`USER: xxx\nASSISTANT:`) and add the token `<image>` to the location where you want to query images:
+### Using `pipeline`:
+```python
+from transformers import pipeline
+from PIL import Image
+import requests
+model_id = "llava-hf/llava-1.5-7b-hf"
+pipe = pipeline("image-to-text", model=model_id)
+url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+prompt = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nASSISTANT:"
+outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
+print(outputs)
+>>> {"generated_text": "\nUSER: What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nASSISTANT: Lava"}
+```
+### Using pure `transformers`:
+Below is an example script to run generation in `float16` precision on a GPU device:
+```python
+import requests
+from PIL import Image
+import torch
+from transformers import AutoProcessor, LlavaForConditionalGeneration
+model_id = "HuggingFaceH4/vsft-llava-1.5-7b-hf-trl"
+prompt = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat are these?\nASSISTANT:"
+image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
+model = LlavaForConditionalGeneration.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True,
+).to(0)
+processor = AutoProcessor.from_pretrained(model_id)
+raw_image = Image.open(requests.get(image_file, stream=True).raw)
+inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16)
+output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
+print(processor.decode(output[0][2:], skip_special_tokens=True))
+```
+### Model optimization
+#### 4-bit quantization through `bitsandbytes` library
+First make sure to install `bitsandbytes`, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with:
+```diff
+model = LlavaForConditionalGeneration.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True,
++   load_in_4bit=True
+)
+```
+#### Use Flash-Attention 2 to further speed-up generation
+First make sure to install `flash-attn`. Refer to the [original repository of Flash Attention](https://github.com/Dao-AILab/flash-attention) regarding that package installation. Simply change the snippet above with:
+```diff
+model = LlavaForConditionalGeneration.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True,
++   use_flash_attention_2=True
+).to(0)
+```
+## License
+Llama 2 is licensed under the LLAMA 2 Community License,
+Copyright (c) Meta Platforms, Inc. All Rights Reserved.