---
license: apache-2.0
datasets:
- chenjoya/Live-CC-5M
- chenjoya/Live-WhisperX-526K
- lmms-lab/LLaVA-Video-178K
language:
- en
base_model:
- Qwen/Qwen2-VL-7B
tags:
- qwen_vl
- video
- real-time
- multimodal
- LLM
---
# LiveCC-7B-Instruct GGUF Models
## Model Generation Details
This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`e291450`](https://github.com/ggerganov/llama.cpp/commit/e291450b7602d7a36239e4ceeece37625f838373).
## Ultra-Low-Bit Quantization with IQ-DynamicGate (1-2 bit)
Our latest quantization method introduces **precision-adaptive quantization** for ultra-low-bit models (1-2 bit), with benchmark-proven improvements on **Llama-3-8B**. This approach uses layer-specific strategies to preserve accuracy while maintaining extreme memory efficiency.
### **Benchmark Context**
All tests conducted on **Llama-3-8B-Instruct** using:
- Standard perplexity evaluation pipeline
- 2048-token context window
- Same prompt set across all quantizations
### **Method**
- **Dynamic Precision Allocation**:
- First/Last 25% of layers → IQ4_XS (selected layers)
- Middle 50% → IQ2_XXS/IQ3_S (increase efficiency)
- **Critical Component Protection**:
- Embeddings/output layers use Q5_K
- Reduces error propagation by 38% vs standard 1-2bit
### **Quantization Performance Comparison (Llama-3-8B)**
| Quantization | Standard PPL | DynamicGate PPL | Δ PPL | Std Size | DG Size | Δ Size | Std Speed | DG Speed |
|--------------|--------------|------------------|---------|----------|---------|--------|-----------|----------|
| IQ2_XXS | 11.30 | 9.84 | -12.9% | 2.5G | 2.6G | +0.1G | 234s | 246s |
| IQ2_XS | 11.72 | 11.63 | -0.8% | 2.7G | 2.8G | +0.1G | 242s | 246s |
| IQ2_S | 14.31 | 9.02 | -36.9% | 2.7G | 2.9G | +0.2G | 238s | 244s |
| IQ1_M | 27.46 | 15.41 | -43.9% | 2.2G | 2.5G | +0.3G | 206s | 212s |
| IQ1_S | 53.07 | 32.00 | -39.7% | 2.1G | 2.4G | +0.3G | 184s | 209s |
**Key**:
- PPL = Perplexity (lower is better)
- Δ PPL = Percentage change from standard to DynamicGate
- Speed = Inference time (CPU avx2, 2048 token context)
- Size differences reflect mixed quantization overhead
**Key Improvements:**
- 🔥 **IQ1_M** shows massive 43.9% perplexity reduction (27.46 → 15.41)
- 🚀 **IQ2_S** cuts perplexity by 36.9% while adding only 0.2GB
- ⚡ **IQ1_S** maintains 39.7% better accuracy despite 1-bit quantization
**Tradeoffs:**
- All variants have modest size increases (0.1-0.3GB)
- Inference speeds remain comparable (<5% difference)
### **When to Use These Models**
📌 **Fitting models into GPU VRAM**
✔ **Memory-constrained deployments**
✔ **Cpu and Edge Devices** where 1-2bit errors can be tolerated
✔ **Research** into ultra-low-bit quantization
## **Choosing the Right Model Format**
Selecting the correct model format depends on your **hardware capabilities** and **memory constraints**.
### **BF16 (Brain Float 16) – Use if BF16 acceleration is available**
- A 16-bit floating-point format designed for **faster computation** while retaining good precision.
- Provides **similar dynamic range** as FP32 but with **lower memory usage**.
- Recommended if your hardware supports **BF16 acceleration** (check your device's specs).
- Ideal for **high-performance inference** with **reduced memory footprint** compared to FP32.
📌 **Use BF16 if:**
✔ Your hardware has native **BF16 support** (e.g., newer GPUs, TPUs).
✔ You want **higher precision** while saving memory.
✔ You plan to **requantize** the model into another format.
📌 **Avoid BF16 if:**
❌ Your hardware does **not** support BF16 (it may fall back to FP32 and run slower).
❌ You need compatibility with older devices that lack BF16 optimization.
---
### **F16 (Float 16) – More widely supported than BF16**
- A 16-bit floating-point **high precision** but with less of range of values than BF16.
- Works on most devices with **FP16 acceleration support** (including many GPUs and some CPUs).
- Slightly lower numerical precision than BF16 but generally sufficient for inference.
📌 **Use F16 if:**
✔ Your hardware supports **FP16** but **not BF16**.
✔ You need a **balance between speed, memory usage, and accuracy**.
✔ You are running on a **GPU** or another device optimized for FP16 computations.
📌 **Avoid F16 if:**
❌ Your device lacks **native FP16 support** (it may run slower than expected).
❌ You have memory limitations.
---
### **Quantized Models (Q4_K, Q6_K, Q8, etc.) – For CPU & Low-VRAM Inference**
Quantization reduces model size and memory usage while maintaining as much accuracy as possible.
- **Lower-bit models (Q4_K)** → **Best for minimal memory usage**, may have lower precision.
- **Higher-bit models (Q6_K, Q8_0)** → **Better accuracy**, requires more memory.
📌 **Use Quantized Models if:**
✔ You are running inference on a **CPU** and need an optimized model.
✔ Your device has **low VRAM** and cannot load full-precision models.
✔ You want to reduce **memory footprint** while keeping reasonable accuracy.
📌 **Avoid Quantized Models if:**
❌ You need **maximum accuracy** (full-precision models are better for this).
❌ Your hardware has enough VRAM for higher-precision formats (BF16/F16).
---
### **Very Low-Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0)**
These models are optimized for **extreme memory efficiency**, making them ideal for **low-power devices** or **large-scale deployments** where memory is a critical constraint.
- **IQ3_XS**: Ultra-low-bit quantization (3-bit) with **extreme memory efficiency**.
- **Use case**: Best for **ultra-low-memory devices** where even Q4_K is too large.
- **Trade-off**: Lower accuracy compared to higher-bit quantizations.
- **IQ3_S**: Small block size for **maximum memory efficiency**.
- **Use case**: Best for **low-memory devices** where **IQ3_XS** is too aggressive.
- **IQ3_M**: Medium block size for better accuracy than **IQ3_S**.
- **Use case**: Suitable for **low-memory devices** where **IQ3_S** is too limiting.
- **Q4_K**: 4-bit quantization with **block-wise optimization** for better accuracy.
- **Use case**: Best for **low-memory devices** where **Q6_K** is too large.
- **Q4_0**: Pure 4-bit quantization, optimized for **ARM devices**.
- **Use case**: Best for **ARM-based devices** or **low-memory environments**.
---
### **Summary Table: Model Format Selection**
| Model Format | Precision | Memory Usage | Device Requirements | Best Use Case |
|--------------|------------|---------------|----------------------|---------------|
| **BF16** | Highest | High | BF16-supported GPU/CPUs | High-speed inference with reduced memory |
| **F16** | High | High | FP16-supported devices | GPU inference when BF16 isn't available |
| **Q4_K** | Medium Low | Low | CPU or Low-VRAM devices | Best for memory-constrained environments |
| **Q6_K** | Medium | Moderate | CPU with more memory | Better accuracy while still being quantized |
| **Q8_0** | High | Moderate | CPU or GPU with enough VRAM | Best accuracy among quantized models |
| **IQ3_XS** | Very Low | Very Low | Ultra-low-memory devices | Extreme memory efficiency and low accuracy |
| **Q4_0** | Low | Low | ARM or low-memory devices | llama.cpp can optimize for ARM devices |
---
## **Included Files & Details**
### `LiveCC-7B-Instruct-bf16.gguf`
- Model weights preserved in **BF16**.
- Use this if you want to **requantize** the model into a different format.
- Best if your device supports **BF16 acceleration**.
### `LiveCC-7B-Instruct-f16.gguf`
- Model weights stored in **F16**.
- Use if your device supports **FP16**, especially if BF16 is not available.
### `LiveCC-7B-Instruct-bf16-q8_0.gguf`
- **Output & embeddings** remain in **BF16**.
- All other layers quantized to **Q8_0**.
- Use if your device supports **BF16** and you want a quantized version.
### `LiveCC-7B-Instruct-f16-q8_0.gguf`
- **Output & embeddings** remain in **F16**.
- All other layers quantized to **Q8_0**.
### `LiveCC-7B-Instruct-q4_k.gguf`
- **Output & embeddings** quantized to **Q8_0**.
- All other layers quantized to **Q4_K**.
- Good for **CPU inference** with limited memory.
### `LiveCC-7B-Instruct-q4_k_s.gguf`
- Smallest **Q4_K** variant, using less memory at the cost of accuracy.
- Best for **very low-memory setups**.
### `LiveCC-7B-Instruct-q6_k.gguf`
- **Output & embeddings** quantized to **Q8_0**.
- All other layers quantized to **Q6_K** .
### `LiveCC-7B-Instruct-q8_0.gguf`
- Fully **Q8** quantized model for better accuracy.
- Requires **more memory** but offers higher precision.
### `LiveCC-7B-Instruct-iq3_xs.gguf`
- **IQ3_XS** quantization, optimized for **extreme memory efficiency**.
- Best for **ultra-low-memory devices**.
### `LiveCC-7B-Instruct-iq3_m.gguf`
- **IQ3_M** quantization, offering a **medium block size** for better accuracy.
- Suitable for **low-memory devices**.
### `LiveCC-7B-Instruct-q4_0.gguf`
- Pure **Q4_0** quantization, optimized for **ARM devices**.
- Best for **low-memory environments**.
- Prefer IQ4_NL for better accuracy.
# 🚀 If you find these models useful
❤ **Please click "Like" if you find this useful!**
Help me test my **AI-Powered Network Monitor Assistant** with **quantum-ready security checks**:
👉 [Quantum Network Monitor](https://readyforquantum.com/dashboard)
💬 **How to test**:
1. Click the **chat icon** (bottom right on any page)
2. Choose an **AI assistant type**:
- `TurboLLM` (GPT-4-mini)
- `FreeLLM` (Open-source)
- `TestLLM` (Experimental CPU-only)
### **What I’m Testing**
I’m pushing the limits of **small open-source models for AI network monitoring**, specifically:
- **Function calling** against live network services
- **How small can a model go** while still handling:
- Automated **Nmap scans**
- **Quantum-readiness checks**
- **Metasploit integration**
🟡 **TestLLM** – Current experimental model (llama.cpp on 6 CPU threads):
- ✅ **Zero-configuration setup**
- ⏳ 30s load time (slow inference but **no API costs**)
- 🔧 **Help wanted!** If you’re into **edge-device AI**, let’s collaborate!
### **Other Assistants**
🟢 **TurboLLM** – Uses **gpt-4-mini** for:
- **Real-time network diagnostics**
- **Automated penetration testing** (Nmap/Metasploit)
- 🔑 Get more tokens by [downloading our Quantum Network Monitor Agent](https://readyforquantum.com/download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme)
🔵 **HugLLM** – Open-source models (≈8B params):
- **2x more tokens** than TurboLLM
- **AI-powered log analysis**
- 🌐 Runs on Hugging Face Inference API
### 💡 **Example AI Commands to Test**:
1. `"Give me info on my websites SSL certificate"`
2. `"Check if my server is using quantum safe encyption for communication"`
3. `"Run a quick Nmap vulnerability test"`
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code from. This is a very flexible and powerful feature. Use with caution!
### Final word
I fund the servers to create the models files, run the Quantum Network Monitor Service and Pay for Inference from Novita and OpenAI all from my own pocket. All of the code for creating the models and the work I have done with Quantum Network Monitor is [open source](https://github.com/Mungert69). Feel free to use what you find useful. Please support my work and consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) .
This will help me pay for the services and increase the token limits for everyone.
Thank you :)
# LiveCC-7B-Instruct
## Introduction
We introduce LiveCC, the first video LLM capable of real-time commentary, trained with a novel video-ASR streaming method, SOTA on both streaming and offline benchmarks.
- Project Page: https://showlab.github.io/livecc
> [!Important]
> This is the SFT model. The base model is at [LiveCC-7B-Base](https://huggingface.co/chenjoya/LiveCC-7B-Base).
## Training with Streaming Frame-Words Paradigm
## Quickstart
### Gradio Demo
Please refer to https://github.com/showlab/livecc:
### Hands-on
Like qwen-vl-utils, we offer a toolkit to help you handle various types of visual input more conveniently, **especially on video streaming inputs**. You can install it using the following command:
```bash
pip install qwen-vl-utils livecc-utils liger_kernel
```
Here we show a code snippet to show you how to do **real-time video commentary** with `transformers` and the above utils:
```python
import functools, torch, os, tqdm
from liger_kernel.transformers import apply_liger_kernel_to_qwen2_vl
apply_liger_kernel_to_qwen2_vl() # important. our model is trained with this. keep consistency
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, LogitsProcessor, logging
from livecc_utils import prepare_multiturn_multimodal_inputs_for_generation, get_smart_resized_clip, get_smart_resized_video_reader
from qwen_vl_utils import process_vision_info
class LiveCCDemoInfer:
fps = 2
initial_fps_frames = 6
streaming_fps_frames = 2
initial_time_interval = initial_fps_frames / fps
streaming_time_interval = streaming_fps_frames / fps
frame_time_interval = 1 / fps
def __init__(self, model_path: str = None, device_id: int = 0):
self.model = Qwen2VLForConditionalGeneration.from_pretrained(
model_path, torch_dtype="auto",
device_map=f'cuda:{device_id}',
attn_implementation='flash_attention_2'
)
self.processor = AutoProcessor.from_pretrained(model_path, use_fast=False)
self.model.prepare_inputs_for_generation = functools.partial(prepare_multiturn_multimodal_inputs_for_generation, self.model)
message = {
"role": "user",
"content": [
{"type": "text", "text": 'livecc'},
]
}
texts = self.processor.apply_chat_template([message], tokenize=False)
self.system_prompt_offset = texts.index('<|im_start|>user')
self._cached_video_readers_with_hw = {}
def live_cc(
self,
query: str,
state: dict,
max_pixels: int = 384 * 28 * 28,
default_query: str = 'Please describe the video.',
do_sample: bool = True,
repetition_penalty: float = 1.05,
**kwargs,
):
"""
state: dict, (maybe) with keys:
video_path: str, video path
video_timestamp: float, current video timestamp
last_timestamp: float, last processed video timestamp
last_video_pts_index: int, last processed video frame index
video_pts: np.ndarray, video pts
last_history: list, last processed history
past_key_values: llm past_key_values
past_ids: past generated ids
"""
# 1. preparation: video_reader, and last processing info
video_timestamp, last_timestamp = state.get('video_timestamp', 0), state.get('last_timestamp', -1 / self.fps)
video_path = state['video_path']
if video_path not in self._cached_video_readers_with_hw:
self._cached_video_readers_with_hw[video_path] = get_smart_resized_video_reader(video_path, max_pixels)
video_reader = self._cached_video_readers_with_hw[video_path][0]
video_reader.get_frame_timestamp(0)
state['video_pts'] = torch.from_numpy(video_reader._frame_pts[:, 1])
state['last_video_pts_index'] = -1
video_pts = state['video_pts']
if last_timestamp + self.frame_time_interval > video_pts[-1]:
state['video_end'] = True
return
video_reader, resized_height, resized_width = self._cached_video_readers_with_hw[video_path]
last_video_pts_index = state['last_video_pts_index']
# 2. which frames will be processed
initialized = last_timestamp >= 0
if not initialized:
video_timestamp = max(video_timestamp, self.initial_time_interval)
if video_timestamp <= last_timestamp + self.frame_time_interval:
return
timestamps = torch.arange(last_timestamp + self.frame_time_interval, video_timestamp, self.frame_time_interval) # add compensation
# 3. fetch frames in required timestamps
clip, clip_timestamps, clip_idxs = get_smart_resized_clip(video_reader, resized_height, resized_width, timestamps, video_pts, video_pts_index_from=last_video_pts_index+1)
state['last_video_pts_index'] = clip_idxs[-1]
state['last_timestamp'] = clip_timestamps[-1]
# 4. organize to interleave frames
interleave_clips, interleave_timestamps = [], []
if not initialized:
interleave_clips.append(clip[:self.initial_fps_frames])
interleave_timestamps.append(clip_timestamps[:self.initial_fps_frames])
clip = clip[self.initial_fps_frames:]
clip_timestamps = clip_timestamps[self.initial_fps_frames:]
if len(clip) > 0:
interleave_clips.extend(list(clip.split(self.streaming_fps_frames)))
interleave_timestamps.extend(list(clip_timestamps.split(self.streaming_fps_frames)))
# 5. make conversation and send to model
for clip, timestamps in zip(interleave_clips, interleave_timestamps):
start_timestamp, stop_timestamp = timestamps[0].item(), timestamps[-1].item() + self.frame_time_interval
message = {
"role": "user",
"content": [
{"type": "text", "text": f'Time={start_timestamp:.1f}-{stop_timestamp:.1f}s'},
{"type": "video", "video": clip}
]
}
if not query and not state.get('query', None):
query = default_query
print(f'No query provided, use default_query={default_query}')
if query and state.get('query', None) != query:
message['content'].append({"type": "text", "text": query})
state['query'] = query
texts = self.processor.apply_chat_template([message], tokenize=False, add_generation_prompt=True, return_tensors='pt')
past_ids = state.get('past_ids', None)
if past_ids is not None:
texts = '<|im_end|>\n' + texts[self.system_prompt_offset:]
inputs = self.processor(
text=texts,
images=None,
videos=[clip],
return_tensors="pt",
return_attention_mask=False
)
inputs.to('cuda')
if past_ids is not None:
inputs['input_ids'] = torch.cat([past_ids, inputs.input_ids], dim=1)
outputs = self.model.generate(
**inputs, past_key_values=state.get('past_key_values', None),
return_dict_in_generate=True, do_sample=do_sample,
repetition_penalty=repetition_penalty,
)
state['past_key_values'] = outputs.past_key_values
state['past_ids'] = outputs.sequences[:, :-1]
yield (start_timestamp, stop_timestamp), self.processor.decode(outputs.sequences[0, inputs.input_ids.size(1):], skip_special_tokens=True), state
model_path = 'chenjoya/LiveCC-7B-Instruct'
# download a test video at: https://github.com/showlab/livecc/blob/main/demo/sources/howto_fix_laptop_mute_1080p.mp4
video_path = "demo/sources/howto_fix_laptop_mute_1080p.mp4"
query = "Please describe the video."
infer = LiveCCDemoInfer(model_path=model_path)
state = {'video_path': video_path}
commentaries = []
t = 0
for t in range(31):
state['video_timestamp'] = t
for (start_t, stop_t), response, state in infer.live_cc(
query=query, state=state,
max_pixels = 384 * 28 * 28, repetition_penalty=1.05,
streaming_eos_base_threshold=0.0, streaming_eos_threshold_step=0
):
print(f'{start_t}s-{stop_t}s: {response}')
commentaries.append([start_t, stop_t, response])
if state.get('video_end', False):
break
t += 1
```
Here we show a code snippet to show you how to do **common video (multi-turn) qa** with `transformers` and the above utils:
```python
import functools, torch
from liger_kernel.transformers import apply_liger_kernel_to_qwen2_vl
apply_liger_kernel_to_qwen2_vl() # important. our model is trained with this. keep consistency
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, LogitsProcessor, logging
from livecc_utils import prepare_multiturn_multimodal_inputs_for_generation, get_smart_resized_clip, get_smart_resized_video_reader
from qwen_vl_utils import process_vision_info
class LiveCCDemoInfer:
fps = 2
initial_fps_frames = 6
streaming_fps_frames = 2
initial_time_interval = initial_fps_frames / fps
streaming_time_interval = streaming_fps_frames / fps
frame_time_interval = 1 / fps
def __init__(self, model_path: str = None, device: str = 'cuda'):
self.model = Qwen2VLForConditionalGeneration.from_pretrained(
model_path, torch_dtype="auto",
device_map=device,
attn_implementation='flash_attention_2'
)
self.processor = AutoProcessor.from_pretrained(model_path, use_fast=False)
self.streaming_eos_token_id = self.processor.tokenizer(' ...').input_ids[-1]
self.model.prepare_inputs_for_generation = functools.partial(prepare_multiturn_multimodal_inputs_for_generation, self.model)
message = {
"role": "user",
"content": [
{"type": "text", "text": 'livecc'},
]
}
texts = self.processor.apply_chat_template([message], tokenize=False)
self.system_prompt_offset = texts.index('<|im_start|>user')
def video_qa(
self,
message: str,
state: dict,
do_sample: bool = True,
repetition_penalty: float = 1.05,
**kwargs,
):
"""
state: dict, (maybe) with keys:
video_path: str, video path
video_timestamp: float, current video timestamp
last_timestamp: float, last processed video timestamp
last_video_pts_index: int, last processed video frame index
video_pts: np.ndarray, video pts
last_history: list, last processed history
past_key_values: llm past_key_values
past_ids: past generated ids
"""
video_path = state.get('video_path', None)
conversation = []
past_ids = state.get('past_ids', None)
content = [{"type": "text", "text": message}]
if past_ids is None and video_path: # only use once
content.insert(0, {"type": "video", "video": video_path})
conversation.append({"role": "user", "content": content})
image_inputs, video_inputs = process_vision_info(conversation)
texts = self.processor.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True, return_tensors='pt')
if past_ids is not None:
texts = '<|im_end|>\n' + texts[self.system_prompt_offset:]
inputs = self.processor(
text=texts,
images=image_inputs,
videos=video_inputs,
return_tensors="pt",
return_attention_mask=False
)
inputs.to(self.model.device)
if past_ids is not None:
inputs['input_ids'] = torch.cat([past_ids, inputs.input_ids], dim=1)
outputs = self.model.generate(
**inputs, past_key_values=state.get('past_key_values', None),
return_dict_in_generate=True, do_sample=do_sample,
repetition_penalty=repetition_penalty,
max_new_tokens=512,
)
state['past_key_values'] = outputs.past_key_values
state['past_ids'] = outputs.sequences[:, :-1]
response = self.processor.decode(outputs.sequences[0, inputs.input_ids.size(1):], skip_special_tokens=True)
return response, state
model_path = 'chenjoya/LiveCC-7B-Instruct'
# download a test video at: https://github.com/showlab/livecc/blob/main/demo/sources/howto_fix_laptop_mute_1080p.mp4
video_path = "demo/sources/howto_fix_laptop_mute_1080p.mp4"
infer = LiveCCDemoInfer(model_path=model_path)
state = {'video_path': video_path}
# first round
query1 = 'What is the video?'
response1, state = infer.video_qa(message=query1, state=state)
print(f'Q1: {query1}\nA1: {response1}')
# second round
query2 = 'How do you know that?'
response2, state = infer.video_qa(message=query2, state=state)
print(f'Q2: {query2}\nA2: {response2}')
```
## Performance
## Limitations
- This model is finetuned on LiveCC-7B-Base, which is starting from Qwen2-VL-7B-Base, so it may have limitations mentioned in https://huggingface.co/Qwen/Qwen2-VL-7B.
- When performing real-time video commentary, it may appear collapse --- e.g., repeat pattern. If you encounter this situation, try to adjust repetition_penalty, streaming_eos_base_threshold, and streaming_eos_threshold_step.
- This model only has a context window of 32768. Using more visual tokens per frame (e.g. 768 * 28 * 28) will have better performance, but will shorten the working duration.
These limitations serve as ongoing directions for model optimization and improvement, and we are committed to continually enhancing the model's performance and scope of application.
## Citation
If you find our work helpful, feel free to give us a cite.
```
@article{livecc,
author = {Joya Chen and Ziyun Zeng and Yiqi Lin and Wei Li and Zejun Ma and Mike Zheng Shou},
title = {LiveCC: Learning Video LLM with Streaming Speech Transcription at Scale},
journal = {arXiv preprint arXiv:2504.16030}
year = {2025},
}
```