GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
hfl
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
hfl
Model Tree
Qwen/Qwen2.5-VL-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
This is an UNOFFICIAL GPTQ-Int4 quantized version of the Qwen2.5-VL model using gptqmodel library.
The model is compatible with the latest transformers library (which can run non-quantized Qwen2.5-VL models).
| Model | Size (Disk) | ChartQA (test) | OCRBench |
|---|---|---|---|
| Qwen2.5-VL-3B-Instruct | 7.1 GB | 83.48 | 791 |
| Qwen2.5-VL-3B-Instruct-AWQ | 3.2 GB | 82.52 | 786 |
| Qwen2.5-VL-3B-Instruct-GPTQ-Int4 | 3.2 GB | 82.56 | 784 |
| Qwen2.5-VL-3B-Instruct-GPTQ-Int3 | 2.9 GB | 76.68 | 742 |
| Qwen2.5-VL-7B-Instruct | 16.0 GB | 83.2 | 846 |
| Qwen2.5-VL-7B-Instruct-AWQ | 6.5 GB | 79.68 | 837 |
| Qwen2.5-VL-7B-Instruct-GPTQ-Int4 | 6.5 GB | 81.48 | 845 |
| Qwen2.5-VL-7B-Instruct-GPTQ-Int3 | 5.8 GB | 78.56 | 823 |
gptqmodel instead of autogptq library, as autogptq is no longer maintained.Install the required libraries:
text
pip install git+https://github.com/huggingface/transformers accelerate qwen-vl-utilspip install git+https://github.com/huggingface/optimum.gitpip install gptqmodel Optionally, you may need to install:
text
pip install tokenicer device_smi logbarSample code:
python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessorfrom qwen_vl_utils import process_vision_info model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "hfl/Qwen2.5-VL-3B-Instruct-GPTQ-Int4", attn_implementation="flash_attention_2", device_map="auto")processor = AutoProcessor.from_pretrained("hfl/Qwen2.5-VL-3B-Instruct-GPTQ-Int4") messages = [{ "role": "user", "content": [ {"type": "image", "image": "https://raw.githubusercontent.com/ymcui/Chinese-LLaMA-Alpaca-3/refs/heads/main/pics/banner.png"}, {"type": "text", "text": "请你描述一下这张图片。"}, ],}] text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)image_inputs, video_inputs = process_vision_info(messages)inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt",).to("cuda") generated_ids = model.generate(**inputs, max_new_tokens=512)generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)print(output_text[0])Response:
这张图片展示了一个中文和英文的标志，内容为“中文LLaMA & Alpaca大模型”和“Chinese LLaMA & Alpaca Large Language Models”。标志左侧有两个卡通形象，一个是红色围巾的羊驼，另一个是白色毛发的羊驼，背景是一个绿色的草地和一座红色屋顶的建筑。标志右侧有一个数字3，旁边有一些电路图案。整体设计简洁明了，使用了明亮的颜色和可爱的卡通形象来吸引注意力。
pip install git+https://github.com/huggingface/transformers accelerate qwen-vl-utilspip install git+https://github.com/huggingface/optimum.gitpip install gptqmodel from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessorfrom qwen_vl_utils import process_vision_info model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "hfl/Qwen2.5-VL-3B-Instruct-GPTQ-Int4", attn_implementation="flash_attention_2", device_map="auto")processor = AutoProcessor.from_pretrained("hfl/Qwen2.5-VL-3B-Instruct-GPTQ-Int4") messages = [{ "role": "user", "content": [ {"type": "image", "image": "https://raw.githubusercontent.com/ymcui/Chinese-LLaMA-Alpaca-3/refs/heads/main/pics/banner.png"}, {"type": "text", "text": "请你描述一下这张图片。"}, ],}] text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)image_inputs, video_inputs = process_vision_info(messages)inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt",).to("cuda") generated_ids = model.generate(**inputs, max_new_tokens=512)generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)print(output_text[0])