Image-Text-to-Text
Transformers
Safetensors
PyTorch
English
Korean
qwen3_5
qwen3.8
multimodal
vision-language-model
quantization
post-training-quantization
warpquant
llm
conversational
Instructions to use HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4") model = AutoModelForMultimodalLM.from_pretrained("HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4
- SGLang
How to use HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4 with Docker Model Runner:
docker model run hf.co/HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4
| { | |
| "source": "/workspace/warpquant-qwen38-27b-20260815/source-bf16", | |
| "base": "/workspace/warpquant-qwen38-27b-20260815/base-3.50", | |
| "selection": "/workspace/warpquant-qwen38-27b-20260815/results/output-fisher-selection.json", | |
| "selection_sha256": "2a249ad8626308fef8f7cdbf85c49fac3438e8eb9b35a5639f62c3fae6eea3bb", | |
| "result_index": 1, | |
| "domain": "original", | |
| "base_selected_bpw": 3.5, | |
| "selected_values": 24326963200, | |
| "selected_average_bpw": 3.5499997474407325, | |
| "recovery_bits": 16, | |
| "group_size": 128, | |
| "recovery_scales": 0, | |
| "index_bits": 436224, | |
| "tensor_count": 368, | |
| "column_count": 13632, | |
| "corrected_values": 75994112, | |
| "auxiliary_bits": null, | |
| "embedding_bits": 4, | |
| "embedding_values": 1271398400, | |
| "embedding_scales": 9932800, | |
| "visual_bits": null, | |
| "visual_values": 0, | |
| "visual_scales": 0, | |
| "output_head_bits": 4, | |
| "output_head_values": 1271398400, | |
| "output_head_scales": 9932800, | |
| "remaining_bf16_values": 911667952, | |
| "total_values": 27781427952, | |
| "analytical_source_bpw": 4.011184646107352, | |
| "shard_sha256": { | |
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