Image-Text-to-Text
Safetensors
qwen3_5
quantization
auto-round
gptq
llm-compressor
compressed-tensors
4bit
text-generation
reasoning
distillation
conversational
4-bit precision
Instructions to use Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- vLLM
How to use Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ", "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/Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ
- SGLang
How to use Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ 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 "Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ" \ --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": "Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ", "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 "Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ" \ --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": "Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ", "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 Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ with Docker Model Runner:
docker model run hf.co/Vishva007/Qwen3.8-4B-Distill-W4A16-AutoRound-GPTQ
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> 🎁 **Need GPU compute?** Sign up via [RunPod](https://runpod.io?ref=iabrlp7z) and get **$5–$500 in free credits** when you add your first $10.
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#### PyTorch 2.13
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| Template | CUDA Version | Docker Image | Template ID | Deploy |
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> 🎁 **Need GPU compute?** Sign up via [RunPod](https://runpod.io?ref=iabrlp7z) and get **$5–$500 in free credits** when you add your first $10.
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#### PyTorch 2.14
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| Template | CUDA Version | Docker Image | Template ID | Deploy |
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| :--- | :--- | :--- | :--- | :--- |
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| **PyTorch 2.14 (CUDA 12.6)** | 12.6 | `vishva123/cuda-12.6-pytorch-2.14-runpod` | `d7lxsa4w9m` | [](https://runpod.io/console/deploy?template=d7lxsa4w9m&ref=iabrlp7z) |
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| **PyTorch 2.14 (CUDA 13.0)** | 13.0 | `vishva123/cuda-13.0-pytorch-2.14-runpod` | `yk0y6j6rpg` | [](https://runpod.io/console/deploy?template=yk0y6j6rpg&ref=iabrlp7z) |
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| **PyTorch 2.14 (CUDA 13.2)** | 13.2 | `vishva123/cuda-13.2-pytorch-2.14-runpod` | `gsp4gwx0nw` | [](https://runpod.io/console/deploy?template=gsp4gwx0nw&ref=iabrlp7z) |
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#### PyTorch 2.13
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| Template | CUDA Version | Docker Image | Template ID | Deploy |
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