Image-Text-to-Text
Safetensors
English
qwen3_5
qwen3.5
gptq
int4
quantized
coding
agentic
vlm
vision
conversational
4-bit precision
Instructions to use raydelossantos/OmniCoder-9B-GPTQ-Int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps
- vLLM
How to use raydelossantos/OmniCoder-9B-GPTQ-Int4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "raydelossantos/OmniCoder-9B-GPTQ-Int4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "raydelossantos/OmniCoder-9B-GPTQ-Int4", "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/raydelossantos/OmniCoder-9B-GPTQ-Int4
- SGLang
How to use raydelossantos/OmniCoder-9B-GPTQ-Int4 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 "raydelossantos/OmniCoder-9B-GPTQ-Int4" \ --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": "raydelossantos/OmniCoder-9B-GPTQ-Int4", "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 "raydelossantos/OmniCoder-9B-GPTQ-Int4" \ --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": "raydelossantos/OmniCoder-9B-GPTQ-Int4", "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 raydelossantos/OmniCoder-9B-GPTQ-Int4 with Docker Model Runner:
docker model run hf.co/raydelossantos/OmniCoder-9B-GPTQ-Int4
File size: 1,152 Bytes
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"pack_dtype": "int32",
"meta": {
"quantizer": [
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"uri": "https://github.com/modelcloud/gptqmodel",
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"threshold": "0.5%",
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"offload_to_disk": true,
"offload_to_disk_path": "./gptqmodel_offload/jqalracr-ibpdeuuz/",
"pack_impl": "cpu",
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"gc_mode": "interval",
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