Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

raydelossantos
/
OmniCoder-9B-GPTQ-Int8

Image-Text-to-Text
Safetensors
English
qwen3_5
qwen3.5
gptq
int8
quantized
coding
agentic
vlm
vision
conversational
8-bit precision
Model card Files Files and versions
xet
Community

Instructions to use raydelossantos/OmniCoder-9B-GPTQ-Int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Local Apps Settings
  • vLLM

    How to use raydelossantos/OmniCoder-9B-GPTQ-Int8 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-Int8"
    # 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-Int8",
    		"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-Int8
  • SGLang

    How to use raydelossantos/OmniCoder-9B-GPTQ-Int8 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-Int8" \
        --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-Int8",
    		"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-Int8" \
            --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-Int8",
    		"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-Int8 with Docker Model Runner:

    docker model run hf.co/raydelossantos/OmniCoder-9B-GPTQ-Int8
OmniCoder-9B-GPTQ-Int8
Ctrl+K
Ctrl+K
  • 1 contributor
History: 12 commits
raydelossantos's picture
raydelossantos
Fix model card: remove enforce-eager (fixed in vLLM 0.18.0 PR #35256)
e11369e verified 4 months ago
  • .gitattributes
    1.57 kB
    Upload folder using huggingface_hub 4 months ago
  • README.md
    2.72 kB
    Fix model card: remove enforce-eager (fixed in vLLM 0.18.0 PR #35256) 4 months ago
  • chat_template.jinja
    7.76 kB
    Upload folder using huggingface_hub 4 months ago
  • config.json
    4.11 kB
    Fix config.json: use VLM structure (model_type=qwen3_5, Qwen3_5ForConditionalGeneration) with quantization_config 4 months ago
  • generation_config.json
    141 Bytes
    Upload folder using huggingface_hub 4 months ago
  • model.safetensors
    14.1 GB
    xet
    Fix weight keys for VLM: rename model.* to model.language_model.*, add visual encoder from base model 4 months ago
  • preprocessor_config.json
    337 Bytes
    Add preprocessor_config.json for vLLM compatibility (text-only quant) 4 months ago
  • processor_config.json
    1.17 kB
    Add processor_config.json with video processor for vLLM compat 4 months ago
  • quant_log.csv
    4.36 kB
    Upload folder using huggingface_hub 4 months ago
  • quantize_config.json
    1.15 kB
    Upload folder using huggingface_hub 4 months ago
  • tokenizer.json
    20 MB
    xet
    Upload folder using huggingface_hub 4 months ago
  • tokenizer_config.json
    1.1 kB
    Upload folder using huggingface_hub 4 months ago