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razor5050
/
codex-qwen2-5-0-5b-unsloth-codex1m

Text Generation
Transformers
Safetensors
PEFT
English
unsloth
qwen2
qwen2.5
lora
sft
code
reasoning
codex
trl
Model card Files Files and versions
xet
Community
1

Instructions to use razor5050/codex-qwen2-5-0-5b-unsloth-codex1m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use razor5050/codex-qwen2-5-0-5b-unsloth-codex1m with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="razor5050/codex-qwen2-5-0-5b-unsloth-codex1m")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("razor5050/codex-qwen2-5-0-5b-unsloth-codex1m", device_map="auto")
  • PEFT

    How to use razor5050/codex-qwen2-5-0-5b-unsloth-codex1m with PEFT:

    Task type is invalid.
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use razor5050/codex-qwen2-5-0-5b-unsloth-codex1m with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "razor5050/codex-qwen2-5-0-5b-unsloth-codex1m"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "razor5050/codex-qwen2-5-0-5b-unsloth-codex1m",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/razor5050/codex-qwen2-5-0-5b-unsloth-codex1m
  • SGLang

    How to use razor5050/codex-qwen2-5-0-5b-unsloth-codex1m 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 "razor5050/codex-qwen2-5-0-5b-unsloth-codex1m" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "razor5050/codex-qwen2-5-0-5b-unsloth-codex1m",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    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 "razor5050/codex-qwen2-5-0-5b-unsloth-codex1m" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "razor5050/codex-qwen2-5-0-5b-unsloth-codex1m",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Unsloth Desktop
  • Docker Model Runner

    How to use razor5050/codex-qwen2-5-0-5b-unsloth-codex1m with Docker Model Runner:

    docker model run hf.co/razor5050/codex-qwen2-5-0-5b-unsloth-codex1m
codex-qwen2-5-0-5b-unsloth-codex1m
1.15 GB
Ctrl+K
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  • 1 contributor
History: 35 commits
razor5050's picture
razor5050
Mark under8000 complete
5f5f46b verified 4 months ago
  • checkpoints_under8000
    Upload under8000 checkpoint step 74653 4 months ago
  • final_lora_adapter_under8000
    Upload final under8000 LoRA adapter 4 months ago
  • metrics_under8000
    Mark under8000 complete 4 months ago
  • reports_under8000
    Upload under8000 training and inference report 4 months ago
  • .gitattributes
    2.41 kB
    Upload final under8000 LoRA adapter 4 months ago
  • README.md
    2.98 kB
    Add initial model card 4 months ago