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Chia-Mu-Lab
/
qwen25-7b-ot-rep-gemini-3.5-flash

Text Generation
Transformers
Safetensors
English
distillation
reasoning
reasoning-trace-exposure
qwen2.5
Model card Files Files and versions
xet
Community

Instructions to use Chia-Mu-Lab/qwen25-7b-ot-rep-gemini-3.5-flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Chia-Mu-Lab/qwen25-7b-ot-rep-gemini-3.5-flash with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Chia-Mu-Lab/qwen25-7b-ot-rep-gemini-3.5-flash")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Chia-Mu-Lab/qwen25-7b-ot-rep-gemini-3.5-flash", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use Chia-Mu-Lab/qwen25-7b-ot-rep-gemini-3.5-flash with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Chia-Mu-Lab/qwen25-7b-ot-rep-gemini-3.5-flash"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Chia-Mu-Lab/qwen25-7b-ot-rep-gemini-3.5-flash",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Chia-Mu-Lab/qwen25-7b-ot-rep-gemini-3.5-flash
  • SGLang

    How to use Chia-Mu-Lab/qwen25-7b-ot-rep-gemini-3.5-flash 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 "Chia-Mu-Lab/qwen25-7b-ot-rep-gemini-3.5-flash" \
        --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": "Chia-Mu-Lab/qwen25-7b-ot-rep-gemini-3.5-flash",
    		"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 "Chia-Mu-Lab/qwen25-7b-ot-rep-gemini-3.5-flash" \
            --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": "Chia-Mu-Lab/qwen25-7b-ot-rep-gemini-3.5-flash",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Chia-Mu-Lab/qwen25-7b-ot-rep-gemini-3.5-flash with Docker Model Runner:

    docker model run hf.co/Chia-Mu-Lab/qwen25-7b-ot-rep-gemini-3.5-flash
qwen25-7b-ot-rep-gemini-3.5-flash
76.2 GB
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  • 1 contributor
History: 7 commits
0x-YuAN's picture
0x-YuAN
model card
5da8cc9 verified 25 days ago
  • checkpoint-1000
    epoch 2 (checkpoint-1000) 25 days ago
  • checkpoint-1500
    epoch 3 (checkpoint-1500) 25 days ago
  • checkpoint-2000
    epoch 4 (checkpoint-2000) 25 days ago
  • checkpoint-2500
    epoch 5 (checkpoint-2500) 25 days ago
  • checkpoint-500
    epoch 1 (checkpoint-500) 25 days ago
  • .gitattributes
    1.85 kB
    epoch 5 (checkpoint-2500) 25 days ago
  • README.md
    2.85 kB
    model card 25 days ago