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xDAN-AI
/
xDAN-L1-Chat-v0.1

Text Generation
Transformers
PyTorch
mistral
text-generation-inference
Model card Files Files and versions
xet
Community
1

Instructions to use xDAN-AI/xDAN-L1-Chat-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use xDAN-AI/xDAN-L1-Chat-v0.1 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="xDAN-AI/xDAN-L1-Chat-v0.1")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("xDAN-AI/xDAN-L1-Chat-v0.1")
    model = AutoModelForCausalLM.from_pretrained("xDAN-AI/xDAN-L1-Chat-v0.1", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use xDAN-AI/xDAN-L1-Chat-v0.1 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "xDAN-AI/xDAN-L1-Chat-v0.1"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "xDAN-AI/xDAN-L1-Chat-v0.1",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/xDAN-AI/xDAN-L1-Chat-v0.1
  • SGLang

    How to use xDAN-AI/xDAN-L1-Chat-v0.1 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 "xDAN-AI/xDAN-L1-Chat-v0.1" \
        --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": "xDAN-AI/xDAN-L1-Chat-v0.1",
    		"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 "xDAN-AI/xDAN-L1-Chat-v0.1" \
            --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": "xDAN-AI/xDAN-L1-Chat-v0.1",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use xDAN-AI/xDAN-L1-Chat-v0.1 with Docker Model Runner:

    docker model run hf.co/xDAN-AI/xDAN-L1-Chat-v0.1

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  • .gitattributes
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  • README.md
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  • added_tokens.json
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    xDAN-L1-Think-En-11B v0.1 almost 3 years ago
  • config.json
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  • generation_config.json
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    xDAN-L1-Think-En-11B v0.1 almost 3 years ago
  • pytorch_model-00001-of-00002.bin
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    xDAN-L1-Think-En-11B v0.1 almost 3 years ago
  • pytorch_model-00002-of-00002.bin
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    xDAN-L1-Think-En-11B v0.1 almost 3 years ago
  • pytorch_model.bin.index.json
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  • rng_state.pth
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    xDAN-L1-Think-En-11B v0.1 almost 3 years ago
  • scheduler.pt
    1.06 kB
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    xDAN-L1-Think-En-11B v0.1 almost 3 years ago
  • tokenizer.model
    493 kB
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    xDAN-L1-Think-En-11B v0.1 almost 3 years ago
  • tokenizer_config.json
    1.21 kB
    xDAN-L1-Think-En-11B v0.1 almost 3 years ago
  • trainer_state.json
    1.22 MB
    xDAN-L1-Think-En-11B v0.1 almost 3 years ago
  • training_args.bin
    4.98 kB
    xet
    xDAN-L1-Think-En-11B v0.1 almost 3 years ago