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
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

m-a-p
/
CT-LLM-SFT-DPO

Text Generation
Transformers
Safetensors
llama
text-generation-inference
Model card Files Files and versions
xet
Community
2

Instructions to use m-a-p/CT-LLM-SFT-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use m-a-p/CT-LLM-SFT-DPO with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="m-a-p/CT-LLM-SFT-DPO")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("m-a-p/CT-LLM-SFT-DPO")
    model = AutoModelForCausalLM.from_pretrained("m-a-p/CT-LLM-SFT-DPO", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use m-a-p/CT-LLM-SFT-DPO with vLLM:

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

    How to use m-a-p/CT-LLM-SFT-DPO 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 "m-a-p/CT-LLM-SFT-DPO" \
        --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": "m-a-p/CT-LLM-SFT-DPO",
    		"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 "m-a-p/CT-LLM-SFT-DPO" \
            --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": "m-a-p/CT-LLM-SFT-DPO",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use m-a-p/CT-LLM-SFT-DPO with Docker Model Runner:

    docker model run hf.co/m-a-p/CT-LLM-SFT-DPO
CT-LLM-SFT-DPO
4.27 GB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 19 commits
dododododo's picture
dododododo
Update README.md
da94d14 verified over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    4.83 kB
    Update README.md over 2 years ago
  • added_tokens.json
    67 Bytes
    Upload added_tokens.json with huggingface_hub over 2 years ago
  • config.json
    742 Bytes
    Upload config.json with huggingface_hub over 2 years ago
  • general.png
    232 kB
    Upload 2 files (#2) over 2 years ago
  • generation_config.json
    111 Bytes
    Upload generation_config.json with huggingface_hub over 2 years ago
  • model.safetensors
    4.27 GB
    xet
    Upload model.safetensors with huggingface_hub over 2 years ago
  • safe.png
    217 kB
    Upload 2 files (#2) over 2 years ago
  • scheduler.pt
    1.06 kB
    xet
    Upload scheduler.pt with huggingface_hub over 2 years ago
  • special_tokens_map.json
    628 Bytes
    Upload special_tokens_map.json with huggingface_hub over 2 years ago
  • tokenization_baichuan.py
    9.64 kB
    Upload tokenization_baichuan.py with huggingface_hub over 2 years ago
  • tokenizer.model
    2 MB
    xet
    Upload tokenizer.model with huggingface_hub over 2 years ago
  • tokenizer_config.json
    1.61 kB
    Upload tokenizer_config.json with huggingface_hub over 2 years ago
  • trainer_state.json
    1.67 MB
    Upload trainer_state.json with huggingface_hub over 2 years ago
  • training_args.bin
    6.01 kB
    xet
    Upload training_args.bin with huggingface_hub over 2 years ago
  • zero_to_fp32.py
    24.3 kB
    Upload zero_to_fp32.py with huggingface_hub over 2 years ago