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telcom
/
dee-tulu-train

Text Generation
Transformers
Safetensors
English
causal-lm
lora
tulu
Model card Files Files and versions
xet
Community

Instructions to use telcom/dee-tulu-train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use telcom/dee-tulu-train with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="telcom/dee-tulu-train")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("telcom/dee-tulu-train", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use telcom/dee-tulu-train with vLLM:

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

    How to use telcom/dee-tulu-train 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 "telcom/dee-tulu-train" \
        --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": "telcom/dee-tulu-train",
    		"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 "telcom/dee-tulu-train" \
            --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": "telcom/dee-tulu-train",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use telcom/dee-tulu-train with Docker Model Runner:

    docker model run hf.co/telcom/dee-tulu-train
dee-tulu-train
290 MB
Ctrl+K
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  • 3 contributors
History: 13 commits
Javad Taghia
added check.py for cuda
bdd77bf 6 months ago
  • archive
    cput ok for compare 6 months ago
  • evaluation
    cput ok for compare 6 months ago
  • .env.example
    416 Bytes
    updated with evaluation 6 months ago
  • .gitattributes
    1.65 kB
    update 6 months ago
  • .gitignore
    317 Bytes
    cput ok for compare 6 months ago
  • README.md
    8.35 kB
    cput ok for compare 6 months ago
  • check.py
    45 Bytes
    added check.py for cuda 6 months ago
  • environment.yml
    1.14 kB
    update 6 months ago
  • requirements.txt
    345 Bytes
    update 6 months ago
  • train_tulu.py
    12.6 kB
    cpu run 6 months ago