Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing

  • Log In
  • Sign Up

mrm8488
/
santacoder-finetuned-the-stack-clojure

Text Generation
Transformers
PyTorch
TensorBoard
code
gpt2
Generated from Trainer
clojure
codegen
custom_code
text-generation-inference
Model card Files Files and versions
xet
Metrics Training metrics Community
1

Instructions to use mrm8488/santacoder-finetuned-the-stack-clojure with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use mrm8488/santacoder-finetuned-the-stack-clojure with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="mrm8488/santacoder-finetuned-the-stack-clojure", trust_remote_code=True)
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("mrm8488/santacoder-finetuned-the-stack-clojure", trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained("mrm8488/santacoder-finetuned-the-stack-clojure", trust_remote_code=True)
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use mrm8488/santacoder-finetuned-the-stack-clojure with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "mrm8488/santacoder-finetuned-the-stack-clojure"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "mrm8488/santacoder-finetuned-the-stack-clojure",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/mrm8488/santacoder-finetuned-the-stack-clojure
  • SGLang

    How to use mrm8488/santacoder-finetuned-the-stack-clojure 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 "mrm8488/santacoder-finetuned-the-stack-clojure" \
        --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": "mrm8488/santacoder-finetuned-the-stack-clojure",
    		"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 "mrm8488/santacoder-finetuned-the-stack-clojure" \
            --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": "mrm8488/santacoder-finetuned-the-stack-clojure",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use mrm8488/santacoder-finetuned-the-stack-clojure with Docker Model Runner:

    docker model run hf.co/mrm8488/santacoder-finetuned-the-stack-clojure
santacoder-finetuned-the-stack-clojure / runs
309 kB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 14 commits
mrm8488's picture
mrm8488
End of training
47455f1 about 3 years ago
  • Feb11_19-55-17_2cb2cefa7ce2
    Training in progress, step 500 about 3 years ago
  • Feb12_11-20-39_2cb2cefa7ce2
    Training in progress, step 500 about 3 years ago
  • Feb12_11-38-02_2cb2cefa7ce2
    Training in progress, step 1000 about 3 years ago
  • Feb12_18-35-52_2cb2cefa7ce2
    Training in progress, step 3500 about 3 years ago
  • Feb13_03-29-35_2cb2cefa7ce2
    Training in progress, step 3500 about 3 years ago
  • Feb13_10-29-06_2cb2cefa7ce2
    Training in progress, step 3500 about 3 years ago
  • Feb13_11-00-48_2cb2cefa7ce2
    Training in progress, step 6500 about 3 years ago
  • Feb13_18-03-33_2cb2cefa7ce2
    Training in progress, step 6500 about 3 years ago
  • Feb13_19-50-28_2cb2cefa7ce2
    Training in progress, step 6500 about 3 years ago
  • Feb14_09-26-32_2cb2cefa7ce2
    Training in progress, step 6500 about 3 years ago
  • Feb14_16-37-39_2cb2cefa7ce2
    Training in progress, step 6500 about 3 years ago
  • Feb15_11-58-21_2cb2cefa7ce2
    End of training about 3 years ago