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mrm8488
/
santacoder-finetuned-the-stack-bash-shell

Text Generation
Transformers
PyTorch
TensorBoard
code
gpt2
Generated from Trainer
bash
shell
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-bash-shell with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

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

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

    How to use mrm8488/santacoder-finetuned-the-stack-bash-shell 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-bash-shell"
    # 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-bash-shell",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/mrm8488/santacoder-finetuned-the-stack-bash-shell
  • SGLang

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

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

    docker model run hf.co/mrm8488/santacoder-finetuned-the-stack-bash-shell
santacoder-finetuned-the-stack-bash-shell
4.6 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 17 commits
mrm8488's picture
mrm8488
Update config.json
fa6115f over 3 years ago
  • runs
    End of training over 3 years ago
  • .gitattributes
    1.48 kB
    initial commit over 3 years ago
  • .gitignore
    13 Bytes
    Training in progress, step 500 over 3 years ago
  • README.md
    4.51 kB
    Update README.md over 3 years ago
  • added_tokens.json
    126 Bytes
    Upload tokenizer over 3 years ago
  • config.json
    994 Bytes
    Update config.json over 3 years ago
  • emissions.csv
    293 Bytes
    End of training over 3 years ago
  • merges.txt
    455 kB
    Upload tokenizer over 3 years ago
  • pytorch_model.bin

    Detected Pickle imports (4)

    • "torch.FloatStorage",
    • "torch._utils._rebuild_tensor_v2",
    • "collections.OrderedDict",
    • "torch.ByteStorage"

    What is a pickle import?

    4.6 GB
    xet
    End of training over 3 years ago
  • special_tokens_map.json
    234 Bytes
    Upload tokenizer over 3 years ago
  • tokenizer.json
    2.08 MB
    Upload tokenizer over 3 years ago
  • tokenizer_config.json
    421 Bytes
    Upload tokenizer over 3 years ago
  • training_args.bin

    Detected Pickle imports (6)

    • "transformers.training_args.OptimizerNames",
    • "transformers.trainer_utils.SchedulerType",
    • "transformers.trainer_utils.HubStrategy",
    • "torch.device",
    • "transformers.training_args.TrainingArguments",
    • "transformers.trainer_utils.IntervalStrategy"

    How to fix it?

    3.52 kB
    xet
    Training in progress, step 9500 over 3 years ago
  • vocab.json
    790 kB
    Upload tokenizer over 3 years ago