Text Generation
Transformers
PyTorch
TensorBoard
code
gpt2
Generated from Trainer
codegen
assembly
custom_code
text-generation-inference
Instructions to use muhtasham/santacoder-finetuned-the-stack-cobol with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use muhtasham/santacoder-finetuned-the-stack-cobol with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="muhtasham/santacoder-finetuned-the-stack-cobol", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("muhtasham/santacoder-finetuned-the-stack-cobol", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("muhtasham/santacoder-finetuned-the-stack-cobol", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use muhtasham/santacoder-finetuned-the-stack-cobol with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "muhtasham/santacoder-finetuned-the-stack-cobol" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "muhtasham/santacoder-finetuned-the-stack-cobol", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/muhtasham/santacoder-finetuned-the-stack-cobol
- SGLang
How to use muhtasham/santacoder-finetuned-the-stack-cobol 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 "muhtasham/santacoder-finetuned-the-stack-cobol" \ --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": "muhtasham/santacoder-finetuned-the-stack-cobol", "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 "muhtasham/santacoder-finetuned-the-stack-cobol" \ --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": "muhtasham/santacoder-finetuned-the-stack-cobol", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use muhtasham/santacoder-finetuned-the-stack-cobol with Docker Model Runner:
docker model run hf.co/muhtasham/santacoder-finetuned-the-stack-cobol
Commit History
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Librarian Bot: Add base_model information to model (#1)
- Librarian Bot: Add base_model information to model (6a6aaa9dee5ced2d0fcba4ebf6a3675273b5bb37) Co-authored-by: Librarian Bot (Bot) <librarian-bot@users.noreply.huggingface.co>da16538
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