Text Generation
Transformers
PyTorch
TensorBoard
gpt2
Generated from Trainer
custom_code
text-generation-inference
Instructions to use Huamin/santacoder-finetuned-the-stack-bash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Huamin/santacoder-finetuned-the-stack-bash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Huamin/santacoder-finetuned-the-stack-bash", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Huamin/santacoder-finetuned-the-stack-bash", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("Huamin/santacoder-finetuned-the-stack-bash", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Huamin/santacoder-finetuned-the-stack-bash with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Huamin/santacoder-finetuned-the-stack-bash" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Huamin/santacoder-finetuned-the-stack-bash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Huamin/santacoder-finetuned-the-stack-bash
- SGLang
How to use Huamin/santacoder-finetuned-the-stack-bash 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 "Huamin/santacoder-finetuned-the-stack-bash" \ --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": "Huamin/santacoder-finetuned-the-stack-bash", "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 "Huamin/santacoder-finetuned-the-stack-bash" \ --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": "Huamin/santacoder-finetuned-the-stack-bash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Huamin/santacoder-finetuned-the-stack-bash with Docker Model Runner:
docker model run hf.co/Huamin/santacoder-finetuned-the-stack-bash
- Xet hash:
- 31a22494d3c02d4e6bb1ca991ac6cd02c900f51f3a629a54e2a1aac43d5c00b5
- Size of remote file:
- 4.6 GB
- SHA256:
- c59cf9998e804d423dac5321261440f477b18f22bde9c1ed9251824e5d6c52c9
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