Text Generation
Transformers
PyTorch
TensorBoard
gpt2
Generated from Trainer
custom_code
text-generation-inference
Instructions to use mrm8488/santacoder-finetuned-the-stack-dockerfiles with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/santacoder-finetuned-the-stack-dockerfiles with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mrm8488/santacoder-finetuned-the-stack-dockerfiles", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mrm8488/santacoder-finetuned-the-stack-dockerfiles", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("mrm8488/santacoder-finetuned-the-stack-dockerfiles", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use mrm8488/santacoder-finetuned-the-stack-dockerfiles 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-dockerfiles" # 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-dockerfiles", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mrm8488/santacoder-finetuned-the-stack-dockerfiles
- SGLang
How to use mrm8488/santacoder-finetuned-the-stack-dockerfiles 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-dockerfiles" \ --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-dockerfiles", "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-dockerfiles" \ --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-dockerfiles", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mrm8488/santacoder-finetuned-the-stack-dockerfiles with Docker Model Runner:
docker model run hf.co/mrm8488/santacoder-finetuned-the-stack-dockerfiles
| timestamp,experiment_id,project_name,duration,emissions,energy_consumed,country_name,country_iso_code,region,on_cloud,cloud_provider,cloud_region | |
| 2023-02-04T09:50:11,36f83c41-96fa-43b8-be17-d367f3e6e9f2,codecarbon,60034.34314966202,3.7166218624815475,6.231175801814985,Spain,ESP,murcia,N,, | |