Instructions to use PurCL/src_prober_codellama-13b-last1unfreeze with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PurCL/src_prober_codellama-13b-last1unfreeze with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PurCL/src_prober_codellama-13b-last1unfreeze")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("PurCL/src_prober_codellama-13b-last1unfreeze", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use PurCL/src_prober_codellama-13b-last1unfreeze with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PurCL/src_prober_codellama-13b-last1unfreeze" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PurCL/src_prober_codellama-13b-last1unfreeze", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/PurCL/src_prober_codellama-13b-last1unfreeze
- SGLang
How to use PurCL/src_prober_codellama-13b-last1unfreeze 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 "PurCL/src_prober_codellama-13b-last1unfreeze" \ --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": "PurCL/src_prober_codellama-13b-last1unfreeze", "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 "PurCL/src_prober_codellama-13b-last1unfreeze" \ --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": "PurCL/src_prober_codellama-13b-last1unfreeze", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use PurCL/src_prober_codellama-13b-last1unfreeze with Docker Model Runner:
docker model run hf.co/PurCL/src_prober_codellama-13b-last1unfreeze
Download training_args.bin from PurCL/src_prober_codellama-13b-last1unfreeze: direct link, hf CLI and curl.
- Browser
- Download file 5.05 kB
-
https://huggingface.co/PurCL/src_prober_codellama-13b-last1unfreeze/resolve/main/training_args.bin
- Command line
-
hf download hf://PurCL/src_prober_codellama-13b-last1unfreeze/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/PurCL/src_prober_codellama-13b-last1unfreeze/resolve/main/training_args.bin
5.05 kB
- Xet hash:
- 41839eea582181cee90d2cd63143a976fbaa8072eb74eee99525dd717174e658
- Size of remote file:
- 5.05 kB
- SHA256:
- fd6a31d2da7b4686068d511137b843e541147626d5878a410233f3ef91ae5dc6
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