Instructions to use Pection/llama3-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pection/llama3-finetune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Pection/llama3-finetune")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Pection/llama3-finetune") model = AutoModelForCausalLM.from_pretrained("Pection/llama3-finetune", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Pection/llama3-finetune with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Pection/llama3-finetune" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Pection/llama3-finetune", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Pection/llama3-finetune
- SGLang
How to use Pection/llama3-finetune 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 "Pection/llama3-finetune" \ --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": "Pection/llama3-finetune", "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 "Pection/llama3-finetune" \ --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": "Pection/llama3-finetune", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Pection/llama3-finetune with Docker Model Runner:
docker model run hf.co/Pection/llama3-finetune
Commit History
-
Update config.json
Verified signaturece8485e -
Update config.json
Verified signature040728d -
Update README.md
Verified signaturebf315e2 -
Update config.json
Verified signatureac5be09 -
Update config.json
Verified signature8546a04 -
Update README.md
Verified signatureb0cff90 -
Update README.md
Verified signatureae0b463 -
Update README.md
Verified signature95cdb69 -
Update README.md
Verified signaturef57ef81 -
Update README.md
Verified signaturec882bc2 -
Update README.md
Verified signature6a1452a -
Update README.md
Verified signature54d383b -
Update README.md
Verified signatured33e1d8 -
Add: json
Naphat3f7a57f -
Del: json
Naphatf259c6e -
Add: config.json
Naphatb26d2a0 -
Delete config.json
Naphat892ee32 -
Update: git attributes
Naphatd2fe434 -
Update: config.json
Naphat2902b77 -
Update README.md
Verified signaturef3856fc -
Update README.md
Verified signature917ed89 -
Update README.md
Verified signaturee96efe7 -
Update README.md
Verified signature52602de -
Update README.md
Verified signature8242bc6
-
Update README.md
Verified signature4359a3c -
Update: config file
Naphata480b5f -
Configure Git LFS for large files
Naphatd4561ad -
Update: config file
Naphat755aa74 -
Add model files using Git LFS
Naphatec94218 -
README.md
Naphat5db139f -
initial commit
Verified signature034193c