Instructions to use Rajesh222/finetuned_llama3_python_function_generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rajesh222/finetuned_llama3_python_function_generation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Rajesh222/finetuned_llama3_python_function_generation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Rajesh222/finetuned_llama3_python_function_generation") model = AutoModelForCausalLM.from_pretrained("Rajesh222/finetuned_llama3_python_function_generation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Rajesh222/finetuned_llama3_python_function_generation with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Rajesh222/finetuned_llama3_python_function_generation" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Rajesh222/finetuned_llama3_python_function_generation", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Rajesh222/finetuned_llama3_python_function_generation
- SGLang
How to use Rajesh222/finetuned_llama3_python_function_generation 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 "Rajesh222/finetuned_llama3_python_function_generation" \ --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": "Rajesh222/finetuned_llama3_python_function_generation", "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 "Rajesh222/finetuned_llama3_python_function_generation" \ --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": "Rajesh222/finetuned_llama3_python_function_generation", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Desktop
- Docker Model Runner
How to use Rajesh222/finetuned_llama3_python_function_generation with Docker Model Runner:
docker model run hf.co/Rajesh222/finetuned_llama3_python_function_generation
Download adapter_model.safetensors from Rajesh222/finetuned_llama3_python_function_generation: direct link, hf CLI and curl.
- Browser
- Download file 168 MB
-
https://huggingface.co/Rajesh222/finetuned_llama3_python_function_generation/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://Rajesh222/finetuned_llama3_python_function_generation/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/Rajesh222/finetuned_llama3_python_function_generation/resolve/main/adapter_model.safetensors
168 MB
- Xet hash:
- 49e8fef0c252df74a3ff1c6b38a00dd5dfa283a98114b5bb6d1ac23f9f95061d
- Size of remote file:
- 168 MB
- SHA256:
- 0260860113d1863f5a73b924aca1e2e41e242a459bab2fc51c28570ba7186789
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.