Instructions to use CharlesLi/llama_3_code_cot_simplest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CharlesLi/llama_3_code_cot_simplest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CharlesLi/llama_3_code_cot_simplest")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CharlesLi/llama_3_code_cot_simplest") model = AutoModelForCausalLM.from_pretrained("CharlesLi/llama_3_code_cot_simplest", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CharlesLi/llama_3_code_cot_simplest with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CharlesLi/llama_3_code_cot_simplest" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CharlesLi/llama_3_code_cot_simplest", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CharlesLi/llama_3_code_cot_simplest
- SGLang
How to use CharlesLi/llama_3_code_cot_simplest 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 "CharlesLi/llama_3_code_cot_simplest" \ --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": "CharlesLi/llama_3_code_cot_simplest", "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 "CharlesLi/llama_3_code_cot_simplest" \ --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": "CharlesLi/llama_3_code_cot_simplest", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CharlesLi/llama_3_code_cot_simplest with Docker Model Runner:
docker model run hf.co/CharlesLi/llama_3_code_cot_simplest
Download generation_config.json from CharlesLi/llama_3_code_cot_simplest: direct link, hf CLI and curl.
- Browser
- Download file 184 Bytes
-
https://huggingface.co/CharlesLi/llama_3_code_cot_simplest/resolve/3280d04cdc4d47607b212ffd7ded3acf496cf708/generation_config.json
- Command line
-
hf download hf://CharlesLi/llama_3_code_cot_simplest@3280d04cdc4d47607b212ffd7ded3acf496cf708/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/CharlesLi/llama_3_code_cot_simplest/resolve/3280d04cdc4d47607b212ffd7ded3acf496cf708/generation_config.json
184 Bytes
| { | |
| "bos_token_id": 128000, | |
| "do_sample": true, | |
| "eos_token_id": [ | |
| 128001, | |
| 128008, | |
| 128009 | |
| ], | |
| "temperature": 0.6, | |
| "top_p": 0.9, | |
| "transformers_version": "4.47.1" | |
| } | |