Text Generation
Transformers
PyTorch
Chinese
English
llama
llama2
llama2-chat
llama2-chat-7B
code
code revise
code summarization
code comment
text-generation-inference
Instructions to use RicardoLee/Llama2-chat-7B-Chinese-withCode3W-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RicardoLee/Llama2-chat-7B-Chinese-withCode3W-LoRA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RicardoLee/Llama2-chat-7B-Chinese-withCode3W-LoRA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RicardoLee/Llama2-chat-7B-Chinese-withCode3W-LoRA") model = AutoModelForCausalLM.from_pretrained("RicardoLee/Llama2-chat-7B-Chinese-withCode3W-LoRA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RicardoLee/Llama2-chat-7B-Chinese-withCode3W-LoRA with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RicardoLee/Llama2-chat-7B-Chinese-withCode3W-LoRA" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RicardoLee/Llama2-chat-7B-Chinese-withCode3W-LoRA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RicardoLee/Llama2-chat-7B-Chinese-withCode3W-LoRA
- SGLang
How to use RicardoLee/Llama2-chat-7B-Chinese-withCode3W-LoRA 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 "RicardoLee/Llama2-chat-7B-Chinese-withCode3W-LoRA" \ --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": "RicardoLee/Llama2-chat-7B-Chinese-withCode3W-LoRA", "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 "RicardoLee/Llama2-chat-7B-Chinese-withCode3W-LoRA" \ --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": "RicardoLee/Llama2-chat-7B-Chinese-withCode3W-LoRA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use RicardoLee/Llama2-chat-7B-Chinese-withCode3W-LoRA with Docker Model Runner:
docker model run hf.co/RicardoLee/Llama2-chat-7B-Chinese-withCode3W-LoRA
Download sft_lora_model/tokenizer.model from RicardoLee/Llama2-chat-7B-Chinese-withCode3W-LoRA: direct link, hf CLI and curl.
- Browser
- Download file 758 kB
-
https://huggingface.co/RicardoLee/Llama2-chat-7B-Chinese-withCode3W-LoRA/resolve/main/sft_lora_model/tokenizer.model
- Command line
-
hf download hf://RicardoLee/Llama2-chat-7B-Chinese-withCode3W-LoRA/sft_lora_model/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/RicardoLee/Llama2-chat-7B-Chinese-withCode3W-LoRA/resolve/main/sft_lora_model/tokenizer.model
758 kB
- Xet hash:
- d345a5fae02c2fa9580ecefc71f99f52a44bfc1e590c3c7aefe43ecaf2e44ec6
- Size of remote file:
- 758 kB
- SHA256:
- 2d967e855b1213a439df6c8ce2791f869c84b4f3b6cfacf22b86440b8192a2f8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.