Instructions to use LinkSoul/Chinese-Llama-2-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LinkSoul/Chinese-Llama-2-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LinkSoul/Chinese-Llama-2-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LinkSoul/Chinese-Llama-2-7b") model = AutoModelForCausalLM.from_pretrained("LinkSoul/Chinese-Llama-2-7b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use LinkSoul/Chinese-Llama-2-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LinkSoul/Chinese-Llama-2-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LinkSoul/Chinese-Llama-2-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LinkSoul/Chinese-Llama-2-7b
- SGLang
How to use LinkSoul/Chinese-Llama-2-7b 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 "LinkSoul/Chinese-Llama-2-7b" \ --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": "LinkSoul/Chinese-Llama-2-7b", "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 "LinkSoul/Chinese-Llama-2-7b" \ --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": "LinkSoul/Chinese-Llama-2-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LinkSoul/Chinese-Llama-2-7b with Docker Model Runner:
docker model run hf.co/LinkSoul/Chinese-Llama-2-7b
Pretraining error
#18 opened over 2 years ago
by
saivineetha
Adding Evaluation Results
#17 opened over 2 years ago
by
leaderboard-pr-bot
Adding `safetensors` variant of this model
#16 opened over 2 years ago
by
chen6850
OS:Macos, 在Chat的时候出错,RuntimeError: MPS does not support cumsum op with int64 input,Chat窗口不显示内容,请大佬们支招。
#15 opened almost 3 years ago
by
Bossiniliu
7B的模型,怎么权重大小是27GB?
#14 opened almost 3 years ago
by
zhaoying9105
为什么最大输入长度是2048?
2
#13 opened almost 3 years ago
by
yuyijiong
请问可否有lora单独模型可供单独使用?
#12 opened almost 3 years ago
by
sdasd112132
oobabooga-windows 出错 ! 选的是llmam.cpp
#11 opened almost 3 years ago
by
aldennX
能不能放出一个fp16的版本
1
#10 opened almost 3 years ago
by
louisY
试了一下效果不错,有没有更大参数版本放出来
2
#9 opened almost 3 years ago
by
Weiguo
请问7B的中文性能和ChatGLM2-6B相比如何呢?可以出一个微调教程吗?
1
#6 opened almost 3 years ago
by
SQJKL