Instructions to use MagicHub/Chinese-llama2-CLAM-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MagicHub/Chinese-llama2-CLAM-7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MagicHub/Chinese-llama2-CLAM-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MagicHub/Chinese-llama2-CLAM-7b")
model = AutoModelForCausalLM.from_pretrained("MagicHub/Chinese-llama2-CLAM-7b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MagicHub/Chinese-llama2-CLAM-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MagicHub/Chinese-llama2-CLAM-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MagicHub/Chinese-llama2-CLAM-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MagicHub/Chinese-llama2-CLAM-7b

SGLang

How to use MagicHub/Chinese-llama2-CLAM-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 "MagicHub/Chinese-llama2-CLAM-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": "MagicHub/Chinese-llama2-CLAM-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 "MagicHub/Chinese-llama2-CLAM-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": "MagicHub/Chinese-llama2-CLAM-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MagicHub/Chinese-llama2-CLAM-7b with Docker Model Runner:
```
docker model run hf.co/MagicHub/Chinese-llama2-CLAM-7b
```

MagicHub commited on Aug 2, 2023

Commit

9cd8d64

1 Parent(s): d24498e

Update README.md

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@@ -5,6 +5,7 @@ language:
 tags:
 - text-generation-inference
 ---
 For details see: https://github.com/magichub-opensource/CLAM-Conversational-Language-AI-from-MagicData
 ### 模型推理
@@ -14,7 +15,7 @@ For details see: https://github.com/magichub-opensource/CLAM-Conversational-Lang
 #### Web Demo
-我们使用 [text-generation-webui](https://github.com/oobabooga/text-generation-webui/tree/main) 开源项目搭建的 demo 进行推理，得到文档中的对比样例。该demo支持>在网页端切换模型、调整多种常见参数等。
 实验环境：py310-torch1.13.1-cuda11.6-cudnn8

 tags:
 - text-generation-inference
 ---
 For details see: https://github.com/magichub-opensource/CLAM-Conversational-Language-AI-from-MagicData
 ### 模型推理
 #### Web Demo
+我们使用 [text-generation-webui](https://github.com/oobabooga/text-generation-webui/tree/main) 开源项目搭建的 demo 进行推理，得到文档中的对比样例。该demo支持在网页端切换模型、调整多种常见参数等。
 实验环境：py310-torch1.13.1-cuda11.6-cudnn8