Instructions to use charent/Phi2-Chinese-0.2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use charent/Phi2-Chinese-0.2B with Transformers:

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

pipe = pipeline("text-generation", model="charent/Phi2-Chinese-0.2B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("charent/Phi2-Chinese-0.2B")
model = AutoModelForCausalLM.from_pretrained("charent/Phi2-Chinese-0.2B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use charent/Phi2-Chinese-0.2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "charent/Phi2-Chinese-0.2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "charent/Phi2-Chinese-0.2B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/charent/Phi2-Chinese-0.2B

SGLang

How to use charent/Phi2-Chinese-0.2B 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 "charent/Phi2-Chinese-0.2B" \
    --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": "charent/Phi2-Chinese-0.2B",
		"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 "charent/Phi2-Chinese-0.2B" \
        --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": "charent/Phi2-Chinese-0.2B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use charent/Phi2-Chinese-0.2B with Docker Model Runner:
```
docker model run hf.co/charent/Phi2-Chinese-0.2B
```

模型和 phi2 的关系是什么？

by xianf - opened Dec 28, 2023

Discussion

xianf

Dec 28, 2023

很棒的想法，把 phi2 实现到中文上。但是 phi2 是使用一定的 topic 和 token 从 GPT 生成了一定量级的伪数据来训练的。这个模型和 phi2 的关系好像并不大？还是说联系是，选择的预训练数据都是知识型的数据，而不是 common crawl 这种通用数据？

charent

Owner Dec 29, 2023

模型结构是 phi2 ，但参数不一样，是小模型。预训练数据、sft数据、dpo数据和微软 phi2不一样，我这都是中文的，其他语种很少。预训练数据就属于知识型数据了，通用数据太大了我这跑不动，github仓库的dev分支有合并wiki数据的预训练处理代码，有需要你可以参考下。

charent changed discussion status to closed Jan 1, 2024

charent changed discussion status to open Jan 8, 2024

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