Instructions to use TurboPascal/Chatterbox-LLaMA-zh-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TurboPascal/Chatterbox-LLaMA-zh-base with Transformers:

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

pipe = pipeline("text-generation", model="TurboPascal/Chatterbox-LLaMA-zh-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TurboPascal/Chatterbox-LLaMA-zh-base")
model = AutoModelForCausalLM.from_pretrained("TurboPascal/Chatterbox-LLaMA-zh-base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TurboPascal/Chatterbox-LLaMA-zh-base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/TurboPascal/Chatterbox-LLaMA-zh-base

SGLang

How to use TurboPascal/Chatterbox-LLaMA-zh-base 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 "TurboPascal/Chatterbox-LLaMA-zh-base" \
    --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": "TurboPascal/Chatterbox-LLaMA-zh-base",
		"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 "TurboPascal/Chatterbox-LLaMA-zh-base" \
        --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": "TurboPascal/Chatterbox-LLaMA-zh-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TurboPascal/Chatterbox-LLaMA-zh-base with Docker Model Runner:
```
docker model run hf.co/TurboPascal/Chatterbox-LLaMA-zh-base
```

TurboPascal commited on May 5, 2023

Commit

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@@ -39,7 +39,7 @@ Notes:
 ### 中文预训练数据
 - 新浪新闻数据（SinaNews），220万条新闻文档数据
-- 人民日报数据（People's Daily Datasets），
 - [维基百科(wiki2019zh)，100万个结构良好的中文词条](https://github.com/brightmart/nlp_chinese_corpus)
 - [新闻语料(news2016zh)，250万篇新闻，含关键词、描述](https://github.com/brightmart/nlp_chinese_corpus)
 - [社区问答json版(webtext2019zh)，410万个高质量社区问答](https://github.com/brightmart/nlp_chinese_corpus)

 ### 中文预训练数据
 - 新浪新闻数据（SinaNews），220万条新闻文档数据
+- 人民日报数据（People's Daily Datasets），148万条人民日报数据。
 - [维基百科(wiki2019zh)，100万个结构良好的中文词条](https://github.com/brightmart/nlp_chinese_corpus)
 - [新闻语料(news2016zh)，250万篇新闻，含关键词、描述](https://github.com/brightmart/nlp_chinese_corpus)
 - [社区问答json版(webtext2019zh)，410万个高质量社区问答](https://github.com/brightmart/nlp_chinese_corpus)