Instructions to use yuanzhoulvpi/chinese_bloom_7b_chat_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yuanzhoulvpi/chinese_bloom_7b_chat_v3 with Transformers:

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

pipe = pipeline("text-generation", model="yuanzhoulvpi/chinese_bloom_7b_chat_v3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yuanzhoulvpi/chinese_bloom_7b_chat_v3")
model = AutoModelForCausalLM.from_pretrained("yuanzhoulvpi/chinese_bloom_7b_chat_v3", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yuanzhoulvpi/chinese_bloom_7b_chat_v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yuanzhoulvpi/chinese_bloom_7b_chat_v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yuanzhoulvpi/chinese_bloom_7b_chat_v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yuanzhoulvpi/chinese_bloom_7b_chat_v3

SGLang

How to use yuanzhoulvpi/chinese_bloom_7b_chat_v3 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 "yuanzhoulvpi/chinese_bloom_7b_chat_v3" \
    --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": "yuanzhoulvpi/chinese_bloom_7b_chat_v3",
		"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 "yuanzhoulvpi/chinese_bloom_7b_chat_v3" \
        --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": "yuanzhoulvpi/chinese_bloom_7b_chat_v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yuanzhoulvpi/chinese_bloom_7b_chat_v3 with Docker Model Runner:
```
docker model run hf.co/yuanzhoulvpi/chinese_bloom_7b_chat_v3
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

体验链接

🔗http://101.68.79.42:7861/

🚀更新

模型链接	训练的数据量	模型版本	备注
https://huggingface.co/yuanzhoulvpi/chinese_bloom_7b_chat	15w中文指令数据	v1
https://huggingface.co/yuanzhoulvpi/chinese_bloom_7b_chat_v2	150w条中文指令数据	v2	目前已经测试过效果，相较于v1，效果有所提升
https://huggingface.co/yuanzhoulvpi/chinese_bloom_7b_chat_v3	420w条中文指令数据	v3	目前效果还没测试，欢迎大家测试

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Safetensors

Model size

8B params

Tensor type

BF16

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Quantizations

2 models

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chinese_bloom_7b_chat_v3

体验链接

🚀更新

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