Instructions to use yuanzhoulvpi/xiaoheizi-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yuanzhoulvpi/xiaoheizi-3b with Transformers:

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

pipe = pipeline("text-generation", model="yuanzhoulvpi/xiaoheizi-3b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yuanzhoulvpi/xiaoheizi-3b")
model = AutoModelForCausalLM.from_pretrained("yuanzhoulvpi/xiaoheizi-3b")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yuanzhoulvpi/xiaoheizi-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yuanzhoulvpi/xiaoheizi-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yuanzhoulvpi/xiaoheizi-3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yuanzhoulvpi/xiaoheizi-3b

SGLang

How to use yuanzhoulvpi/xiaoheizi-3b 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/xiaoheizi-3b" \
    --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/xiaoheizi-3b",
		"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/xiaoheizi-3b" \
        --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/xiaoheizi-3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yuanzhoulvpi/xiaoheizi-3b with Docker Model Runner:
```
docker model run hf.co/yuanzhoulvpi/xiaoheizi-3b
```

介绍

模型名称：xiaoheizi（小黑子），现在国内的大模型起的名字一个比一个厉害，那我们和别人做点不一样的，主打一个随意～
模型参数量：是一个参数量为3B的模型
模型：模型基于dolly_v2_3b模型，而dolly_v2_3b模型本质上就是gpt_neox模型
数据：基于BelleGroup/train_1M_CN数据做的训练
商用：可以商用
GitHub仓库：本项目的训练和推理代码已经全部开源https://github.com/yuanzhoulvpi2017/zero_nlp/blob/main/chinese_dolly_v2_3b

使用方法

推理

step1

git clone git@github.com:yuanzhoulvpi2017/zero_nlp.git

cd chinese_dolly_v2_3b

step2

from transformers import GPTNeoXForCausalLM,AutoTokenizer
from dolly.generate import generate_response,load_model_tokenizer_for_generate
import torch


model, tokenizer = load_model_tokenizer_for_generate(pretrained_model_name_or_path="yuanzhoulvpi/xiaoheizi-3b")


for i in range(10):
    print("*" * 100)
    res = generate_response(instruction="列出三个在二十世纪被认为是最伟大的小说之一的书名，以及它们的作者。", model=model,tokenizer=tokenizer)
    print(res)
    torch.cuda.empty_cache()


# ****************************************************************************************************
# - 《致命招若众》 by 黄伟作为一部历史传说学者的作品，创作于 1898 年。
# - 《讲故事人生》 by 苏珊作为一部历史传说，近年的迈克尔·道尔莎尼的重装作品，创作于1999年。
# - 《金头银头》 by 洛克菲尔德·迪伊纳作为一部小说、小说之旅、花园小说家之一，创作于1958年。

yuanzhoulvpi
/

xiaoheizi-3b

介绍

使用方法

推理

step1

step2

训练

Dataset used to train yuanzhoulvpi/xiaoheizi-3b

Space using yuanzhoulvpi/xiaoheizi-3b 1