Instructions to use opencsg/csg-wukong-code-1B-cpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use opencsg/csg-wukong-code-1B-cpt with Transformers:

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

pipe = pipeline("text-generation", model="opencsg/csg-wukong-code-1B-cpt")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("opencsg/csg-wukong-code-1B-cpt")
model = AutoModelForCausalLM.from_pretrained("opencsg/csg-wukong-code-1B-cpt")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use opencsg/csg-wukong-code-1B-cpt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "opencsg/csg-wukong-code-1B-cpt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "opencsg/csg-wukong-code-1B-cpt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/opencsg/csg-wukong-code-1B-cpt

SGLang

How to use opencsg/csg-wukong-code-1B-cpt 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 "opencsg/csg-wukong-code-1B-cpt" \
    --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": "opencsg/csg-wukong-code-1B-cpt",
		"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 "opencsg/csg-wukong-code-1B-cpt" \
        --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": "opencsg/csg-wukong-code-1B-cpt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use opencsg/csg-wukong-code-1B-cpt with Docker Model Runner:
```
docker model run hf.co/opencsg/csg-wukong-code-1B-cpt
```

csg-wukong-code-1B-cpt [中文] [English]

[OpenCSG Community] [github] [wechat] [Twitter]

OpenCSG stands for Converged resources, Software refinement, and Generative LM. The 'C' represents Converged resources, indicating the integration and full utilization of hybrid resources. The 'S' stands for Software refinement, signifying software that is refined by large models. The 'G' represents Generative LM, which denotes widespread, inclusive, and democratized generative large models.

The vision of OpenCSG is to empower every industry, every company, and every individual to own their models. We adhere to the principles of openness and open source, making the large model software stack of OpenCSG available to the community. We welcome everyone to use, send feedback, and contribute collaboratively.

Model Description

csg-wukong-1B-code-cpt is a 1 billion-parameter small language model(SLM) continue pretrained based on csg-wukong-1B.
we will introduce more information about csg-wukong-code-1B-cpt.

Training

Hardware

GPUs: 16 H800
Training time: 5days

Software

Orchestration: Deepspeed
Neural networks: PyTorch
BP16 if applicable: apex

OpenCSG介绍

OpenCSG

[OpenCSG 社区] [github] [微信] [推特]

OpenCSG中 Open是开源开放；C 代表 Converged resources，整合和充分利用的混合异构资源优势，算力降本增效；S 代表 Software refined，重新定义软件的交付方式，通过大模型驱动软件开发，人力降本增效；G 代表 Generative LM，大众化、普惠化和民主化的可商用的开源生成式大模型。

OpenCSG的愿景是让每个行业、每个公司、每个人都拥有自己的模型。我们坚持开源开放的原则，将OpenCSG的大模型软件栈开源到社区，欢迎使用、反馈和参与共建，欢迎关注。

模型介绍

csg-wukong-1B-code-cpt 是一个1B参数量的小语言模型，该模型是在csg-wukong-1B,二次预训练二成

我们将在后面介绍更多关于这个模型的信息。

训练

硬件资源

GPU数量： 16 H800
训练时间： 5天

软件使用

微调训练框架： Deepspeed
深度学习框架： PyTorch
BP16： apex

Downloads last month: 5

Safetensors

Model size

1B params

Tensor type

BF16

Collection including opencsg/csg-wukong-code-1B-cpt

csg-wukong

Collection

SLM pretrained from scratch • 7 items • Updated Aug 29, 2024 • 1