Text Generation
Transformers
PyTorch
GGUF
Chinese
English
codeshell
wisdomshell
pku-kcl
openbankai
custom_code
Instructions to use WisdomShell/CodeShell-7B-Chat-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WisdomShell/CodeShell-7B-Chat-int4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="WisdomShell/CodeShell-7B-Chat-int4", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("WisdomShell/CodeShell-7B-Chat-int4", trust_remote_code=True, dtype="auto") - llama-cpp-python
How to use WisdomShell/CodeShell-7B-Chat-int4 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="WisdomShell/CodeShell-7B-Chat-int4", filename="codeshell-chat-q4_0.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use WisdomShell/CodeShell-7B-Chat-int4 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf WisdomShell/CodeShell-7B-Chat-int4:Q4_0 # Run inference directly in the terminal: llama-cli -hf WisdomShell/CodeShell-7B-Chat-int4:Q4_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf WisdomShell/CodeShell-7B-Chat-int4:Q4_0 # Run inference directly in the terminal: llama-cli -hf WisdomShell/CodeShell-7B-Chat-int4:Q4_0
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf WisdomShell/CodeShell-7B-Chat-int4:Q4_0 # Run inference directly in the terminal: ./llama-cli -hf WisdomShell/CodeShell-7B-Chat-int4:Q4_0
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf WisdomShell/CodeShell-7B-Chat-int4:Q4_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf WisdomShell/CodeShell-7B-Chat-int4:Q4_0
Use Docker
docker model run hf.co/WisdomShell/CodeShell-7B-Chat-int4:Q4_0
- LM Studio
- Jan
- vLLM
How to use WisdomShell/CodeShell-7B-Chat-int4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "WisdomShell/CodeShell-7B-Chat-int4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "WisdomShell/CodeShell-7B-Chat-int4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/WisdomShell/CodeShell-7B-Chat-int4:Q4_0
- SGLang
How to use WisdomShell/CodeShell-7B-Chat-int4 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 "WisdomShell/CodeShell-7B-Chat-int4" \ --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": "WisdomShell/CodeShell-7B-Chat-int4", "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 "WisdomShell/CodeShell-7B-Chat-int4" \ --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": "WisdomShell/CodeShell-7B-Chat-int4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use WisdomShell/CodeShell-7B-Chat-int4 with Ollama:
ollama run hf.co/WisdomShell/CodeShell-7B-Chat-int4:Q4_0
- Unsloth Studio new
How to use WisdomShell/CodeShell-7B-Chat-int4 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for WisdomShell/CodeShell-7B-Chat-int4 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for WisdomShell/CodeShell-7B-Chat-int4 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for WisdomShell/CodeShell-7B-Chat-int4 to start chatting
- Docker Model Runner
How to use WisdomShell/CodeShell-7B-Chat-int4 with Docker Model Runner:
docker model run hf.co/WisdomShell/CodeShell-7B-Chat-int4:Q4_0
- Lemonade
How to use WisdomShell/CodeShell-7B-Chat-int4 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull WisdomShell/CodeShell-7B-Chat-int4:Q4_0
Run and chat with the model
lemonade run user.CodeShell-7B-Chat-int4-Q4_0
List all available models
lemonade list
Update README.md
Browse files
README.md
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# CodeShell
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CodeShell是[北京大学知识计算实验室](http://se.pku.edu.cn/kcl/)联合四川天府银行AI团队研发的多语言代码大模型基座。CodeShell具有70亿参数,在五千亿Tokens进行了训练,上下文窗口长度为8194。在权威的代码评估Benchmark(HumanEval与MBPP)上,CodeShell取得同等规模最好的性能。与此同时,我们提供了与CodeShell配套的部署方案与IDE插件,请参考代码库[CodeShell](https://github.com/WisdomShell/codeshell)。同时,为了方便中国用户下载,我们在
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CodeShell is a multi-language code LLM developed by the [Knowledge Computing Lab](http://se.pku.edu.cn/kcl/) of Peking University. CodeShell has 7 billion parameters and was trained on 500 billion tokens with a context window length of 8194. On authoritative code evaluation benchmarks (HumanEval and MBPP), CodeShell achieves the best performance of its scale. Meanwhile, we provide deployment solutions and IDE plugins that complement CodeShell. Please refer to the [CodeShell code repository](https://github.com/WisdomShell/codeshell) for more details. This repository is for the Int4 quantized model of CodeShell-7B-Chat.
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# CodeShell
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CodeShell是[北京大学知识计算实验室](http://se.pku.edu.cn/kcl/)联合四川天府银行AI团队研发的多语言代码大模型基座。CodeShell具有70亿参数,在五千亿Tokens进行了训练,上下文窗口长度为8194。在权威的代码评估Benchmark(HumanEval与MBPP)上,CodeShell取得同等规模最好的性能。与此同时,我们提供了与CodeShell配套的部署方案与IDE插件,请参考代码库[CodeShell](https://github.com/WisdomShell/codeshell)。同时,为了方便中国用户下载,我们在[Modelscope](https://modelscope.cn/organization/WisdomShell)和[Wisemodel](https://www.wisemodel.cn/models/WisdomShell/CodeShell-7B-Chat-int4/)中也上传了对应版本,国内用户可以访问。本仓库为CodeShell-7B-Chat的Int4量化模型的仓库。
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CodeShell is a multi-language code LLM developed by the [Knowledge Computing Lab](http://se.pku.edu.cn/kcl/) of Peking University. CodeShell has 7 billion parameters and was trained on 500 billion tokens with a context window length of 8194. On authoritative code evaluation benchmarks (HumanEval and MBPP), CodeShell achieves the best performance of its scale. Meanwhile, we provide deployment solutions and IDE plugins that complement CodeShell. Please refer to the [CodeShell code repository](https://github.com/WisdomShell/codeshell) for more details. This repository is for the Int4 quantized model of CodeShell-7B-Chat.
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