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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## Quickstart
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Codeshell offers a model in the Hugging Face format. Developers can load and use it with the following code.
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("codeshell", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("codeshell", trust_remote_code=True).cuda()
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inputs = tokenizer('def print_hello_world():', return_tensors='pt').cuda()
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outputs = model.generate(inputs)
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print(tokenizer.decode(outputs[0]))
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```
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```python
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input_text = "<fim_prefix>def print_hello_world():\n <fim_suffix>\n print('Hello world!')<fim_middle>"
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inputs = tokenizer(input_text, return_tensors='pt').cuda()
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outputs = model.generate(inputs)
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print(tokenizer.decode(outputs[0]))
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```
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## Model Details
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## Quickstart
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CodeShell-7B-Chat量化版本 提供了Hugging Face格式的模型,开发者可以通过下列代码加载并使用。
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CodeShell-7B-Chat-int4 offers a model in the Hugging Face format. Developers can load and use it with the following code.
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```python
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import time
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = torch.device('cuda:0')
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model = AutoModelForCausalLM.from_pretrained('WisdomShell/CodeShell-7B-Chat-int4', trust_remote_code=True).to(device)
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tokenizer = AutoTokenizer.from_pretrained('WisdomShell/CodeShell-7B-Chat-int4')
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history = []
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query = '你是谁?'
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response = model.chat(query, history, tokenizer)
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print(response)
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history.append((query, response))
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query = '用Python写一个HTTP server'
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response = model.chat(query, history, tokenizer)
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print(response)
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history.append((query, response))
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```
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开发者也可以通过VS Code与JetBrains插件与CodeShell-7B-Chat量化版本交互,详情请参[VSCode插件仓库](https://github.com/WisdomShell/codeshell-vscode)与[IntelliJ插件仓库](https://github.com/WisdomShell/codeshell-intellij)。
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Developers can also interact with CodeShell-7B-Chat-int4 through VS Code and JetBrains plugins. For details, please refer to the [VSCode Plugin Repository](https://github.com/WisdomShell/codeshell-vscode) and [IntelliJ Plugin Repository](https://github.com/WisdomShell/codeshell-intellij).
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```
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## Model Details
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