Instructions to use WisdomShell/CodeShell-7B-Chat-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

ruixie commited on Nov 1, 2023

Commit

d7ec959

1 Parent(s): 57963d9

Update README.md

Browse files

Files changed (1) hide show

README.md +3 -7

README.md CHANGED Viewed

@@ -121,16 +121,12 @@ Despite our significant efforts to ensure compliance in the data used during the
 1. 关联方的服务或产品的每日平均活跃用户数（DAU）原则上不能超过100万。
 2. 关联方不得是面向个人用户的软件服务提供商或云服务提供商。
 3. 关联方不存在将获得授予的商业许可，在未经许可的前提下将其再授权给其他第三方的可能性。
-在满足上述条件的前提下，您需要通过向codeshell.opensource@gmail.com发送电子邮件提交申请。经审核通过后，将授予您一个全球的、非排他的、不可转让的、不可再授权的商业版权许可。
-Community use of the CodeShell model requires adherence to the CodeShell Model License Agreement and the Apache 2.0 License. The CodeShell model is allowed for commercial use, but if you plan to use the CodeShell model or its derivatives for commercial purposes, you need to ensure that the entity meets the following conditions:
 1. The Daily Active Users (DAU) of your or your affiliate's service or product is less than 1 million.
 2. You and your affiliates must not be a software service provider or cloud service provider targeting individual users.
 3. You and your affiliates should not have the possibility of sub-licensing to other third parties without obtaining the commercial license granted.
-Upon meeting the above conditions, you need to submit an application by sending an email to codeshell.opensource@gmail.com. After approval, you will be granted a global, non-exclusive, non-transferable, non-sublicensable commercial copyright license.

 1. 关联方的服务或产品的每日平均活跃用户数（DAU）原则上不能超过100万。
 2. 关联方不得是面向个人用户的软件服务提供商或云服务提供商。
 3. 关联方不存在将获得授予的商业许可，在未经许可的前提下将其再授权给其他第三方的可能性。
+在满足上述条件的前提下，您需要通过向codeshell.opensource@gmail.com发送电子邮件，提交《CodeShell模型许可协议》要求的申请材料。经审核通过后，将授予您一个全球的、非排他的、不可转让的、不可再授权的商业版权许可。
+Community use of the CodeShell model requires adherence to the ["CodeShell License Agreement"](https://huggingface.co/WisdomShell/CodeShell-7B/resolve/main/CodeShell%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf) and the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). The CodeShell model is allowed for commercial use, but if you plan to use the CodeShell model or its derivatives for commercial purposes, you need to ensure that the entity meets the following conditions:
 1. The Daily Active Users (DAU) of your or your affiliate's service or product is less than 1 million.
 2. You and your affiliates must not be a software service provider or cloud service provider targeting individual users.
 3. You and your affiliates should not have the possibility of sub-licensing to other third parties without obtaining the commercial license granted.
+Under the aforementioned conditions, you need to submit the application materials required by the "CodeShell Model License Agreement" by sending an email to codeshell.opensource@gmail.com. After approval, you will be granted a global, non-exclusive, non-transferable, non-sublicensable commercial copyright license.