Instructions to use codefuse-ai/CodeFuse-QWen-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use codefuse-ai/CodeFuse-QWen-14B with Transformers:

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

pipe = pipeline("text-generation", model="codefuse-ai/CodeFuse-QWen-14B", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("codefuse-ai/CodeFuse-QWen-14B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use codefuse-ai/CodeFuse-QWen-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "codefuse-ai/CodeFuse-QWen-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "codefuse-ai/CodeFuse-QWen-14B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/codefuse-ai/CodeFuse-QWen-14B

SGLang

How to use codefuse-ai/CodeFuse-QWen-14B 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 "codefuse-ai/CodeFuse-QWen-14B" \
    --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": "codefuse-ai/CodeFuse-QWen-14B",
		"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 "codefuse-ai/CodeFuse-QWen-14B" \
        --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": "codefuse-ai/CodeFuse-QWen-14B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use codefuse-ai/CodeFuse-QWen-14B with Docker Model Runner:
```
docker model run hf.co/codefuse-ai/CodeFuse-QWen-14B
```

codefuse-admin commited on Nov 4, 2023

Commit

83d3e08

1 Parent(s): 85e6030

Update README.md

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@@ -62,7 +62,7 @@ CodeFuse-QWen-14B is a 14B Code-LLM finetuned by QLoRA of multiple code tasks on
 | **CodeFuse-QWen-14B**       |     **48.78%**    | 2023.10 |
 <p align="center">
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/650a8f083f8a38f064aa1f43/2ZUZ6mIg7fMVsLqPjpY_i.png" width="50%" />
 </p>
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@@ -210,7 +210,7 @@ CodeFuse-QWen-14B 是一个通过QLoRA对基座模型QWen-14B进行多代码任
 | **CodeFuse-QWen-14B**       |     **48.78%**     | 2023.8 |
 <p align="center">
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/650a8f083f8a38f064aa1f43/2ZUZ6mIg7fMVsLqPjpY_i.png" width="50%" />
 </p>
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 | **CodeFuse-QWen-14B**       |     **48.78%**    | 2023.10 |
 <p align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/650a8f083f8a38f064aa1f43/2ZUZ6mIg7fMVsLqPjpY_i.png" width="90%" />
 </p>
 <br>
 | **CodeFuse-QWen-14B**       |     **48.78%**     | 2023.8 |
 <p align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/650a8f083f8a38f064aa1f43/2ZUZ6mIg7fMVsLqPjpY_i.png" width="90%" />
 </p>
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