Instructions to use codefuse-ai/CodeFuse-DeepSeek-33B-4bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use codefuse-ai/CodeFuse-DeepSeek-33B-4bits with Transformers:

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

pipe = pipeline("text-generation", model="codefuse-ai/CodeFuse-DeepSeek-33B-4bits")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("codefuse-ai/CodeFuse-DeepSeek-33B-4bits")
model = AutoModelForCausalLM.from_pretrained("codefuse-ai/CodeFuse-DeepSeek-33B-4bits")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use codefuse-ai/CodeFuse-DeepSeek-33B-4bits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "codefuse-ai/CodeFuse-DeepSeek-33B-4bits"
# 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-DeepSeek-33B-4bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/codefuse-ai/CodeFuse-DeepSeek-33B-4bits

SGLang

How to use codefuse-ai/CodeFuse-DeepSeek-33B-4bits 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-DeepSeek-33B-4bits" \
    --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-DeepSeek-33B-4bits",
		"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-DeepSeek-33B-4bits" \
        --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-DeepSeek-33B-4bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use codefuse-ai/CodeFuse-DeepSeek-33B-4bits with Docker Model Runner:
```
docker model run hf.co/codefuse-ai/CodeFuse-DeepSeek-33B-4bits
```

codefuse-admin commited on Jan 15, 2024

Commit

eaa10ca

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@@ -144,7 +144,7 @@ def separate_paren_groups(paren_string: str) -> List[str]:
 ```
-Specifically, we also add the Programming Language Tag (e.g. ```# language: Python``` for Python) used by CodeGeex models.
@@ -358,7 +358,7 @@ def separate_paren_groups(paren_string: str) -> List[str]:
 ```
-特别地，我们也使用了CodeGeeX系列模型采用的编程语言区分标签（例如，对于Python语言，我们会使用```# language: Python```）。
 ## 快速使用

 ```
+Specifically, we also add the Programming Language Tag (e.g. "```# language: Python```" for Python) used by CodeGeex models.
 ```
+特别地，我们也使用了CodeGeeX系列模型采用的编程语言区分标签（例如，对于Python语言，我们会使用"```# language: Python```"）。
 ## 快速使用