Instructions to use codefuse-ai/CodeFuse-DeepSeek-33B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codefuse-ai/CodeFuse-DeepSeek-33B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="codefuse-ai/CodeFuse-DeepSeek-33B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("codefuse-ai/CodeFuse-DeepSeek-33B") model = AutoModelForCausalLM.from_pretrained("codefuse-ai/CodeFuse-DeepSeek-33B") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use codefuse-ai/CodeFuse-DeepSeek-33B 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" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "codefuse-ai/CodeFuse-DeepSeek-33B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/codefuse-ai/CodeFuse-DeepSeek-33B
- SGLang
How to use codefuse-ai/CodeFuse-DeepSeek-33B 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" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "codefuse-ai/CodeFuse-DeepSeek-33B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "codefuse-ai/CodeFuse-DeepSeek-33B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use codefuse-ai/CodeFuse-DeepSeek-33B with Docker Model Runner:
docker model run hf.co/codefuse-ai/CodeFuse-DeepSeek-33B
what is the performance of sql generating?
or this model didn't train on sql data?
Hello, we haven't tested its performance on SQL generation; we'll test it.
In the fine-tuning training data, we did not use a dedicated SQL generation dataset.
@waytohou Hello,
I have tested the performance of our model in SQL generation. I selected the Spider evaluation dataset as our test set, and after using our model for inference generation, I utilized GPT-4 to determine the semantic equivalence between the predictions and the reference answers, resulting in an accuracy of 66.8% (690/1033). The format I used for inference is as follows:
prompt:
<s>system
You are a MySQL SQL Writer. You must generate clean SQL statements using MySQL Syntax according to the user's requirements.
<s>human
What are the names, countries, and ages for every singer in descending order of age?
singer(singer_id|name|country|song_name|song_release_year|age|is_male)
<s>bot
predict:
SELECT name, country, age
FROM singer
ORDER BY age DESC;
label:
SELECT name , country , age FROM singer ORDER BY age DESC