Instructions to use 01-ai/Yi-Coder-1.5B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 01-ai/Yi-Coder-1.5B-Chat with Transformers:

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

pipe = pipeline("text-generation", model="01-ai/Yi-Coder-1.5B-Chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("01-ai/Yi-Coder-1.5B-Chat")
model = AutoModelForCausalLM.from_pretrained("01-ai/Yi-Coder-1.5B-Chat")
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 01-ai/Yi-Coder-1.5B-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "01-ai/Yi-Coder-1.5B-Chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "01-ai/Yi-Coder-1.5B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/01-ai/Yi-Coder-1.5B-Chat

SGLang

How to use 01-ai/Yi-Coder-1.5B-Chat 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 "01-ai/Yi-Coder-1.5B-Chat" \
    --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": "01-ai/Yi-Coder-1.5B-Chat",
		"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 "01-ai/Yi-Coder-1.5B-Chat" \
        --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": "01-ai/Yi-Coder-1.5B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use 01-ai/Yi-Coder-1.5B-Chat with Docker Model Runner:
```
docker model run hf.co/01-ai/Yi-Coder-1.5B-Chat
```

[wish] Release SAMPLE datasets

by twardoch - opened Sep 6, 2024

Discussion

twardoch

Sep 6, 2024

https://github.com/01-ai/Yi-Coder/issues/7

Yi Coder 1.5b is potentially a great model for fine-tuning on one's own codebase. It'd be great if you could release some SAMPLE portions of the dataset used for base training & instruction tuning (and perhaps recipes), so that fine-tuning could be done in a compatible format.

I have an app that includes built-in Python scripting with an app-specific API, and would love to add a chatbot tuned on that API, so that users could get small scripts written from natural language prompts, or even written and executed, without the user ever needed to see the code.

haijian06

Sep 6, 2024

Hi 👋 @twardoch , thank you so much 🙏. We've already forwarded the request regarding the example data for the Coder model. We also have some example datasets for fine-tuning available at https://github.com/01-ai/Yi/tree/main/finetune/yi_example_dataset/data. Although they are not directly related to code, the format is the same. Thanks again!

lorinma

01-ai org Sep 10, 2024

https://github.com/01-ai/Yi-Coder/issues/7

Yi Coder 1.5b is potentially a great model for fine-tuning on one's own codebase. It'd be great if you could release some SAMPLE portions of the dataset used for base training & instruction tuning (and perhaps recipes), so that fine-tuning could be done in a compatible format.

I have an app that includes built-in Python scripting with an app-specific API, and would love to add a chatbot tuned on that API, so that users could get small scripts written from natural language prompts, or even written and executed, without the user ever needed to see the code.

Hi twardoch,
Pasting my reply from github issue:
Unfortunately we will not be releasing our data, but I'd be happy to provide some directions for your requirement.
For pertaining, check https://huggingface.co/datasets/bigcode/starcoderdata and https://huggingface.co/datasets/bigcode/the-stack-dedup .
For code SFT, check Code Feedback / CodeAlpaca / Evol-Instruct-code.
For library-specific SDK, start with library-specific SFT pairs mixed with general-purpose SFT first, continue train is very difficult to do correctly without performance loss, and python codes are not extremely hard to generalize. Just remember to keep your SFT pairs as diverse(both semantic-wise and task-wise) and as high-quality as possible.

Best Regards,
Nuo

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