| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: 01-ai/Yi-Coder-9B |
| pipeline_tag: text-generation |
| --- |
| <div align="center"> |
|
|
| <picture> |
| <img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="120px"> |
| </picture> |
|
|
| </div> |
|
|
| <p align="center"> |
| <a href="https://github.com/01-ai">🐙 GitHub</a> • |
| <a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> • |
| <a href="https://twitter.com/01ai_yi">🐤 Twitter</a> • |
| <a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a> |
| <br/> |
| <a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> • |
| <a href="https://01-ai.github.io/">💪 Tech Blog</a> • |
| <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> • |
| <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a> |
| </p> |
|
|
| # Intro |
|
|
| Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters. |
|
|
| Key features: |
| - Excelling in long-context understanding with a maximum context length of 128K tokens. |
| - Supporting 52 major programming languages: |
| ```bash |
| 'java', 'markdown', 'python', 'php', 'javascript', 'c++', 'c#', 'c', 'typescript', 'html', 'go', 'java_server_pages', 'dart', 'objective-c', 'kotlin', 'tex', 'swift', 'ruby', 'sql', 'rust', 'css', 'yaml', 'matlab', 'lua', 'json', 'shell', 'visual_basic', 'scala', 'rmarkdown', 'pascal', 'fortran', 'haskell', 'assembly', 'perl', 'julia', 'cmake', 'groovy', 'ocaml', 'powershell', 'elixir', 'clojure', 'makefile', 'coffeescript', 'erlang', 'lisp', 'toml', 'batchfile', 'cobol', 'dockerfile', 'r', 'prolog', 'verilog' |
| ``` |
|
|
| For model details and benchmarks, see [Yi-Coder blog](https://01-ai.github.io/) and [Yi-Coder README](https://github.com/01-ai/Yi-Coder). |
|
|
| <p align="left"> |
| <img src="https://github.com/01-ai/Yi/blob/main/assets/img/coder/yi-coder-calculator-demo.gif?raw=true" alt="demo1" width="500"/> |
| </p> |
|
|
| # Models |
|
|
| | Name | Type | Length | Download | |
| |--------------------|------|----------------|---------------------------------------------------------------------------------------------------------------------------------------------------| |
| | Yi-Coder-9B-Chat | Chat | 128K | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-9B-Chat) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-9B-Chat) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-9B-Chat) | |
| | Yi-Coder-1.5B-Chat | Chat | 128K | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-1.5B-Chat) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-1.5B-Chat) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-1.5B-Chat) | |
| | Yi-Coder-9B | Base | 128K | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-9B) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-9B) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-9B) | |
| | Yi-Coder-1.5B | Base | 128K | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-1.5B) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-1.5B) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-1.5B) | |
| | | |
|
|
| # Benchmarks |
|
|
| As illustrated in the figure below, Yi-Coder-9B-Chat achieved an impressive 23% pass rate in LiveCodeBench, making it the only model with under 10B parameters to surpass 20%. It also outperforms DeepSeekCoder-33B-Ins at 22.3%, CodeGeex4-9B-all at 17.8%, CodeLLama-34B-Ins at 13.3%, and CodeQwen1.5-7B-Chat at 12%. |
|
|
| <p align="left"> |
| <img src="https://github.com/01-ai/Yi/blob/main/assets/img/coder/bench1.webp?raw=true" alt="bench1" width="1000"/> |
| </p> |
|
|
| # Quick Start |
|
|
| You can use transformers to run inference with Yi-Coder models (both chat and base versions) as follows: |
| ```python |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| |
| device = "cuda" # the device to load the model onto |
| model_path = "01-ai/Yi-Coder-9B-Chat" |
| |
| tokenizer = AutoTokenizer.from_pretrained(model_path) |
| model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto").eval() |
| |
| prompt = "Write a quick sort algorithm." |
| messages = [ |
| {"role": "system", "content": "You are a helpful assistant."}, |
| {"role": "user", "content": prompt} |
| ] |
| text = tokenizer.apply_chat_template( |
| messages, |
| tokenize=False, |
| add_generation_prompt=True |
| ) |
| model_inputs = tokenizer([text], return_tensors="pt").to(device) |
| |
| generated_ids = model.generate( |
| model_inputs.input_ids, |
| max_new_tokens=1024, |
| eos_token_id=tokenizer.eos_token_id |
| ) |
| generated_ids = [ |
| output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
| ] |
| |
| response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
| print(response) |
| ``` |
|
|
| For getting up and running with Yi-Coder series models quickly, see [Yi-Coder README](https://github.com/01-ai/Yi-Coder). |
