Yi Coder 9B

🚀 Yi-Coder

Yi-Coder is a series of open - source code language models that offers state - of - the - art coding performance with fewer than 10 billion parameters.

🐙 GitHub • 👾 Discord • 🐤 Twitter • 💬 WeChat
📝 Paper • 💪 Tech Blog • 🙌 FAQ • 📗 Learning Hub

✨ Features

• Long - context understanding: Excelling in long - context understanding with a maximum context length of 128K tokens.
• Multi - language support: Supporting 52 major programming languages:

  '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 and Yi - Coder README.

📦 Models

🔧 Benchmarks

As shown 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%.

🚀 Quick Start

You can use transformers to run inference with Yi - Coder models (both chat and base versions) as follows:

💻 Usage Examples

Basic Usage

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.

📄 License

The project uses the Apache - 2.0 license.