Yi Coder 9B Chat

🚀 Yi-Coder

Yi-Coder is a series of open - source code language models. It can achieve state - of - the - art coding performance with fewer than 10 billion parameters, offering high - quality coding assistance.

✨ Features

• Long - context Understanding: Excels in long - context understanding with a maximum context length of 128K tokens.
• Rich Language Support: Supports 52 major programming languages, including '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'.

📦 Installation

No specific installation steps are provided in the original document.

💻 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)

📚 Documentation

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%.

📄 License

The project uses the apache - 2.0 license.

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