CodeGeeX4/candle_demo

• candle-codegeex4_9b
  • Cli
  • Output_Example
• Citation

candle-codegeex4_9b

THUDM/CodeGeeX4 is a versatile model for all AI software development scenarios, including code completion, code interpreter, web search, function calling, repository-level Q&A and much more.

• Github
• HomePage
• huggingface
• Candle
• 目前openai-api正在开发中

Cli

  cargo build --release -p codegeex4-cli # Cpu
  cargo build --release -p codegeex4-cli --features cuda # if cuda is avalibe
  ./target/release/codegeex4-cli --sample-len 500

Output_Example

  avx: false, neon: false, simd128: false, f16c: false
  temp: 0.95 repeat-penalty: 1.10 repeat-last-n: 64
  cache path /root/autodl-tmp
  Prompt: [please write a FFT in rust]
  Using Seed 11511762269791786684
  DType is BF16
  transofrmer layers create
  模型加载完毕 4
  starting the inference loop

   开始生成
  samplelen 500

  500 tokens generated (34.60 token/s)
  Result:

  Sure, I can help you with that. Here's an example of a Fast Fourier Transform (FFT) implementation in Rust:

  ```rust
  use num_complex::Complex;

  fn fft(input: &[Complex<f64> > ] ) -> Vec<Complex<f64> > > {
      let n = input.len();
    
      if n == 1 {
	  return vec![input[0]]];
      }
    
      let mut even = vec![];
      let mut odd = vec![];
    
      for i in 0..n {

	      if i % 2 == 0 {
	      even.push(input[i]);
	  } else {
	      odd.push(input[i]);
	  }
      }
    
      let even_fft = fft(&even);
      let odd_fft = fft(&odd);
    
      let mut output = vec![];
    
      for k in 0..n/2 {
	  let t = Complex::new(0.0, -2.0 * std::f64::consts::PI * (k as f64) / (n as f64))) ).exp();
        
	  output.push(even_fft[k] + odd_fft[k] * t]);
	  output.push(even_fft[k] - odd_fft[k] * t]);
      }
    
      return output;
  }
  ```

  This implementation uses the Cooley-Tukey algorithm to perform the FFT. The function takes an array of complex numbers and returns an array of complex numbers which is the result of the FFT.

Citation

  @inproceedings{zheng2023codegeex,
  title={CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X},
  author={Qinkai Zheng and Xiao Xia and Xu Zou and Yuxiao Dong and Shan Wang and Yufei Xue and Zihan Wang and Lei Shen and Andi Wang and Yang Li and Teng Su and Zhilin Yang and Jie Tang},
  booktitle={Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages={5673--5684},
  year={2023}
}