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Code-13B

Large Language Models (LLMs) are good with code generations. Sometimes they do make mistakes in code generation. How about if they can give detailed explanation along with the code. This is what I have tried over here. The base Llama-2 model was used for training purpose. It is trained on around 74000 set of codes. Each set having 2 conversations. Along with Python, Java, JavaScript, GO, C++, Rust etc. code with detailed explanation is used for training purpose. It is built upon using my existing Dataset Python-Code-23k-ShareGPT. This conversation is in Vicuna/ShareGPT format. Each set, along with code, has detailed explanation.

I have released the new data Code-74k-ShareGPT on which this Model is trained.

Training:

Entire dataset was trained on Azure 4 x A100 80GB. For 3 epoch, training took 42 hours. DeepSpeed codebase was used for training purpose. This was trained on Llama-2 by Meta.

This is a full fine tuned model. Links for quantized models are given below.

GPTQ GGUF & AWQ

GPTQ: Link

GGUF: Link

AWQ: Link

Extremely thankful to TheBloke for making Quantized versions of model.

Example Prompt:

This is a conversation with your helpful AI assistant. AI assistant can generate Code in various Programming Languages along with necessary explanation.

Context
You are a helpful AI assistant.

USER: <prompt>
ASSISTANT:

You can modify above Prompt as per your requirement. I have used ShareGPT/Vicuna format v1.1 .

I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development.

Thank you for your love & support.

Example Output

  1. Navier-Stokes Equation Solver

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  1. KSC Complexity

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  1. GO

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Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 54.81
AI2 Reasoning Challenge (25-Shot) 57.34
HellaSwag (10-Shot) 83.28
MMLU (5-Shot) 53.17
TruthfulQA (0-shot) 42.46
Winogrande (5-shot) 73.56
GSM8k (5-shot) 19.03
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Dataset used to train ajibawa-2023/Code-13B

Evaluation results