Code-290k-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 290000 set of codes. Each set having 2 conversations. Along with Python, Java, JavaScript, GO, C++, Rust, Ruby, Sql, MySql, R, Julia, Haskell, etc. code with detailed explanation is used for training purpose. It is built upon using my existing Datasets Python-Code-23k-ShareGPT and Code-74k-ShareGPT . This conversation is in Vicuna/ShareGPT format. Each set, along with code, has detailed explanation.
I have released the new data Code-290k-ShareGPT on which this Model is trained.
Training:
Entire dataset was trained on 4 x A100 80GB. For 3 epoch, training took 165 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 & Exllama
GPTQ: Link
GGUF: Link
AWQ: Link
Exllama v2: Link
Extremely thankful to TheBloke and Bartowski for making Quantized versions of the 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
Will update soon.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 52.96 |
AI2 Reasoning Challenge (25-Shot) | 56.06 |
HellaSwag (10-Shot) | 81.55 |
MMLU (5-Shot) | 51.99 |
TruthfulQA (0-shot) | 37.65 |
Winogrande (5-shot) | 72.69 |
GSM8k (5-shot) | 17.82 |
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Dataset used to train ajibawa-2023/Code-290k-13B
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard56.060
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard81.550
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard51.990
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard37.650
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard72.690
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard17.820