Code-290k-6.7B-Instruct
This model is trained on DeepSeek-Coder-6.7B-Instruct. I have used my existing dataset Code-290k-ShareGPT for training purpose. It is trained on around 290000 set of codes. Along with Python, Java, JavaScript, GO, C++, Rust, Ruby, Sql, MySql, R, Julia, Haskell, etc. code with detailed explanation is used for training purpose. This model utilises Alpaca format. Besides code generation it will also give you explanation.
Training:
Entire dataset was trained on 4 x A100 80GB. For 3 epoch, training took 85 hours. DeepSeek-Coder codebase and DeepSpeed was used for training purpose.
This is a full fine tuned model.
Links for quantized models are given below.
Exllama
Exllama v2:Link
Extremely thankful to Bartowski for making Quantized version 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.
### Instruction:
{instruction}
### Response:
You can modify above Prompt as per your requirement. I have used Alpaca format.
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.
Examples
- Bayes Theorem - Python
- Fermat's little theorem
- The Arrhenius equation using R
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 36.64 |
AI2 Reasoning Challenge (25-Shot) | 34.90 |
HellaSwag (10-Shot) | 51.99 |
MMLU (5-Shot) | 34.89 |
TruthfulQA (0-shot) | 41.95 |
Winogrande (5-shot) | 52.64 |
GSM8k (5-shot) | 3.49 |
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Dataset used to train ajibawa-2023/Code-290k-6.7B-Instruct
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard34.900
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard51.990
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard34.890
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard41.950
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard52.640
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard3.490