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 |