Code-290k-6.7B-Instruct/README.md

---
language:
- en
license: other
tags:
- code
datasets:
- ajibawa-2023/Code-290k-ShareGPT
model-index:
- name: Code-290k-6.7B-Instruct
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 34.9
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-6.7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 51.99
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-6.7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 34.89
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-6.7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 41.95
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-6.7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 52.64
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-6.7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 3.49
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-6.7B-Instruct
      name: Open LLM Leaderboard
---

**Code-290k-6.7B-Instruct**

This model is trained on [DeepSeek-Coder-6.7B-Instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct). I have used my existing dataset [Code-290k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/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](https://huggingface.co/bartowski/Code-290k-6.7B-Instruct-exl2)

Extremely thankful to [Bartowski](https://huggingface.co/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** 

1. **Bayes Theorem - Python**


![image/png](https://cdn-uploads.huggingface.co/production/uploads/64aea8ff67511bd3d965697b/J8uqoT_LYhPW2VpnE1K-8.png)

2. **Fermat's little theorem**

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64aea8ff67511bd3d965697b/H0sc9jk7ypv_N5V7LSANl.png)

3. **The Arrhenius equation using R**

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64aea8ff67511bd3d965697b/BQ8PZhYhoZ9wpVMPXJPnQ.png)


# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Code-290k-6.7B-Instruct)

|             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|