README.md

---
language:
- en
license: apache-2.0
tags:
- code
- finetune
- synthetic data
- text-generation-inference
- conversational
datasets:
- ajibawa-2023/OpenHermes-2.5-Code-290k
model-index:
- name: OpenHermes-2.5-Code-290k-13B
  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: 57.34
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/OpenHermes-2.5-Code-290k-13B
      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: 80.48
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/OpenHermes-2.5-Code-290k-13B
      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: 56.53
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/OpenHermes-2.5-Code-290k-13B
      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: 52.5
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/OpenHermes-2.5-Code-290k-13B
      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: 74.82
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/OpenHermes-2.5-Code-290k-13B
      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: 58.3
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/OpenHermes-2.5-Code-290k-13B
      name: Open LLM Leaderboard
---

**OpenHermes-2.5-Code-290k-13B**

OpenHermes-2.5-Code-290k-13B is a state of the art Llama-2 Fine-tune, which is trained on additional code dataset.
This model is trained on my existing dataset [OpenHermes-2.5-Code-290k](https://huggingface.co/datasets/ajibawa-2023/OpenHermes-2.5-Code-290k). 
This dataset is amalgamation of two datasets. I have used [OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) a super quality dataset made avaliable by teknium. Other datset is my own [Code-290k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Code-290k-ShareGPT). 
Dataset is in Vicuna/ShareGPT format. There are around **1.29 million** set of conversations. I have cleaned the dataset provided by Teknium and removed metadata such as "source" & "category" etc. This dataset has primarily synthetically generated instruction and chat samples. 

This model has enhanced coding capabilities besides other capabilities such as **Blogging, story generation, Q&A and many more**.

**Training:**

Entire model was trained on 4 x A100 80GB. For 2 epoch, training took **21 Days**. Fschat & 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 will be updated soon.


**GPTQ, GGUF, AWQ & Exllama**

GPTQ: TBA

GGUF: TBA

AWQ: TBA

Exllama v2: TBA





**Example Prompt:**
```
This is a conversation with your helpful AI assistant. AI assistant can generate Code in various Programming Languages along with necessary explanation. It can generate Story, Blogs .....

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**

I will update soon.
# [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__OpenHermes-2.5-Code-290k-13B)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |63.33|
|AI2 Reasoning Challenge (25-Shot)|57.34|
|HellaSwag (10-Shot)              |80.48|
|MMLU (5-Shot)                    |56.53|
|TruthfulQA (0-shot)              |52.50|
|Winogrande (5-shot)              |74.82|
|GSM8k (5-shot)                   |58.30|