---
language:
- en
license: apache-2.0
tags:
- wikihow
- tutorial
- educational
datasets:
- ajibawa-2023/WikiHow
model-index:
- name: WikiHow-Mistral-Instruct-7B
  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: 60.92
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/WikiHow-Mistral-Instruct-7B
      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: 80.99
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/WikiHow-Mistral-Instruct-7B
      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: 58.57
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/WikiHow-Mistral-Instruct-7B
      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: 62.16
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/WikiHow-Mistral-Instruct-7B
      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/WikiHow-Mistral-Instruct-7B
      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: 30.02
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/WikiHow-Mistral-Instruct-7B
      name: Open LLM Leaderboard
---

**WikiHow-Mistral-Instruct-7B**

This Model is trained on my [WikiHow](https://huggingface.co/datasets/ajibawa-2023/WikiHow) dataset.

This model is **very very good** with generating tutorials in the style of **WikiHow**. By leveraging this repository of practical knowledge, the model has been trained to comprehend and generate text that is highly informative and instructional in nature.
The depth and accuracy of generated tutorials is exceptional. 
The WikiHow dataset encompasses a wide array of topics, ranging from everyday tasks to specialized skills, making it an invaluable resource for refining the capabilities of language models. 
Through this fine-tuning process, the model has been equipped with the ability to offer insightful guidance and assistance across diverse domains.

This is a fully finetuned model. Links for Quantized models are given below.

**GGUF & Exllama**

GGUF: [Link](https://huggingface.co/bartowski/WikiHow-Mistral-Instruct-7B-GGUF)

Exllama v2: [Link](https://huggingface.co/bartowski/WikiHow-Mistral-Instruct-7B-exl2)

Special Thanks to [Bartowski](https://huggingface.co/bartowski) for quantizing this model.


**Training:**

Entire dataset was trained on 4 x A100 80GB. For 3 epoch, training took more than 15 Hours. Axolotl codebase was used for training purpose.
Entire data is trained on Mistral-7B-Instruct-v0.2.


**Example Prompt:**

This model uses **ChatML** prompt format.

```
<|im_start|>system
You are a Helpful Assistant who can write long and very detailed tutorial on various subjects in the styles of WiKiHow. Include in depth explanations for each step and how it helps achieve the desired outcome, including key tips and guidelines.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

```
You can modify above Prompt as per your requirement. 


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**

Example 1

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64aea8ff67511bd3d965697b/HxHaUTOanjTQPXlIw8BuN.jpeg)

Example 2

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64aea8ff67511bd3d965697b/qkkFHgv2pQlc5IlhF-xTn.jpeg)

Example 3

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64aea8ff67511bd3d965697b/dt7JDy-lNQ0hqNx_5nkGC.jpeg)




# [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__WikiHow-Mistral-Instruct-7B)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |61.25|
|AI2 Reasoning Challenge (25-Shot)|60.92|
|HellaSwag (10-Shot)              |80.99|
|MMLU (5-Shot)                    |58.57|
|TruthfulQA (0-shot)              |62.16|
|Winogrande (5-shot)              |74.82|
|GSM8k (5-shot)                   |30.02|