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---
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
Example 2
Example 3
# [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|
|