WikiHow-Mistral-Instruct-7B
This Model is trained on my 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
Exllama v2: Link
Special Thanks to 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
Detailed results can be found here
| 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 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard60.920
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard80.990
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard58.570
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard62.160
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard74.820
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard30.020