README.md

---
library_name: peft
tags:
- meta-llama
- code
- instruct
- WizardLM
- Mistral-7B-v0.1
datasets:
- WizardLM/WizardLM_evol_instruct_70k
base_model: mistralai/Mistral-7B-v0.1
license: apache-2.0
---

### Finetuning Overview:

**Model Used:** mistralai/Mistral-7B-v0.1  
**Dataset:** WizardLM/WizardLM_evol_instruct_70k  

#### Dataset Insights:

The WizardLM/WizardLM_evol_instruct_70k dataset, tailored specifically for enhancing interactive capabilities, it was developed using EVOL-Instruct method.Which will basically enhance a smaller dataset, with tougher quesitons for the LLM to perform
#### Finetuning Details:

With the utilization of [MonsterAPI](https://monsterapi.ai)'s [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm), this finetuning:

- Was achieved with great cost-effectiveness.
- Completed in a total duration of 5hrs 18mins for 1 epoch using an A6000 48GB GPU.
- Costed `$10` for the entire epoch.

#### Hyperparameters & Additional Details:

- **Epochs:** 1
- **Cost Per Epoch:** $10
- **Total Finetuning Cost:** $10
- **Model Path:** mistralai/Mistral-7B-v0.1
- **Learning Rate:** 0.0002
- **Data Split:** 90% train 10% validation
- **Gradient Accumulation Steps:** 4

---
Prompt Structure
```
### INSTRUCTION:
[instruction]

### RESPONSE:
[output]
```
Training loss :
![training loss](train-loss.png "Training loss")


---
#### Benchmark Results
![ARC HELLSWAG TRUTHFULMQ Benchmark comparison](./updated_title_performance_comparison_bar_plot.png)

```
ARC (arc_challenge, acc_norm)   0.5543
HellaSwag (hellaswag, acc_norm) 0.7979
TruthfulQA (truthfulqa_mc2)     0.4781
```

license: apache-2.0