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---
library_name: transformers
tags:
- code
- instruct
- llama2
datasets:
- HuggingFaceH4/no_robots
base_model: meta-llama/Llama-2-7b-hf
license: apache-2.0
---
### Finetuning Overview:
**Model Used:** meta-llama/Llama-2-7b-hf
**Dataset:** HuggingFaceH4/no_robots
#### Dataset Insights:
[No Robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better.
#### 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 39mins 4secs for 1 epoch using an A6000 48GB GPU.
- Costed `$1.313` for the entire epoch.
#### Hyperparameters & Additional Details:
- **Epochs:** 1
- **Cost Per Epoch:** $1.313
- **Total Finetuning Cost:** $1.313
- **Model Path:** meta-llama/Llama-2-7b-hf
- **Learning Rate:** 0.0002
- **Data Split:** 99% train 1% validation
- **Gradient Accumulation Steps:** 4
- **lora r:** 32
- **lora alpha:** 64
---
Prompt Structure
```
### INSTRUCTION:
[instruction]
### RESPONSE:
[text]
```
Train loss :
license: apache-2.0 |