File size: 1,480 Bytes
4390f4c c4a1d81 a35dad5 c4a1d81 0f60e61 f83c306 a35dad5 4390f4c a35dad5 4390f4c f83c306 c4a1d81 d67c2dd 4390f4c a35dad5 d67c2dd a42fc92 a35dad5 f83c306 a35dad5 02806bf a35dad5 a42fc92 a35dad5 a42fc92 a35dad5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 |
---
library_name: peft
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 |