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---
title: ERA SESSION27 - Phi2 Model Finetuning with QLoRA on OpenAssistant Conversations Dataset (OASST1)
emoji: 💻
colorFrom: yellow
colorTo: blue
sdk: gradio
sdk_version: 4.14.0
app_file: app.py
pinned: false
license: mit
---
[**Repository Link**](https://github.com/RaviNaik/ERA-SESSION27)
This is an implementation of [Phi2](https://huggingface.co/microsoft/phi-2) model finetuning using QLoRA stratergy on [OpenAssistant Conversations Dataset (OASST1)](https://huggingface.co/datasets/OpenAssistant/oasst1)
Dataset used to finetune: [OpenAssistant Conversations Dataset (OASST1)](https://huggingface.co/datasets/OpenAssistant/oasst1)
ChatML modified OSST Dataset: [RaviNaik/oasst1-chatml](https://huggingface.co/datasets/RaviNaik/oasst1-chatml)
Finetuned Model: [RaviNaik/Phi2-Osst](https://huggingface.co/RaviNaik/Phi2-Osst)
### Tasks:
1. :heavy_check_mark: Use OpenAssistant dataset.
2. :heavy_check_mark: Finetune Microsoft Phi2 model.
3. :heavy_check_mark: Use QLoRA stratergy.
4. :heavy_check_mark: Create an App on HF space using finetuned model.
## Phi2 Model Description:
```python
PhiForCausalLM(
(transformer): PhiModel(
(embd): Embedding(
(wte): Embedding(51200, 2560)
(drop): Dropout(p=0.0, inplace=False)
)
(h): ModuleList(
(0-31): 32 x ParallelBlock(
(ln): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(resid_dropout): Dropout(p=0.1, inplace=False)
(mixer): MHA(
(rotary_emb): RotaryEmbedding()
(Wqkv): Linear4bit(in_features=2560, out_features=7680, bias=True)
(out_proj): Linear4bit(in_features=2560, out_features=2560, bias=True)
(inner_attn): SelfAttention(
(drop): Dropout(p=0.0, inplace=False)
)
(inner_cross_attn): CrossAttention(
(drop): Dropout(p=0.0, inplace=False)
)
)
(mlp): MLP(
(fc1): Linear4bit(in_features=2560, out_features=10240, bias=True)
(fc2): Linear4bit(in_features=10240, out_features=2560, bias=True)
(act): NewGELUActivation()
)
)
)
)
(lm_head): CausalLMHead(
(ln): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(linear): Linear(in_features=2560, out_features=51200, bias=True)
)
(loss): CausalLMLoss(
(loss_fct): CrossEntropyLoss()
)
)
```
## Training Loss Curve:
### Training Output
```python
TrainOutput(global_step=500, training_loss=1.4746462078094482, metrics={'train_runtime': 4307.6684, 'train_samples_per_second': 3.714, 'train_steps_per_second': 0.116, 'total_flos': 6.667526640623616e+16, 'train_loss': 1.4746462078094482, 'epoch': 1.62})
```
### Loss vs Steps Logs
## Sample Results:
## Gradio UI:
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