README.md

metadata

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

This is an implementation of Phi2 model finetuning using QLoRA stratergy on OpenAssistant Conversations Dataset (OASST1)

Dataset used to finetune: OpenAssistant Conversations Dataset (OASST1) ChatML modified OSST Dataset: RaviNaik/oasst1-chatml Finetuned Model: 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:

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

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: