---
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
library_name: transformers
tags:
  - text-generation
  - sft
  - lora
  - tinyllama
---

# Best SFT Model — SFT-T4

TinyLlama-1.1B-Chat-v1.0 fine-tuned via Supervised Fine-Tuning (SFT) on
[databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k).

## Authors
- Ahmad Murtaza (24478)
- Simra Sheikh (27135)

## Project Context
This model was developed as part of Assignment 04 for the NLP with Deep Learning
course at the Institute of Business Administration (IBA). The assignment involves
building a full SFT → DPO fine-tuning pipeline on TinyLlama-1.1B-Chat-v1.0 and
evaluating the impact of instruction tuning and preference alignment.

## Training config
```
trial         : SFT-T4
lora_rank     : 16
lora_alpha    : 32
target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj']
learning_rate : 0.0002
epochs        : 1
val_loss      : 1.7798
```

## Evaluation (10-prompt test set, merged model)
| Model                           | BLEU-4 | BERTScore F1 |
| ------------------------------- | ------ | ------------ |
| Base (TinyLlama-1.1B)           | 2.1400 | 85.4700 |
| **Best SFT (SFT-T4)** | **2.4200** | **87.1100** |