---
language:
  - en
license: gpl-3.0
library_name: transformers
tags:
  - text-generation
  - tinygpt2
  - causal-lm
  - instruction-tuned
  - sft
  - rope
  - grouped-query-attention
  - rms-norm
datasets:
  - tatsu-lab/alpaca
  - Skylion007/openwebtext
pipeline_tag: text-generation
model-index:
  - name: TinyGPT2-IT
    results: []
---

<div align="center">

# TinyGPT2-IT

### A 95M parameter instruction-tuned language model trained from scratch on a single consumer GPU

[![GitHub](https://img.shields.io/badge/GitHub-NotShrirang%2Ftinygpt-blue?logo=github)](https://github.com/NotShrirang/tinygpt)
[![Demo](https://img.shields.io/badge/Demo-Streamlit-FF4B4B?logo=streamlit)](https://tinygpt.streamlit.app/)
[![License](https://img.shields.io/badge/License-GPL--3.0-green)](https://www.gnu.org/licenses/gpl-3.0.en.html)

</div>

---

## Overview

**TinyGPT2-IT** is an instruction-tuned variant of [TinyGPT2](https://github.com/NotShrirang/tinygpt) — a modern GPT architecture built from scratch using PyTorch. The base model was pretrained on ~6.7B tokens from OpenWebText, then supervised fine-tuned (SFT) on Stanford Alpaca's 52K instruction-response pairs.

The entire pipeline — pretraining, fine-tuning, and inference — runs on a **single NVIDIA RTX 3070 Ti (8 GB VRAM)**.

> This model uses a custom architecture and requires `trust_remote_code=True`.

---

## Architecture

| Component | Detail |
|---|---|
| **Parameters** | ~95M |
| **Layers** | 12 transformer blocks |
| **Attention** | Grouped Query Attention (12 query heads, 4 KV groups) |
| **Embedding dim** | 768 |
| **FFN hidden dim** | 2048 |
| **Position encoding** | Rotary Position Embeddings (RoPE) |
| **Normalization** | RMSNorm |
| **Context window** | 512 tokens |
| **Vocabulary** | 50,304 (GPT-2 tiktoken + PAD token) |
| **Weight tying** | Token embedding ↔ LM head |
| **KV Cache** | Supported for efficient generation |

---

## Training

### Stage 1 — Pretraining

| | |
|---|---|
| **Dataset** | OpenWebText (~6.7B tokens) |
| **Optimizer** | AdamW (fused) |
| **Effective batch** | 262K tokens/step |
| **Precision** | bfloat16 + `torch.compile` |
| **Hardware** | NVIDIA RTX 3070 Ti (8 GB) |

### Stage 2 — Supervised Fine-Tuning (SFT)

| | |
|---|---|
| **Dataset** | Stanford Alpaca (52K instructions) |
| **Epochs** | 3 |
| **Loss masking** | Response-only (instruction tokens are masked) |
| **Final train loss** | 1.91 |
| **Final val loss** | 1.98 |
| **Final val perplexity** | 7.26 |
| **Tokens processed** | ~72M |
| **Prompt format** | `### Instruction: ... ### Response: ...` |

---

## Usage

### Quick Start

```python
from transformers import AutoModelForCausalLM
import tiktoken
import torch

# Load model
model = AutoModelForCausalLM.from_pretrained(
    "NotShrirang/tinygpt2-it",
    trust_remote_code=True,
)
model.eval()

# Tokenize
enc = tiktoken.get_encoding("gpt2")
prompt = "### Instruction:\nWhat is the capital of France?\n\n### Response:\n"
input_ids = torch.tensor([enc.encode(prompt)])

# Generate
with torch.no_grad():
    output = model.generate(input_ids, max_new_tokens=128, do_sample=True, temperature=0.7, top_k=40)

print(enc.decode(output[0].tolist()))
```

### Prompt Format

This model expects instructions in the following template:

```
### Instruction:
{your instruction here}

### Response:
```

For instructions with additional context:

```
### Instruction:
{your instruction here}

### Input:
{additional context}

### Response:
```

---

## Example Outputs

**Factual Q&A**
```
>>> What is the capital of France?
The capital of France is Paris.
```

**Explanation**
```
>>> Explain what machine learning is in simple terms.
Machine learning is a branch of computer science that focuses on using algorithms to
identify patterns in data. These algorithms are used to analyze large amounts of data
and make predictions about future trends.
```

**Creative**
```
>>> Write a motivational quote.
"The only way to make a difference is to be bold and courageous."
```

---

## Limitations

- **Small model** — 95M parameters is far below production LLMs; expect factual errors, repetition, and limited reasoning.
- **Short context** — 512 token window limits the length of conversations and documents.
- **Training data** — pretrained on web text and fine-tuned on synthetic Alpaca data, which may contain biases or inaccuracies.
- **Not safety-aligned** — no RLHF/DPO applied to this checkpoint; the model may produce harmful or inappropriate content.

---

## Model Family

| Model | Params | Description | Link |
|---|---|---|---|
| TinyGPT | 51M | Standard GPT, TinyStories | [GitHub](https://github.com/NotShrirang/tinygpt) |
| TinyGPT-MoE | 85M | Mixture of Experts, TinyStories | [GitHub](https://github.com/NotShrirang/tinygpt) |
| Wikipedia-MoE | 135M | 8-expert MoE, Wikipedia/C4 | [GitHub](https://github.com/NotShrirang/tinygpt) |
| TinyGPT2 | 95M | RoPE + GQA + RMSNorm, OpenWebText | [GitHub](https://github.com/NotShrirang/tinygpt) |
| TinyGPT2.1 | 183M | Scaled TinyGPT2, FineWeb-Edu | [GitHub](https://github.com/NotShrirang/tinygpt) |
| **TinyGPT2-IT** | **95M** | **Instruction-tuned (this model)** | **You are here** |
| TinyGPT2-DPO | 95M | DPO-aligned with Anthropic HH-RLHF | [GitHub](https://github.com/NotShrirang/tinygpt) |

---

## Citation

```bibtex
@misc{tinygpt2-it,
  author       = {Shrirang Mahajan},
  title        = {TinyGPT2-IT: Instruction-Tuned 95M Parameter Language Model},
  year         = {2025},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/NotShrirang/tinygpt2-it}
}
```

---

## License

This model is released under the [GPL-3.0 License](https://www.gnu.org/licenses/gpl-3.0.en.html).