---
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- decoder-only
- nlp
- autoregressive
- rope
- gqa
- rmsnorm
- swiglu
- from-scratch
datasets:
- roneneldan/TinyStories
license: apache-2.0
model-index:
- name: GatorGPT2
  results: []
---

# 🐊 GatorGPT2

**GatorGPT2** is a small, decoder-only Transformer trained from scratch on a subset of **TinyStories** for next-token prediction.  
It uses **RoPE** (rotary positional embeddings), **GQA** (grouped-query attention), **RMSNorm**, and a **SwiGLU MLP**.  
Tokenizer is **tiktoken** with **p50k_base** vocabulary.

> **Repo**: `kunjcr2/GatorGPT2`  
> **Intended use**: research, experimentation, educational demos for training/serving custom LMs

---

## 🔧 Architecture

- **Type**: Decoder-only, causal LM  
- **Layers**: `num_hidden_layers = 10`  
- **Hidden size**: `hidden_size = 448`  
- **Heads**: `num_attention_heads = 8` (GQA with 2 KV heads per query group)  
- **FFN**: SwiGLU, `d_ff ≈ 2× hidden_size`  
- **Norm**: RMSNorm (pre-norm blocks)  
- **Positional**: RoPE  
- **Vocab**: `vocab_size = 50,257` (tiktoken p50k_base)  
- **Context length**: `max_position_embeddings = 1024`  
- **Weight tying**: output head tied with token embeddings  
- **Files**:
  - `pytorch_model.bin` (or `model.safetensors`)
  - `config.json` (`model_type: "gator-transformer"`, `auto_map` provided)
  - `modeling_gator.py`, `configuration_gator.py`, `__init__.py`
  - `tokenizer_manifest.json` → `{ "library": "tiktoken", "encoding": "p50k_base" }`

> Custom code is loaded via `trust_remote_code=True`.

---

## 📦 Install

```bash
pip install torch transformers tiktoken
````

---

## 🚀 Quickstart (Transformers + tiktoken)

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

MODEL_ID = "kunjcr2/GatorGPT2"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# Load model (uses custom modeling code)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    trust_remote_code=True,
    torch_dtype=torch.float32,
).to(DEVICE).eval()

# Tokenizer (p50k_base via tiktoken)
tok = tiktoken.get_encoding("p50k_base")

def generate_greedy(prompt: str, max_new_tokens: int = 64) -> str:
    ids = tok.encode(prompt)
    x = torch.tensor([ids], device=DEVICE)
    for _ in range(max_new_tokens):
        with torch.no_grad():
            out = model(x)
        logits = out["logits"] if isinstance(out, dict) else out.logits
        next_id = int(torch.argmax(logits[0, -1]))
        x = torch.cat([x, torch.tensor([[next_id]], device=DEVICE)], dim=1)
    return tok.decode(x[0].tolist()).replace("<|endoftext|>", "").strip()

print(generate_greedy("Little girl was"))
```

### Temperature-only sampling (no top-k/p)

```python
def generate_temp(prompt, max_new_tokens=64, temperature=0.9):
    ids = tok.encode(prompt)
    x = torch.tensor([ids], device=DEVICE)
    for _ in range(max_new_tokens):
        with torch.no_grad():
            logits = model(x).logits[0, -1] / max(temperature, 1e-6)
        probs = torch.softmax(logits, dim=-1)
        next_id = torch.multinomial(probs, 1).item()
        x = torch.cat([x, torch.tensor([[next_id]], device=DEVICE)], dim=1)
    return tok.decode(x[0].tolist()).replace("<|endoftext|>", "").strip()
```

---

## 🌐 Serving with vLLM (Optional)

```bash
python -m vllm.entrypoints.openai.api_server \
  --model kunjcr2/GatorGPT2 \
  --tokenizer kunjcr2/GatorGPT2 \
  --trust-remote-code \
  --dtype float32 \
  --max-model-len 1024 \
  --host 0.0.0.0 --port 8000
```

Call it:

```bash
curl http://localhost:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"kunjcr2/GatorGPT2","prompt":"Little girl was","max_tokens":64,"temperature":0.9}'
```

---

## 🧪 Training Summary

* **Data**: `roneneldan/TinyStories` (train split; subset of \~1.5M stories)
* **Objective**: causal LM (next-token prediction), cross-entropy
* **Optimizer**: AdamW (`lr=3e-4`, `weight_decay=0.01`, `eps=1e-8`)
* **Precision**: bf16 autocast on CUDA during forward for speed
* **Batching**: sliding windows via a `FastDataset` (window size e.g. 512, stride 256)
* **Eval**: periodic validation over fixed batches; train loss downsampled to eval steps for plotting
* **Hardware**: intended for A100-class GPUs; also runs on CPU for debug (slow)

> This is a *from-scratch* toy/educational model; quality depends heavily on steps, data cleaned, and schedule. Expect simple, short English generations.

---

## ✅ Intended Use

* Research on small decoder-only Transformers
* Educational demos (training, saving, model hub, vLLM serving)
* Baseline for experimenting with:

  * LoRA/QLoRA, quantization, distillation
  * Attention variants (Flash-Attention, GQA configs)
  * Data curation and scaling laws

**Not** intended for production or safety-critical use.

---

## ⚠️ Limitations & Risks

* Trained on children’s story data ⇒ limited world knowledge & reasoning
* May output incoherent, repetitive, or undesirable text
* No instruction-tuning or RLHF
* Tokenizer is `tiktoken p50k_base` (not a standard HF tokenizer), so examples use `tiktoken` directly

---

## 📁 Repo Structure

```
.
├── config.json
├── pytorch_model.bin        # or model.safetensors
├── modeling_gator.py        # custom architecture (RoPE, GQA, RMSNorm, SwiGLU)
├── configuration_gator.py
├── __init__.py
└── tokenizer_manifest.json  # { "library": "tiktoken", "encoding": "p50k_base" }
```

`config.json` includes:

```json
{
  "model_type": "gator-transformer",
  "architectures": ["GatorModel"],
  "auto_map": {
    "AutoConfig": "configuration_gator.GatorConfig",
    "AutoModelForCausalLM": "modeling_gator.GatorModel"
  }
}
```

---

## 📊 Evaluation

No formal benchmarks reported. You can compute loss/perplexity on your own validation subset:

```python
import math, torch
from torch.utils.data import DataLoader, TensorDataset

# ...build a DataLoader of (input_ids, target_ids) pairs...
def eval_loss(model, loader, device="cuda"):
    model.eval(); total, n = 0.0, 0
    with torch.no_grad():
        for x, y in loader:
            x, y = x.to(device), y.to(device)
            logits = model(x).logits
            loss = torch.nn.functional.cross_entropy(
                logits.view(-1, logits.size(-1)), y.view(-1)
            )
            total += loss.item(); n += 1
    return total / max(n,1)

val_loss = eval_loss(model, your_val_loader)
print("val loss:", val_loss, "  ppl:", math.exp(val_loss))
```

---

## 📜 License

**apache-2.0**

---

## 🙌 Acknowledgements

* **TinyStories** dataset by Ronen Eldan et al. (`roneneldan/TinyStories`)
* Community tooling: **PyTorch**, **🤗 Transformers**, **tiktoken**, **vLLM**

---

## ✉️ Citation

If you use this model, please cite this repository:

```bibtex
@software{GatorGPT2_2025,
  author = {Kunj},
  title = {GatorGPT2: a small decoder-only Transformer with RoPE+GQA},
  year = {2025},
  url = {https://huggingface.co/kunjcr2/GatorGPT2}
}
```