---
language: en
tags:
- causal-lm
- gqa
- rope
- swiglu
license: apache-2.0
---

# Experimenting

Small language model (9.9M parameters) trained from scratch.

## Architecture
| Property | Value |
|---|---|
| Layers | 12 |
| Hidden size | 256 |
| Intermediate size | 704 |
| Attention heads | 4 (GQA kv=2) |
| Max sequence length | 1024 |
| Vocab size | 4096 |
| Tied embeddings | True |
| Total parameters | 9.902M |

## Training
- Tokens seen: 2,979,215,382
- Val loss: 2.5222
- Val PPL: 12.46

## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("GODELEV/Experimenting")
model = AutoModelForCausalLM.from_pretrained("GODELEV/Experimenting")
inputs = tokenizer("Hello", return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```