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library_name: transformers
tags:
- convergent-evolution
- fourier-features
- number-embeddings
license: mit
datasets:
- HuggingFaceFW/fineweb-edu
---
# convergent-llama-300M-muon-isolate
A 300M-parameter language model trained from scratch on **FineWeb-Edu 10BT** (~9.4B tokens, 1 epoch) as part of the *Convergent Evolution* project, which investigates how Fourier features emerge in LLM number embeddings.
## Model details
| | |
|---|---|
| **Architecture** | LLaMA-style Transformer (12 layers, 1024 hidden, 16 heads, GQA) |
| **Parameters** | ~300M |
| **Optimizer** | Muon (for 2D weights) + AdamW (for embeddings/bias/norm) |
| **Data perturbation** | block-diagonal attention mask (numbers cannot attend to context) |
| **Training data** | [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) sample-10BT (~9.4B tokens) |
| **Context length** | 1024 |
| **Tokenizer** | Llama 3 (128K vocab) |
| **Batch size** | 512 sequences |
## Training dynamics
Intermediate checkpoints are saved as branches: `tokens-200M`, `tokens-400M`, ..., `tokens-9.6B`.
```python
from transformers import AutoModelForCausalLM
# Load final checkpoint
model = AutoModelForCausalLM.from_pretrained("deqing/convergent-llama-300M-muon-isolate")
# Load intermediate checkpoint (e.g., at 1B tokens)
model = AutoModelForCausalLM.from_pretrained("deqing/convergent-llama-300M-muon-isolate", revision="tokens-1B")
```
## Citation
Paper forthcoming.
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