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convergent-lstm-4layer-muon-original
deqing/convergent-lstm-4layer-muon-original
convergent-lstm-4layer-muon-original is a text generation model from deqing. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.
A 300M-parameter language model trained from scratch on FineWeb-Edu sample-10BT (~9.4B tokens) as part of the Convergent Evolution project, which investigates how Fourier features emerge in LLM number embeddings.
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At a glance
- Task
- Text Generation
- Library
- transformers
- License
- mit
- Model type
- lstm_lm
- Access
- Public
- Created
- Mar 29, 2026
- Updated
- Mar 29, 2026
- SHA
- 067242c3
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Model card
From the Hugging Face model README
convergent-lstm-4layer-muon-original
A 300M-parameter language model trained from scratch on FineWeb-Edu sample-10BT (~9.4B tokens) as part of the Convergent Evolution project, which investigates how Fourier features emerge in LLM number embeddings.
Model details
| Architecture | LSTM (4 layers, window-4 context) |
| Parameters | ~300M |
| Optimizer | Muon (for 2D weights) + AdamW (for embeddings/bias/norm) |
| Data perturbation | standard (unperturbed) text |
| Training data | FineWeb-Edu sample-10BT (~9.4B tokens) |
| Context length | 1024 |
| Tokenizer | Llama 3 (128K vocab) |
| Batch size | 512 sequences |
Usage
from transformers import AutoModelForCausalLM
# Load final checkpoint
model = AutoModelForCausalLM.from_pretrained("deqing/convergent-lstm-4layer-muon-original")
Training dynamics
Intermediate checkpoints are saved as branches: tokens-200M, tokens-400M, ...
# Load intermediate checkpoint (e.g., at 1B tokens)
model = AutoModelForCausalLM.from_pretrained("deqing/convergent-lstm-4layer-muon-original", revision="tokens-1B")
Citation
Paper forthcoming.
- Task
- Text Generation
- Library
- transformers
- Type
- lstm_lm
- License
- mit
- Created
- Mar 29, 2026
- Updated
- Mar 29, 2026