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convergent-mamba2-300M-adamw-original
deqing/convergent-mamba2-300M-adamw-original
convergent-mamba2-300M-adamw-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 10BT (~9.4B tokens, 1 epoch) as part of the Convergent Evolution project, which investigates how Fourier features emerge in LLM number embeddings.
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316M
1.3 GB on disk
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At a glance
- Task
- Text Generation
- Library
- transformers
- License
- mit
- Model type
- mamba2
- Access
- Public
- Created
- Mar 29, 2026
- Updated
- Mar 29, 2026
- SHA
- 6c653f98
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Datasets
Model card
From the Hugging Face model README
convergent-mamba2-300M-adamw-original
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 | Mamba-2 SSM (state-space model) |
| Parameters | ~300M |
| Optimizer | AdamW |
| 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 |
Training dynamics
Intermediate checkpoints are saved as branches: tokens-200M, tokens-400M, ..., tokens-9.6B.
from transformers import AutoModelForCausalLM
# Load final checkpoint
model = AutoModelForCausalLM.from_pretrained("deqing/convergent-mamba2-300M-adamw-original")
# Load intermediate checkpoint (e.g., at 1B tokens)
model = AutoModelForCausalLM.from_pretrained("deqing/convergent-mamba2-300M-adamw-original", revision="tokens-1B")
Citation
Paper forthcoming.
- Task
- Text Generation
- Library
- transformers
- Type
- mamba2
- License
- mit
- Created
- Mar 29, 2026
- Updated
- Mar 29, 2026