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convergent-llama-300M-adamw-isolate
deqing/convergent-llama-300M-adamw-isolate
convergent-llama-300M-adamw-isolate 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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320M
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At a glance
- Task
- Text Generation
- Library
- transformers
- License
- mit
- Model type
- llama
- Access
- Public
- Created
- Mar 29, 2026
- Updated
- Mar 29, 2026
- SHA
- e2bcd652
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Datasets
Model card
From the Hugging Face model README
convergent-llama-300M-adamw-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 | AdamW |
| Data perturbation | block-diagonal attention mask (numbers cannot attend to context) |
| 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-llama-300M-adamw-isolate")
# Load intermediate checkpoint (e.g., at 1B tokens)
model = AutoModelForCausalLM.from_pretrained("deqing/convergent-llama-300M-adamw-isolate", revision="tokens-1B")
Citation
Paper forthcoming.
- Task
- Text Generation
- Library
- transformers
- Type
- llama
- License
- mit
- Created
- Mar 29, 2026
- Updated
- Mar 29, 2026