melephant/2l-bilinear-attn

pile

    Quadratic/bilinear attention causal language model trained with the tensor-mars research stack. This repository packages the final checkpoint, configuration, and reference model code.

    ## Training configuration
    ```yaml
    batch_size: 384

max_steps: 33333 warmup_steps: 200 lr: 0.0003 optimizer: Muon + AdamW dtype: bfloat16 grad_clip: 1.0 ```

    ## Data + tokenizer
    - Context length: 512 | Vocab size: 4096

    ## Metrics
    - **train_loss**: 3.9820

• val_loss: 3.9987

    ## Checkpoints
    - Latest checkpoint exported as `pytorch_model.bin`.
    - Full training log available in `metrics.jsonl`.
  
    ## Usage
    ```python
  

import torch from models.transformer import AttentionLM

checkpoint = torch.load("pytorch_model.bin", map_location="cpu") model = AttentionLM.from_config(json.load(open("config.json"))) model.load_state_dict(checkpoint["model_state_dict"]) model.eval()

        ## Limitations
        - This model is research-grade and not aligned for deployment.
        - Quadratic/bilinear attention stacks can exhibit instability outside the training distribution.