You can deploy this model and start earning money today!

anonx3247/
llm-trm-pretraining

Name: llm-trm-pretraining
Availability: InStock

Views

No views yet

LLM-TRM Sequence TRM

Trained TRM for sequence-to-sequence reasoning (Phase 2).

Model Details

Architecture: Tiny Recursive Network with transformer blocks
Compressed dimension: 256
Layers: 2
Heads: 8
Latent steps (n): 6
Deep recursions (T): 3

Training Metrics

Run summary:
wandb:         epoch/best_loss 0.09876
wandb: epoch/cosine_similarity 0.97987
wandb:              epoch/loss 0.09891
wandb:     train/avg_halt_prob 0.99924
wandb: train/cosine_similarity 0.97987
wandb:         train/halt_loss 0.00076
wandb:              train/loss 0.09891
wandb:                train/lr 0.0
wandb:               train/mse 0.09853
wandb:    train/relative_error 0.24823

Usage

python

import torch
from huggingface_hub import hf_hub_download
from src.train.phase2_trm import SequenceTRM

# Download and load
checkpoint_path = hf_hub_download(repo_id="anonx3247/llm-trm-pretraining", filename="trm.pt")
checkpoint = torch.load(checkpoint_path, map_location="cpu")

# Initialize TRM
trm = SequenceTRM(
    d_compressed=256,
    n_layers=2,
    n_heads=8,
)
trm.load_state_dict(checkpoint["trm_state_dict"])

# Use: takes [B, L, D'] context, outputs [B, L+1, D']
compressed_hidden = ...  # [B, L, 256]
output = trm(compressed_hidden, n_steps=4)  # [B, L+1, 256]
reasoning_result = output[:, -1, :]  # [B, 256]

Part of LLM-TRM

This TRM is part of the LLM-TRM project for integrating Tiny Recursive Models with language models.