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wop/
Cosmos-T2-Accelerate-beta

Name: Cosmos-T2-Accelerate-beta
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Cosmos T2-Accelerate-beta

Universal Kaggle-ready training notebook for the Cosmos T2-Accelerate-beta series.

Notebook-generated card. Final metrics are filled after the Kaggle training run. This notebook is designed to stay Kaggle-friendly on 2x T4 GPUs. The goal is a reusable training recipe, not a production assistant.

Model Details


Model class	`CosmosT2_Accelerate_LLM`
Architecture	Decoder-only Transformer with RoPE, RMSNorm, SwiGLU, GQA, and a configurable Engram memory path
Parameters	`~5.03 M`
Layers	`6`
Attention heads	`2`
KV heads	`1`
d_model	`32`
FFN hidden	`256`
Positional encoding	RoPE (`rope_base=10000`)
Normalization	RMSNorm
MLP	SwiGLU
Memory	Engram (`use_engram=True`, every `2` blocks)
Context length	`1028`
Training block size	`1028`
Tokenizer	`Qwen/Qwen2.5-0.5B`
Dataset	`wop/XXXXXL-chain-of-thought`
License	Apache-2.0

Why these choices

RoPE keeps positional handling compact and avoids learned absolute embeddings.
RMSNorm is cheaper and more stable than LayerNorm for this small decoder-only model.
SwiGLU usually gives a better quality/compute tradeoff than a plain GELU MLP.
GQA reduces KV cost while keeping multi-head query capacity.
Engram gives the stack a lightweight explicit memory path for repeated reasoning patterns.
Dynamic isolated batching keeps conversations separate while padding and masking each batch on CPU.
KV-cache generation avoids recomputing the full prompt for every generated token in the app.

Training Summary

Metric	Value
Rows used	`100,000`
Loss tokens seen	`22,307,381`
Epochs	`2`
Batch size	`2`
Peak LR	`3.00e-04`
Weight decay	`0.1`
Gradient clipping	`1.0`
Wall-clock time	`1h 16m 27s`
Final training loss	`3.6952`
Final training perplexity	`40.25`
Final validation loss	`6.3866`
Final validation perplexity	`593.83`
Best validation loss	`4.0744`
Best epoch	`2`

Loss and perplexity

The notebook shows live loss and perplexity plots every 20 epochs and does not save the graph to disk.

How to Use

Quick start

python

import torch
from transformers import AutoTokenizer

from app import CosmosT2_Accelerate_LLM

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B")
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

ckpt = torch.load("$CHECKPOINT_NAME", map_location="cpu")
model = CosmosT2_Accelerate_LLM(**ckpt["config"])
model.load_state_dict(ckpt["model_state"])
model.eval()

prompt = tokenizer.apply_chat_template(
    [
        {"role": "system", "content": "Enable thinking features: INTUITION"},
        {"role": "user", "content": "What is 12 * 7?"},
    ],
    tokenize=False,
    add_generation_prompt=True,
)
ids = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids
out = model.generate(ids, max_new_tokens=120, temperature=0.8, top_k=50)
print(tokenizer.decode(out[0], skip_special_tokens=False))

Prompt format

Use the Qwen2.5 chat template. The default system prompt is:

Enable thinking features: INTUITION

The model will then emit a <think> block followed by an answer when it has enough signal.

The model is trained to end its turn with the <|im_end|> token (ChatML), so generation stops there. During data prep, any example longer than the 1028-token context has its <think> reasoning replaced by a short placeholder (or is dropped) so every training sequence ends cleanly - the model is never trained on a mid-thought truncation.

Limitations

The model is intentionally small and is still a research/demo artifact.
Training on chain-of-thought data can overfit quickly if the corpus is tiny.
Long-context behavior is limited by the configured block size.
The model is not safety-aligned and should not be exposed as a public assistant without additional work.

Intended Use

Research into small-scale pretraining and reasoning-style formatting
Educational demos for decoder-only Transformer training
Hugging Face Spaces or local inference demos
Not for production use

Cosmos T2-Accelerate-beta Series

This notebook is designed to train future Cosmos T2-Accelerate-beta variants by changing only the config block at the top.

Citation

bibtex

@misc{cosmos-t2,
  author    = {wop},
  title     = {Cosmos-T2: A small from-scratch chain-of-thought Transformer},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/wop/Cosmos-T2-Accelerate-beta}
}

Acknowledgements

Tokenizer from Qwen2.5 by Alibaba Cloud
Training data from wop/XXXXXL-chain-of-thought
Trained on Kaggle T4 GPUs