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BrainChip-AI/
qkv-fineweb-300m

Name: qkv-fineweb-300m
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Do Transformers Need Three Projections? — LLM checkpoint (Q≠K=V, 300M)

A 300M-parameter GPT-style language model trained from scratch with the Q≠K=V attention variant from the ICML 2026 paper "Do Transformers Need Three Projections? A Systematic Study of QKV Variants" (Kayyam, Madan Gopal, Lewis; BrainChip Inc.) — separate query projection, shared key/value (V = K, no value weights at all). Only K needs to be cached during autoregressive generation, halving KV-cache memory versus standard attention.

This card covers the language-modeling result. The paper's original synthetic-task and vision-classification checkpoints are on a separate card.

⚠️ Scope of this checkpoint — please read

This is not the paper's official SlimPajama LM run described in the code repository README (that one reports "~3% perplexity degradation" vs. a matched QKV baseline). This checkpoint is a later, independent reproduction on a different dataset, with only the Q≠K=V variant trained — there is no matched QKV/K/KV baseline run under the same conditions to compare against. Do not read the numbers below as reproducing or contradicting the paper's headline claim.

Dataset: FineWeb-Edu (sample-10BT), not SlimPajama — SlimPajama was no longer resolvable on the HF Hub at training time.
Training script: an updated copy of transformer_KV_1_300_M.py (class GPT_QKV_KEqualsV) with a padding-loss masking fix (short documents were previously scored on their padding). The model architecture is identical to the file linked above; only the training loop's loss masking changed. The fixed training script itself was not preserved outside the training instance.

Results

Single GPU, 10B tokens (FineWeb-Edu sample-10BT), 33,908 steps, ~45 hours.

Metric	Value
Final validation loss	3.058
Final validation perplexity	21.28
Bits per character	4.41

Validation PPL every 500 steps follows a standard power-law decay (281.7 → 21.28 across the run).

Generation quality caveat: sample generations use temperature=0.8, top_k=50 with no seed and no repetition penalty — outputs vary run-to-run and can fall into repetition loops. Treat generated text as illustrative only; validation perplexity is the reliable metric here.

What's included

File	Description
`checkpoints/qkv_keqv_300m_fineweb_edu.pt`	Final model weights only (no optimizer state), `{step, epoch, tokens_seen, model_state_dict, val_metrics, model_config, train_config}`
`checkpoints/model_config.yaml`	Architecture hyperparameters
`checkpoints/train_config.yaml`	Optimizer / schedule hyperparameters

Model configuration

yaml

n_layer: 20
n_embd: 1024
n_head: 16
n_inner: 4096
n_positions: 2048
vocab_size: 50304
tie_word_embeddings: true

284.5M parameters (checkpoint metadata rounds this to "300M" after the paper's naming convention for this scale tier). Tokenizer: GPT-2 (AutoTokenizer.from_pretrained("gpt2")).

Usage

The model class lives in the paper's code repository (not bundled in this HF repo):

bash

git clone https://github.com/Brainchip-Inc/Do-Transformers-Need-3-Projections
pip install torch huggingface_hub

python

import torch
from huggingface_hub import hf_hub_download
from transformer_KV_1_300_M import ModelConfig, GPT_QKV_KEqualsV

path = hf_hub_download(
    "BrainChip-AI/qkv-fineweb-300m",
    "checkpoints/qkv_keqv_300m_fineweb_edu.pt",
)
ckpt = torch.load(path, map_location="cpu", weights_only=False)

model = GPT_QKV_KEqualsV(ModelConfig(**ckpt["model_config"]))
model.load_state_dict(ckpt["model_state_dict"])
model.eval()

print(ckpt["val_metrics"])  # {'loss': 3.058, 'perplexity': 21.28, 'bpc': 4.41}

Citation

bibtex

@inproceedings{kayyam2026qkv,
  title={Do Transformers Need Three Projections? A Systematic Study of {QKV} Variants},
  author={Kayyam, Ali and Madan Gopal, Anusha and Lewis, M Anthony},
  booktitle={Proceedings of the 43rd International Conference on Machine Learning (ICML)},
  year={2026},
  series={PMLR},
  volume={306}
}

License

MIT (see the code repository).