TinyLlama-1.1B-HXQ

3.99x smaller. +0.78% perplexity. The fidelity reference.

TinyLlama-1.1B compressed from 4.4 GB (FP32) to 1.03 GB with the tightest PPL delta in the lineup. No calibration data. No architecture-specific tuning. Just pip install and from_pretrained().

Install and Run

pip install "helix-substrate[hf]"

import helix_substrate  # registers the HXQ quantizer with HuggingFace
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("EchoLabs33/tinyllama-1.1b-helix")
tokenizer = AutoTokenizer.from_pretrained("EchoLabs33/tinyllama-1.1b-helix")

inputs = tokenizer("The meaning of life is", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

That's it. import helix_substrate registers the quantizer. from_pretrained() handles the rest automatically.

Benchmark

	Dense (FP32)	HXQ
Size	4.4 GB	1.03 GB
Perplexity (WikiText-2)	6.172	6.220 (+0.78%)
Compression ratio	—	3.99x
Compressed modules	—	154 HelixLinear + 1 nn.Linear (lm_head)
Architecture	LLaMA (22 layers, GQA)	unchanged

Eval: WikiText-2 test split, 2048 tokens, stride 512.

Good to Know

• GPU and CPU supported — runs on any CUDA GPU or CPU via standard PyTorch. Fused kernels for additional speedup are in progress.
• Fine-tunable via LoRA — compressed weights remain frozen, but LoRA adapters attach to each HelixLinear layer via HelixLinearSTE. See helix-substrate for training infrastructure.
• Requires helix-substrate — the quantizer is not built into transformers. You need pip install "helix-substrate[hf]".

What is HelixCode?

HelixCode is a universal weight compression codec based on vector quantization:

• Each weight matrix is replaced by a 256-entry codebook (float32) + uint8 index matrix + optional sidecar corrections for outlier values
• The compressed form is the executable — HelixLinear performs codebook[indices] @ x directly, no decompression step
• Works on any nn.Linear regardless of architecture (Transformer, Mamba, MLP, CNN)
• No calibration data required — unlike GPTQ/AWQ, codebooks are fit from the weights alone

How It Works

1. import helix_substrate registers the hxq quantizer with HuggingFace
2. from_pretrained() reads quantization_config.quant_method = "hxq" from config.json
3. The quantizer replaces 154 nn.Linear modules with HelixLinear shells before weight loading
4. Safetensors populates the codebook, indices, and sidecar buffers directly
5. The model runs in compressed form — no decompression needed

Why TinyLlama?

This is the fidelity benchmark — at +0.78% PPL, it demonstrates that HelixCode compression introduces negligible degradation on a well-studied reference model. TinyLlama's weights are well-conditioned (low kurtosis), making it the ideal validation target.

Compression Receipt

Compressed tensors:  156
Exact tensors (npy): 45   (norms, embeddings)
From original model: 44
Total keys:          753
Output size:         1,053 MB
Weight ratio:        3.99x
PPL delta:           +0.78% (6.220 vs 6.172 dense)
Eval: WikiText-2 test, 2048 tokens, stride=512

Companion Models

Same codec, same pip install, multiple architectures:

Model	Architecture	Ratio	PPL Delta
qwen2.5-14b-instruct-helix	Transformer	3.4x	pending
qwen2.5-7b-instruct-helix	Transformer	2.2x	+6.34%
qwen2.5-3b-instruct-helix	Transformer	1.6x	+0.69%
qwen2.5-coder-3b-helix	Transformer (code)	1.6x	+1.92%
qwen2.5-coder-1.5b-instruct-helix	Transformer (code)	2.4x	+1.63%
zamba2-2.7b-instruct-helix	Hybrid (Mamba2+Transformer)	1.8x	+6.59%
zamba2-1.2b-helix	Hybrid (Mamba2+Transformer)	1.7x	+2.90%
mamba2-1.3b-helix	Pure SSM (Mamba2)	2.1x	+8.0%
mamba-130m-helix	Pure SSM	3.8x	+18.4%

Citation

@software{helix_substrate_2026,
  title={Helix Substrate: Universal Weight Compression via HelixCode},
  author={EchoLabs},
  year={2026},
  url={https://github.com/echo313unfolding/helix-substrate}
}

License

Apache 2.0 (inherited from TinyLlama/TinyLlama-1.1B-Chat-v1.0).