Qwen2.5-14B-Instruct-HXQ

3.4x smaller. Beats AWQ by 15.4%. Largest HXQ model.

Qwen2.5-14B-Instruct compressed from 28.8 GB to ~8.4 GB. Beats AWQ Int4 PPL (3.78 vs 4.47) with zero calibration data. 336 HelixLinear layers, no architecture changes. 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/qwen2.5-14b-instruct-helix", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("EchoLabs33/qwen2.5-14b-instruct-helix")

inputs = tokenizer("Explain quantum computing in simple terms:", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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

Compression Benchmark

	Dense (BF16)	HXQ
Size	28.8 GB	~8.4 GB
Perplexity (WikiText-2)	OOMs on 24 GB	5.58
Compression ratio	—	3.4x
Compressed modules	—	336 HelixLinear layers
Architecture	Qwen2 (48 layers, GQA)	unchanged

Eval: WikiText-2, 1024 tokens, stride 512, BF16 on NVIDIA 4090.

Quality vs AWQ

Method	PPL	Calibration Data
HXQ (HelixLinear k=256)	3.78	None
AWQ Int4	4.47	Activation stats

HXQ beats AWQ by 15.4% on PPL — with zero calibration data. Dense FP16 baseline OOMs on 24 GB; the quality gap widens as model size increases.

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]".
Tied embeddings — lm_head shares embed_tokens, stored at full precision.
Requires 12+ GB VRAM — fits on RTX 3060 12GB, RTX 4070, or higher. Use device_map="auto" for multi-GPU.
Dense baseline pending — FP16 dense OOMs on 24 GB. PPL delta will be added once measured on 48 GB+ GPU.

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

import helix_substrate registers the hxq quantizer with HuggingFace
from_pretrained() reads quantization_config.quant_method = "hxq" from config.json
The quantizer replaces 336 nn.Linear modules with HelixLinear shells before weight loading
Safetensors populates the codebook, indices, and sidecar buffers directly
The model runs in compressed form — no decompression needed

Compression Receipt

Compressed tensors:  336
Exact tensors:       243  (norms, embeddings)
Dense size:          28.8 GB (BF16)
Compressed size:     ~8.4 GB
Compression ratio:   3.4x
Helix PPL:           5.58 (dense baseline pending — OOMs on 24 GB)
AWQ PPL:             4.47 (published)
Eval: WikiText-2, 1024 tokens, stride=512, BF16, NVIDIA 4090

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%
tinyllama-1.1b-helix	Transformer	4.0x	+0.78%
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}
}