Instructions to use EchoLabs33/zamba2-7b-instruct-hxq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EchoLabs33/zamba2-7b-instruct-hxq with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="EchoLabs33/zamba2-7b-instruct-hxq")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EchoLabs33/zamba2-7b-instruct-hxq")
model = AutoModelForCausalLM.from_pretrained("EchoLabs33/zamba2-7b-instruct-hxq")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EchoLabs33/zamba2-7b-instruct-hxq with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EchoLabs33/zamba2-7b-instruct-hxq"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EchoLabs33/zamba2-7b-instruct-hxq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EchoLabs33/zamba2-7b-instruct-hxq

SGLang

How to use EchoLabs33/zamba2-7b-instruct-hxq with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "EchoLabs33/zamba2-7b-instruct-hxq" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EchoLabs33/zamba2-7b-instruct-hxq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "EchoLabs33/zamba2-7b-instruct-hxq" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EchoLabs33/zamba2-7b-instruct-hxq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use EchoLabs33/zamba2-7b-instruct-hxq with Docker Model Runner:
```
docker model run hf.co/EchoLabs33/zamba2-7b-instruct-hxq
```

Zamba2-7B-Instruct-HXQ

2.0x smaller from BF16. 81-layer hybrid Mamba2+Transformer. Largest HXQ hybrid model.

Zamba2-7B-Instruct compressed from 14.7 GB (BF16) to 7.5 GB. 213 linear layers compressed, 573 exact tensors preserved. No calibration data. 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/zamba2-7b-instruct-hxq")
tokenizer = AutoTokenizer.from_pretrained("EchoLabs33/zamba2-7b-instruct-hxq")

inputs = tokenizer("Explain the theory of relativity in simple terms:", return_tensors="pt")
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.

Benchmark

	Dense (BF16)	HXQ
Size	14.7 GB	7.5 GB
Perplexity (WikiText-2)	pending	pending
Compression ratio	—	2.0x
Compressed modules	—	213 HelixLinear layers
Architecture	Zamba2 (81 layers, Mamba2 + shared Transformer)	unchanged

Good to Know

GPU recommended — 7.5 GB requires 10+ GB VRAM. Use device_map="auto" for multi-GPU.
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]".
Requires transformers >= 4.45 — for Zamba2 architecture support.
mamba-ssm recommended — without it, falls back to a slower sequential code path.
PPL pending — requires cloud GPU eval (model doesn't fit on 4 GB T2000).

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 213 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

Architecture Details

Zamba2-7B-Instruct is a hybrid architecture with:

81 total layers (Mamba2 + shared Transformer hybrid)
hidden_size=3584, attention_hidden_size=7168, 32 attention heads
mamba_d_state=64, mamba_d_conv=4
vocab_size=32000

213 linear layers compressed (162 Mamba projections, 38 attention/MLP, 26 LoRA adapters). Normalization layers, embeddings, conv1d, and Mamba-specific parameters (A_log, D, dt_bias) are stored at full precision.

Compression Receipt

Compressed modules:  213
Exact tensors:       573  (norms, embeddings, conv1d, A_log, D, dt_bias, LoRA)
Skip tensors:        243  (from original model)
Total keys:          1425
Dense size:          14.7 GB (BF16)
Compressed size:     7.5 GB
Compression ratio:   2.0x
PPL delta:           pending (cloud GPU eval)
Gate 1:              PASS (structural validation + SHA256)

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}
}

License

Apache 2.0 (inherited from Zyphra/Zamba2-7B-instruct).

Downloads last month: 10

Safetensors

Model size

13B params

Tensor type

I64

F32

BF16

F16