Instructions to use groxaxo/Qwen3.6-35B-A3B-Heretic-REAP-0.30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use groxaxo/Qwen3.6-35B-A3B-Heretic-REAP-0.30 with Transformers:

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

pipe = pipeline("text-generation", model="groxaxo/Qwen3.6-35B-A3B-Heretic-REAP-0.30")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("groxaxo/Qwen3.6-35B-A3B-Heretic-REAP-0.30")
model = AutoModelForCausalLM.from_pretrained("groxaxo/Qwen3.6-35B-A3B-Heretic-REAP-0.30", device_map="auto")
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 groxaxo/Qwen3.6-35B-A3B-Heretic-REAP-0.30 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "groxaxo/Qwen3.6-35B-A3B-Heretic-REAP-0.30"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "groxaxo/Qwen3.6-35B-A3B-Heretic-REAP-0.30",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/groxaxo/Qwen3.6-35B-A3B-Heretic-REAP-0.30

SGLang

How to use groxaxo/Qwen3.6-35B-A3B-Heretic-REAP-0.30 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 "groxaxo/Qwen3.6-35B-A3B-Heretic-REAP-0.30" \
    --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": "groxaxo/Qwen3.6-35B-A3B-Heretic-REAP-0.30",
		"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 "groxaxo/Qwen3.6-35B-A3B-Heretic-REAP-0.30" \
        --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": "groxaxo/Qwen3.6-35B-A3B-Heretic-REAP-0.30",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use groxaxo/Qwen3.6-35B-A3B-Heretic-REAP-0.30 with Docker Model Runner:
```
docker model run hf.co/groxaxo/Qwen3.6-35B-A3B-Heretic-REAP-0.30
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3.6-35B-A3B-Heretic — REAP-pruned (30%, 256→180 experts)

This is a REAP (Router-weighted Expert Activation Pruning) compressed version of a Qwen3.5/3.6-35B-A3B hybrid linear-attention MoE (Heretic variant). 30% of the routed experts were pruned (256 → 180 per layer) using the Cerebras REAP saliency criterion.

	Base	This model
Routed experts / layer	256	180
Active experts / token	8	8
Decoder layers	40 (30 linear-attn + 10 full-attn)	same
Hidden size	2048	2048
Size (BF16)	~70 GB	~50 GB

Pruning was performed with the layer-wise (block-wise) calibration observer on a c4 calibration set, reap saliency, seed 42. The model is text-only (the multimodal vision tower of the base Omni model was dropped during pruning).

Evaluation

Measured with lm-evaluation-harness against this model served on vLLM (pipeline-parallel, BF16):

Benchmark	Setup	Score
MMLU	5-shot, acc	73.25%
– STEM		69.11%
– Humanities		67.46%
– Social Sciences		82.97%
– Other		76.60%
HellaSwag	0-shot, acc_norm	82.55%
HellaSwag	0-shot, acc	63.06%

(30% of experts pruned; these scores indicate the model retains strong general knowledge and commonsense after compression.)

Architecture note (hybrid linear attention)

This is a hybrid model: 30/40 layers use Gated-DeltaNet linear attention (Mamba-style recurrent conv+SSM state) and 10/40 use full attention. Two consequences:

Hugging Face transformers: loads and generates correctly on a single device. Do not use device_map="auto" to split it across multiple GPUs — splitting the recurrent linear-attention state across devices produces garbage. Use one GPU (if it fits) or CPU.
vLLM: serve with pipeline parallelism across GPUs (--pipeline-parallel-size N --tensor-parallel-size 1). vLLM provides its own GatedDeltaNet kernels.

Method

REAP selects experts to prune that contribute minimally to the layer output by considering both the router gate-values and the average activation norm of the experts, preserving the router's independent control over the remaining experts (avoiding the functional-subspace collapse of expert merging).

Reproduce with cerebras/reap.

Downloads last month: 85

Safetensors

Model size

25B params

Tensor type

BF16

Model tree for groxaxo/Qwen3.6-35B-A3B-Heretic-REAP-0.30

Quantizations

1 model

Paper for groxaxo/Qwen3.6-35B-A3B-Heretic-REAP-0.30

REAP the Experts: Why Pruning Prevails for One-Shot MoE compression

Paper • 2510.13999 • Published Oct 15, 2025 • 20