Qwen3.5-REAP-97B-A10B

OpenMOSE/Qwen3.5-REAP-97B-A10B

Vision–Language MoE model created by applying Router-weighted Expert Activation Pruning (REAP) to Qwen3.5-122B-A10B.

---

1. Model Summary

• Base model: Qwen/Qwen3.5-122B-A10B (vision–language MoE LLM)
• Variant name: Qwen3.5-REAP-97B-A10B
• Architecture: Decoder-only Transformer (hybrid linear/full attention) + MoE MLP experts, with vision encoder + VL fusion as in Qwen3.5
• Pruning method: REAP (Router-weighted Expert Activation Pruning) by Cerebras Research https://github.com/CerebrasResearch/reap
• Expert sparsity: ~22% of MoE experts pruned globally (256 → 200 experts)
• Active parameters: "A10B" indicates roughly ~10B active parameters per token (MoE sparse activation, 8 experts per token), while total parameters are reduced to about 97B
• Modality: Text + Vision (VL support kept intact)
• License: Apache 2.0
• Author / Maintainer: OpenMOSE
• Year: 2025

This is an unofficial community variant of Qwen3.5, not affiliated with or endorsed by Alibaba or Cerebras Systems.

---

2. What Is REAP and What Did We Change?

REAP (Router-weighted Expert Activation Pruning) is a pruning method for MoE models that uses:

• Router statistics (routing probabilities)
• Expert activation patterns on a calibration set

to identify under-used or redundant experts and prune them while preserving model quality as much as possible.

For this model:

• We applied REAP to Qwen3.5-122B-A10B across its MoE MLP blocks.
• ~22% of experts are pruned (256 → 200), based on router-weighted activation statistics.
• The routing mechanism itself is not conceptually changed; we only changed which experts remain.
• We extended the original REAP implementation to support the Qwen3.5 hybrid architecture (interleaved linear attention + full attention layers), so pruning can be applied without disrupting either attention pathway or VL functionality.

In short: same REAP algorithm, adapted to Qwen3.5's hybrid linear/full attention MoE architecture, leaving VL functionality available.

---

3. Calibration Data

The REAP pruning statistics were computed using:

• Calibration dataset: https://huggingface.co/datasets/OpenMOSE/reap-calib-mix

• This dataset is mostly synthetic, generated by Qwen3-235B-Instruct on mixed prompts designed to cover:

  • General instruction-following
  • Reasoning and long-form text

The calibration set is not used for additional fine-tuning; it is used solely to measure router/expert activations to decide which experts to prune.

---

4. Why 97B-A10B? (Motivation & Hardware Footprint)

Qwen3.5-122B-A10B is one of the most frontier-capable open-source models runnable at 96 GB, but it still exceeds what a typical 48 GB GPU setup can handle. By pruning ~22% of experts:

• The model shrinks from ~122B total parameters to about 97B total parameters.

• Sparse MoE activation keeps around 10B parameters active per token ("A10B"), same effective compute profile as the base model.

• In practice, this makes the model feasible to deploy on a 48 GB GPU with modest CPU offload:

  • Aggressive quantization (e.g., Q4) eliminates the need for offloading on 96 GB setups.
  • 48 GB configurations can work with partial layer offloading to CPU.

The overarching goal is to bring the closest OSS approximation to a frontier model into local deployment, making Qwen3.5-122B-A10B accessible without requiring multi-GPU nodes.

---

5. Architecture Notes

Key architectural properties inherited from the base model and preserved after pruning:

The hybrid attention design (linear attention layers interleaved with full attention every 4 layers) is a distinctive feature of the Qwen3.5 family and is retained fully after pruning.

---

6. Intended Use

Primary intended uses

• Research on:

  • MoE pruning and compression (especially REAP) applied to hybrid attention architectures
  • Scaling behavior of pruned MoE VL models under conservative pruning ratios (~22%)
  • Trade-offs between expert sparsity and performance in linear-attention hybrids

• Experimental deployment for:

  • Vision–language assistants on constrained hardware
  • Multimodal chatbots
  • Document + image understanding

Suitable tasks (examples)

• Multimodal chat (image + text → text)
• Image captioning / description
• Visual question answering
• General instruction-following and long-form text generation
• Long-context reasoning (up to 262K tokens)

Out-of-scope / high-risk uses

This model should not be used without additional safeguards for:

• Medical, legal, or financial advice
• Safety-critical decision making
• Political persuasion or targeted disinformation
• Any scenario where incorrect or biased outputs can cause real-world harm

---

7. Limitations & Risks

This model inherits all the limitations of Qwen3.5-122B-A10B plus those introduced by pruning:

• Hallucinations: The model can generate plausible but incorrect facts.

• Bias & toxicity: Biases from the original training data and synthetic calibration data remain and may be amplified.

• Distribution shift from pruning:

  • Some long-tail behaviors or rare domain knowledge may degrade due to removal of 56 experts.
  • Performance may be uneven across tasks or languages underrepresented in the calibration set.

• Multimodal edge cases:

  • Complex compositional visual reasoning or high-resolution images may not work reliably.
  • VL behavior is preserved but not re-tuned after pruning.

• Hybrid attention sensitivity:

  • Linear attention layers are more sensitive to expert distribution changes than standard full attention; this is a known risk with the Qwen3.5 architecture.

Users should perform their own evaluation before relying on the model in any sensitive context.

---

8. How to Use

Note: Requires transformers >= 4.57.0.dev0 for Qwen3.5 MoE support (Qwen3_5MoeForConditionalGeneration).

import torch
from transformers import AutoProcessor
from transformers.models.qwen3_5_moe import Qwen3_5MoeForConditionalGeneration

model_id = "OpenMOSE/Qwen3.5-REAP-97B-A10B"


# default: Load the model on the available device(s)
model = Qwen3_5MoeForConditionalGeneration.from_pretrained(
    model_id, dtype="auto", device_map="auto"#, use_cache = False
)


processor = AutoProcessor.from_pretrained(model_id)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
            },
            {"type": "text", "text": "Describe the image."},
        ],
    }
]

# Preparation for inference
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
)

print(inputs)

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=256,do_sample=True,temperature=0.7,     # ← 1.0より小さいほど保守的
    top_p=0.9)          # or top_k=50 など)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text[0])

For text-only usage, omit the images= argument.

llama.cpp / GGUF

For 48 GB GPU + CPU offload configurations, GGUF quantized versions (e.g., Q4_K_M) are recommended. Example offload configuration:

./llama-cli -m qwen3.5-reap-97b-a10b.Q4_K_M.gguf \
  --n-gpu-layers 38 \   # tune based on your VRAM
  --ctx-size 8192

---

9. Evaluation (Status)

• This release focuses on making the REAP-pruned model available for the community.

• Quantitative benchmarks (e.g., MMLU, GSM8K, MMBench) are still work in progress.

• Early qualitative checks show:

  • VL behavior is preserved after pruning at this sparsity level.
  • Latency and memory usage are meaningfully reduced compared to Qwen3.5-122B-A10B, enabling 48 GB + CPU-offload deployments.
  • The conservative 22% pruning ratio appears to cause less degradation than more aggressive pruning schedules.

Community contributions with detailed benchmarks are very welcome.

---

10. Training & Distillation Details (High-Level)

• Base model: Qwen/Qwen3.5-122B-A10B

• Pruning method: REAP (Router-weighted Expert Activation Pruning)

• Experts: 256 → 200 (22% pruned)

• Calibration data: OpenMOSE/reap-calib-mix (mostly generated by Qwen3-235B-Instruct)

• Post-processing:

  • Router / gating structure retained
  • Experts pruned according to REAP scoring
  • No additional large-scale pretraining in this release

Future versions may include post-pruning fine-tuning or knowledge distillation from the full 122B model to recover further performance.

---

11. Community & Contribution

Let's grow this model together as a community.

You are encouraged to:

• Run benchmarks and publish results

• Contribute scripts for:

  • Further pruning experiments
  • Quantization (GGUF, AWQ, GPTQ)
  • Long-context or domain-specific fine-tuning
  • CPU/GPU offload configuration guides for various hardware setups

• Report issues or findings about failure modes, biases, or surprising behaviors

---

12. License

• Model & code (this repository): Apache License 2.0
• The original Qwen3.5-122B-A10B model and any downstream use must also respect their respective licenses and usage terms.

---

13. Acknowledgements

This architecture research and implementation was made possible with computing power and technical support from Recursal AI. We sincerely thank them for enabling this work.

https://featherless.ai/

• Qwen team for building the Qwen3.5 family of models.
• Cerebras Research for the REAP method and reference implementation: https://github.com/CerebrasResearch/reap
• OpenMOSE community for experimentation, engineering, and calibration data generation.

---

2025 OpenMOSE