Qwopus-GLM-9B-DualReason-Distilled

A DARE-TIES weight merge of two specialized Qwen3.5-9B fine-tunes, combining Opus-style agentic reasoning with GLM-5.1 structured chain-of-thought into a single 9B model.

This model is the result of an extensive research process that explored ANN-based layer routing before converging on DARE-TIES as the optimal merging strategy. The full methodology and findings are documented below.

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Benchmark Results

⏳ Evaluation in progress — Results will be updated shortly.

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Model Details

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Available Quantizations

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Usage

With llama.cpp (recommended)

llama-server \
 -m Qwopus-GLM-9B-DualReason-Distilled-Q4_K_M.gguf \
 --ctx-size 32768 \
 --flash-attn on \
 --n-gpu-layers 99

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
 "rico03/Qwopus-GLM-9B-DualReason-Distilled",
 dtype=torch.bfloat16,
 device_map="cuda",
 trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
 "rico03/Qwopus-GLM-9B-DualReason-Distilled",
 trust_remote_code=True,
)

messages = [{"role": "user", "content": "Solve step by step: 2x + 5 = 13"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to("cuda")

with torch.no_grad():
 output = model.generate(**inputs, max_new_tokens=512, do_sample=False)
print(tokenizer.decode(output[0], skip_special_tokens=True))

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Source Models

All credit for the source models goes to Jackrong.

Jackrong/Qwopus3.5-9B-v3.5

A reasoning-enhanced fine-tune of Qwen3.5-9B trained with ~2x more SFT data than v3, focused on structured reasoning, tool-augmented workflows, and multi-step agentic tasks.

• HumanEval pass@1: 87.80%
• MMLU-Pro: 90.36%
• Training guide: GitHub

Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1

A distilled variant of Qwen3.5-9B trained on high-quality reasoning data from a GLM-5.1 teacher model (~700x scale). Focused on structured reasoning, instruction-following, and problem decomposition.

• Training data: Jackrong/GLM-5.1-Reasoning-1M-Cleaned

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Why DARE-TIES and Not a Frankenmerge

This model went through an extensive research process before arriving at DARE-TIES.

Step 1 — ANN Router (abandoned)

We designed a lightweight ANN router (~1M parameters) to learn optimal layer selection between the two models:

Input hidden states (both models)
 ↓
LayerRouter MLP: Linear(8320,1024) → SiLU → LayerNorm → Dropout → Linear(1024,256) → SiLU → LayerNorm → Linear(256,1)
 ↓
α ∈ [0,1] per layer → mixed = α × h_qwopus + (1-α) × h_glm

Trained on 12,297 examples from GSM8K, MMLU, ARC-Challenge, HumanEval, and IFEval with entropy regularization. Result: the router consistently collapsed to always selecting one model regardless of training objective — entropy loss or cross-entropy.

Step 2 — Cosine Similarity Analysis

To understand why, we computed cosine similarity between hidden states of both models across all 32 layers on 12,297 examples:

Conclusion: Cosine similarity of 0.992–1.000 across all layers means the two models produce nearly identical hidden states. They differ in their weights, not in the structure of their internal representations. Layer-by-layer selection adds no value — the optimal combination must happen at the weight level.

Step 3 — DARE-TIES (adopted)

DARE-TIES operates directly on model weights, interpolating the specialized knowledge of both models continuously:

tv_g = weights_glm - weights_qwopus # task vector
mask = torch.rand_like(tv_g) < 0.5 # DARE dropout
tv_g = tv_g * mask / 0.5
sign = tv_g.sign() # TIES sign resolution
tv_g = tv_g * (tv_g.sign() == sign).float()
merged = weights_qwopus + 0.5 * tv_g # final merge

Unlike Jackrong's frankenmerge which makes a binary choice per layer, DARE-TIES produces a continuous interpolation of both models' knowledge at every weight.

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Why No DPO/SFT

We explored DPO post-training but abandoned it after careful analysis. The primary dataset (GLM-5.1-Reasoning-1M-Cleaned) has a median chosen length of 3,225 tokens. With max_length=512, 95.8% of examples get truncated — the chosen response (with full thinking) gets cut short while the rejected (without thinking) remains intact, inverting the DPO signal. This was confirmed empirically: loss dropped from 0.51 to 0.069 in 20 steps (false convergence from corrupted signal).

Correct DPO for this class of model requires max_length ≥ 4,096 and complete long-CoT pairs — addressed in a future version.

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References

• Rethinking Generalization in Reasoning SFT (Ren et al., 2026) — arXiv:2604.06628
• DARE — Language Model Merging by Uncertainty-Based Model Pruning
• TIES — Resolving Interference When Merging Models
• Jackrong's fine-tuning guide — GitHub

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Acknowledgements

• Jackrong — both source models, training pipelines, datasets, and documentation
• Qwen team — Qwen3.5-9B base model
• GLM-5.1 team — teacher model used in distillation
• Kassadin88 — original GLM-5.1-1000000x dataset
• KyleHessling1 — Qwopus-GLM-18B-Merged reference benchmark

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Citation

@misc{rico03_qwopus_glm_dualreason,
 title = {Qwopus-GLM-9B-DualReason-Distilled},
 author = {rico03},
 year = {2026},
 publisher = {Hugging Face},
 url = {https://huggingface.co/rico03/Qwopus-GLM-9B-DualReason-Distilled}
}

Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1

Jackrong/Qwopus3.5-9B-v3.5