KyleHessling1/Qwopus-GLM-18B-Merged-GGUF

Qwopus-GLM-18B-Merged (Healed)

A 64-layer frankenmerge of two of Jackrong's incredible Qwen3.5-9B finetunes, stacking all 32 layers from each to create an ~18B parameter model, then healed with a 1000-step QLoRA fine-tune to smooth the layer boundary.

This was a fun experiment! A lot of people have been asking for something between Jackrong's 27B and 9B models — something that runs well on 12–16 GB GPUs. This frankenmerge is an attempt at filling that gap, and the results are surprisingly good.

Heal Fine-Tune — It Works

The raw frankenmerge had a known issue: garbled code output. Because two separately-trained models were stacked at layer 32, structured output (code blocks, HTML, bracket matching) would occasionally come out malformed or hallucinated.

We ran a 1000-step QLoRA heal fine-tune using Jackrong's own training data to let gradients flow across the layer boundary — and the results are significant:

• HTML generation is now clean and production-quality. We tested a complex single-page weather dashboard (navbar, dark mode toggle, 5-day forecast grid, responsive sidebar, CSS variables, JavaScript) — the model produced 14,500+ chars of valid HTML/CSS/JS with perfectly balanced CSS braces, perfectly balanced JS parentheses, no garbled text, and a complete </html> closure.
• Programming benchmark improved: 11/15 (raw) -> 12/15 (healed), recovering the longest_substring sliding-window algorithm test (8/8 cases passing)
• Overall score improved: 39/44 -> 40/44 (90.9%), still comfortably beating Qwen 3.6 MoE (38/44) at less than half the VRAM
• Loss dropped 39% during training (1.02 -> 0.62), confirming the layer boundary was a real source of error that training could address

The healed GGUF (Qwopus-GLM-18B-Healed-Q4_K_M.gguf) is the only version in this repo. If you're interested in the raw unhealed merge for research purposes, reach out.

This is still an experimental model — it may have quirks or issues. If you run into anything weird, or if you make something cool with it, reach out on X: @KyleHessling1

Benchmark Results

We ran a 44-test capability suite covering basic generation, reasoning, tool calling, agentic workflows, structured output, context handling, multilingual, programming, and performance.

The healed merge outperforms the brand new Qwen 3.6-35B-A3B MoE (Q4_K_M, 22 GB) despite being significantly smaller (Q4_K_M, 9.2 GB):

Category	Qwopus 9B (source)	Qwopus-GLM-18B (healed)	Qwen 3.6-35B MoE
Basic	6/6	6/6	5/6
Reasoning	4/4	4/4	4/4
Tool Calling	6/6	6/6	6/6
Agentic	4/4	4/4	4/4
Structured Output	2/2	2/2	2/2
Context	2/3	2/3	2/3
Multilingual	2/2	2/2	2/2
Programming	13/15	12/15	12/15
Performance	2/2	2/2	1/2
TOTAL	41/44 (93.2%)	40/44 (90.9%)	38/44 (86.4%)
Throughput	126.0 tok/s	66.0 tok/s	174.2 tok/s
GGUF Size	5.3 GB	9.2 GB	22 GB

Key Takeaways

• 40/44 tests passed (90.9% healed) — beats Qwen 3.6 MoE's 38/44 (86.4%) at less than half the VRAM
• Heal training recovered programming capability: 11/15 raw -> 12/15 healed (matching Qwen 3.6 MoE)
• Perfect tool calling (6/6) — single calls, optional params, tool selection, complex params, response handling
• Perfect agentic reasoning (4/4) — plan generation, multi-step tool workflows, error recovery, self-correction
• Highest Chinese output density of any model tested: 129-138 CJK characters
• ~66 tok/s with low throughput variance — stable inference
• Fits in 12 GB VRAM at Q4_K_M — runs on consumer GPUs like RTX 3060/4070

Heal Fine-Tune Details

The raw frankenmerge had code formatting issues (garbled code blocks, missing brackets). We ran a 1000-step QLoRA heal fine-tune using Jackrong's training data to smooth the layer-32 boundary:

• Method: QLoRA (4-bit NF4), LoRA rank 64, targeting all attention + MLP projections
• Data: Blend of Jackrong/Qwen3.5-reasoning-700x (70%), Jackrong/Competitive-Programming-python-blend (15%), Jackrong/MultiReason-ChatAlpaca (15%)
• Training: 1000 steps, batch 8, lr 2e-5 cosine, ~14 hours on RTX 5090
• Loss: 1.02 -> 0.62 (39% reduction)
• Result: Recovered 1 programming test, HTML/CSS output is now clean and production-quality

Where It Falls Short

Three programming tests still fail on the healed version: one function naming issue, one missing JS paren, and one that doesn't produce a code block for pytest generation. These are residual formatting artifacts from the merge.

Frontend Code Generation — Stress Test Results

We put the healed model through a rigorous frontend stress test: 6 increasingly complex HTML/CSS/JS generation tasks, each requiring thousands of tokens of structurally valid code output. The results speak for themselves:

Test	What We Asked For	Checks Passed	Output Size
Weather Dashboard	Responsive dashboard, CSS vars, dark mode toggle, 5-day forecast grid	9/9	14.5K chars
E-Commerce Product Page	Image gallery, color swatches, quantity selector, tabbed content, sticky mobile bar	12/12	16.7K chars
Animated SaaS Landing	Moving gradient, typing animation, IntersectionObserver scroll reveals, auto-rotating testimonial carousel, 3 pricing tiers	13/13	24.1K chars
Analytics Dashboard	SVG bar chart with tooltips, SVG donut chart, sortable data table, collapsible sidebar, dark theme	13/13	22.3K chars
Multi-Step Registration	3-step form wizard, real-time validation, password strength meter, state dropdown, animated transitions, success modal	12/12	23.3K chars
Snake Game	Canvas game loop, arrow key controls, collision detection, localStorage high score, increasing difficulty	11/12	11.2K chars

62/63 total checks passed (98.4%)

Every single output had:

• Perfectly balanced CSS braces (zero imbalance across all 6 files)
• Perfectly balanced JS parentheses (zero imbalance across all 6 files)
• Zero garbled or hallucinated text
• Working JavaScript — dark mode toggles, IntersectionObserver animations, SVG chart rendering, form validation, canvas game loops

The only miss: the Snake game had a minor closing tag typo (html> instead of </html>) at the very end.

This is remarkable for a frankenmerge of two 9B models with only 1000 steps of QLoRA healing. The model is producing production-quality frontend code — not just syntactically valid HTML, but sophisticated interactive applications with modern CSS (Grid, Flexbox, custom properties, keyframe animations) and non-trivial JavaScript (IntersectionObserver, requestAnimationFrame game loops, real-time form validation, SVG chart generation).

All 6 sample HTML files are included in the samples/ directory of this repo — download them and open in a browser to see for yourself.

Architecture

Property	Value
Total Layers	64 (32 + 32)
Total Parameters	~18B
Hidden Size	4096
Attention Heads	16 (4 KV heads, GQA)
Intermediate Size	12288
Context Length	262,144 tokens
Attention Type	Hybrid (linear + full, every 4th layer)
GGUF Q4_K_M Size	9.2 GB

Layer Composition

Layers  0–31:  Jackrong/Qwopus3.5-9B-v3.5         (Opus reasoning distill)
Layers 32–63:  Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1  (GLM-5.1 reasoning distill)

Embeddings, LM head, MTP, vision encoder: from Qwopus3.5-9B-v3.5

Source Models

All credit for the source models goes to Jackrong, who created both of these excellent finetunes. I just stacked the layers — the quality comes from his work.

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

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

Key insight from the v3.5 design: "Scaling high-quality SFT data may further enhance the generalization ability of large language models." Reasoning SFT helps models better utilize existing knowledge and activate latent knowledge through structured reasoning, rather than simply memorizing long Chain-of-Thought outputs.

Performance highlights (27B line reference):

• MMLU-Pro: 90.36% accuracy (+1.07% over v3)
• Agentic coding tests: 43/44 passed (97.7%)

Resources:

• Model Card
• Fine-Tuning Guide (PDF)
• 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 of Qwen3.5-reasoning-700x). Focused on structured reasoning ability, instruction-following consistency, and problem decomposition.

The model learns a structured reasoning scaffold: understand the task, break down the problem, reason step by step, then construct the final answer.

Training data:

• Primary: Jackrong/GLM-5.1-Reasoning-1M-Cleaned (cleaned from Kassadin88/GLM-5.1-1000000x)
• Auxiliary: Jackrong/Qwen3.5-reasoning-700x

Resources:

• Model Card
• Fine-Tuning Guide (PDF)

Why This Works

By stacking two differently-distilled reasoning models, this merge combines:

1. Qwopus v3.5's strengths in agentic tool use, code generation, and token-efficient reasoning (Opus-style training)
2. GLM-5.1 Distill's strengths in structured problem decomposition, instruction adherence, and chain-of-thought organization (GLM-style reasoning scaffold)

The hypothesis: deeper networks with diverse reasoning training produce more robust, capable models — and the benchmark results suggest it works, at least for the capabilities we tested.

Merge Details

• Method: Passthrough frankenmerge (layer stacking)
• Tool: Custom script (mergekit did not support Qwen3.5's hybrid linear/full attention architecture)
• Embeddings / LM Head / Visual / MTP: From Qwopus3.5-9B-v3.5
• Precision: BF16 -> Q4_K_M GGUF
• No additional training was performed

Usage

With llama.cpp (recommended)

llama-server \
    -m Qwopus-GLM-18B-Merged-Q4_K_M.gguf \
    --chat-template-file your-qwen35-template.jinja \
    --ctx-size 65536 \
    --flash-attn on \
    --n-gpu-layers 99

With Transformers

The full BF16 safetensors are not included in this GGUF repo. If you need them for further fine-tuning or experimentation, reach out on X.

Limitations

• Experimental frankenmerge — no additional training was done. Layer boundaries between the two source models may cause occasional coherence or formatting issues.
• Code formatting — the model sometimes garbles fenced code blocks (returns code without proper markdown fencing). The reasoning is usually correct even when the formatting isn't.
• Not exhaustively tested — this was a fun weekend project. There may be edge cases we haven't found yet.
• Hallucination risk — as with all autoregressive LLMs, outputs may contain factual errors.

Supported Research

Both source models reference: Ren et al., 2026 — Rethinking Generalization in Reasoning SFT (arXiv:2604.06628)

Key findings: reasoning SFT generalizes when sufficiently trained; high-quality long-CoT data enables cross-domain transfer; stronger models learn reasoning structure, not just longer outputs.

Acknowledgements

• Jackrong — the real MVP. Both source models, training pipelines, datasets, and documentation are his work. This merge exists because his finetunes are so good that even stacking them naively produces something surprisingly capable.
• Qwen for the excellent Qwen3.5-9B base model
• Unsloth AI for efficient fine-tuning infrastructure
• GLM-5.1 team for the teacher model used in distillation
• Kassadin88 for the original GLM-5.1-1000000x dataset
• The broader open-source community

Questions?

This was just for fun — reach out on X if you have questions, find issues, or build something cool with it!

@KyleHessling1

Citations

@misc{jackrong_qwopus35_9b_v35,
  title  = {Qwopus3.5-9B-v3.5},
  author = {Jackrong},
  year   = {2026},
  publisher = {Hugging Face}
}

@misc{jackrong_qwen35_9b_glm51_distill_v1,
  title  = {Qwen3.5-9B-GLM5.1-Distill-v1},
  author = {Jackrong},
  year   = {2026},
  publisher = {Hugging Face}
}