What license applies to Jackrong/Qwopus3.6-35B-A3B-Coder-MTP-GGUF?

License: apache-2.0. Verify terms on Hugging Face before commercial use.

How do I run Jackrong/Qwopus3.6-35B-A3B-Coder-MTP-GGUF locally?

Download a GGUF file from this page and load it in guIDE or llama.cpp. Pipeline task: image-text-to-text.

Model Intelligence Sheet

Jackrong/Qwopus3.6-35B-A3B-Coder-MTP-GGUF overview

Q: How much VRAM or disk space does Jackrong/Qwopus3.6-35B-A3B-Coder-MTP-GGUF need?

Runs locally from ~1.66 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

<div style="font family: apple system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans serif; border: 1px solid bae6fd; border radius: 18px; box shadow: 0 18px 40…

transformersggufllama.cppimage-text-to-textvisionmultimodaltext-generation-inferenceunslothconversationalqwen3_6moecoderagenttool-usefunction-callingthinking-offlong-contextlorasftlogicmathenzhes

Runs locally from ~1.66 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

Downloads

159,871

Likes

168

Pipeline

image-text-to-text

Author

Jackrong

Repository Files & Downloads

6 GGUF files detected

Direct downloads for local inference

File	Type	Quantization	Size	Link
Qwopus3.6-35B-A3B-Coder-MTP-Q3_K_M.gguf	GGUF	Q3_K_M	15.99 GB	Download
Qwopus3.6-35B-A3B-Coder-MTP-Q4_K_M.gguf	GGUF	Q4_K_M	20.22 GB	Download
Qwopus3.6-35B-A3B-Coder-MTP-Q5_K_M.gguf	GGUF	Q5_K_M	23.61 GB	Download
Qwopus3.6-35B-A3B-Coder-MTP-Q6_K.gguf	GGUF	Q6_K	27.20 GB	Download
Qwopus3.6-35B-A3B-Coder-MTP-Q8_0.gguf	GGUF	Q8_0	35.21 GB	Download
mmproj-F32.gguf	GGUF	F32	1.66 GB	Download

Model Details

Model ID	Jackrong/Qwopus3.6-35B-A3B-Coder-MTP-GGUF
Author	Jackrong
Pipeline	image-text-to-text
License	apache-2.0
Base model	Jackrong/Qwopus3.6-35B-A3B-v1,unsloth/Qwen3.6-35B-A3B
Last modified	2026-07-09T03:36:48.000Z

Model README

---

library_name: transformers

base_model:

Jackrong/Qwopus3.6-35B-A3B-v1
unsloth/Qwen3.6-35B-A3B

tags:

gguf
llama.cpp
image-text-to-text
vision
multimodal
text-generation-inference
transformers
unsloth
conversational
qwen3_6
moe
coder
agent
tool-use
function-calling
thinking-off
long-context
lora
sft
logic
math

license: apache-2.0

language:

pipeline_tag: image-text-to-text

---

<h1 style="margin: 0; font-size: 28px; font-weight: 900; display: flex; align-items: center; gap: 12px; color: white; border: none;">⚙️ Qwopus-3.6-35B-A3B-Coder</h1>

<span style="background: #f97316; color: #fff7ed; font-size: 11px; font-weight: 900; padding: 5px 12px; border-radius: 999px; text-transform: uppercase; letter-spacing: 0.7px; box-shadow: 0 0 0 1px rgba(255,255,255,0.18);">Agentic Coder Release</span>

</div>

<p style="margin: 10px 0 0 0; font-size: 14px; color: #cffafe; font-weight: 600;">A thinking-off, token-efficient coding agent model built on Qwopus3.6-35B-A3B-v1 / Qwen3.6-35B-A3B.</p>

</div>

<span style="background: #ecfeff; color: #0e7490; font-size: 11px; font-weight: 800; padding: 4px 10px; border-radius: 999px; border: 1px solid #a5f3fc;">🧠 Thinking-Off Agent</span>

<span style="background: #ffedd5; color: #c2410c; font-size: 11px; font-weight: 800; padding: 4px 10px; border-radius: 999px; border: 1px solid #fed7aa;">⚡ Token-Efficient Coding</span>

<span style="background: #dcfce7; color: #166534; font-size: 11px; font-weight: 800; padding: 4px 10px; border-radius: 999px; border: 1px solid #bbf7d0;">🛠️ Tool Calling & Workflow</span>

<span style="background: #fef9c3; color: #854d0e; font-size: 11px; font-weight: 800; padding: 4px 10px; border-radius: 999px; border: 1px solid #fde68a;">🎮 Game Demo Ready</span>

</div>

<h3 style="margin: 0 0 8px 0; font-size: 16px; color: #0e7490; font-weight: 900; display: flex; align-items: center; gap: 6px;"><span>💡</span> What is Qwopus-3.6-35B-A3B-Coder?</h3>

<p style="margin: 0; font-size: 13px; color: #334155; line-height: 1.7;">🪐 <b>Qwopus-3.6-35B-A3B-Coder</b> is a practical coding-agent fine-tune focused on <b>execution efficiency</b>, not simply longer visible reasoning. It is designed for real agentic coding workflows where the model repeatedly reads files, chooses tools, edits code, runs tests, reacts to errors, and summarizes work. The core goal is to complete more of these steps with <b>less token waste, lower latency, and more stable behavior</b> when explicit long thinking is disabled.</p>

</div>

<span style="font-weight: 900; color: #0e7490; font-size: 12px; display: block; margin-bottom: 7px; text-transform: uppercase; letter-spacing: 0.6px;">⚡ Fast Agent Loops</span>

<span style="font-size: 13px; color: #334155; line-height: 1.55;">Optimized for repeated tool decisions, patching, test runs, and error-driven debugging without forcing every step into long thinking mode.</span>

</div>

<span style="font-weight: 900; color: #c2410c; font-size: 12px; display: block; margin-bottom: 7px; text-transform: uppercase; letter-spacing: 0.6px;">🧩 MoE Efficiency</span>

<span style="font-size: 13px; color: #334155; line-height: 1.55;">Built from a 35B total / 3B active-parameter MoE foundation for high-throughput local coding workflows.</span>

</div>

<span style="font-weight: 900; color: #0369a1; font-size: 12px; display: block; margin-bottom: 7px; text-transform: uppercase; letter-spacing: 0.6px;">🛠️ Agent Harness Fit</span>

<span style="font-size: 13px; color: #334155; line-height: 1.55;">Aims to fit Codex-style, OpenHands-style, Claude Code-style, and OpenCode-style agent harnesses.</span>

</div>

<span style="font-weight: 900; color: #047857; font-size: 12px; display: block; margin-bottom: 7px; text-transform: uppercase; letter-spacing: 0.6px;">🎮 Live Coding Demo</span>

<span style="font-size: 13px; color: #334155; line-height: 1.55;">Includes a slot for an RTS/game-building sample generated through an agent workflow.</span>

</div>

> [!WARNING]

> Community Release Notice: Qwopus-3.6-35B-A3B-Coder is an experimental community model intended for research, local coding-agent evaluation, and workflow exploration. It has not undergone complete safety evaluation or broad general-domain benchmarking.

> [!IMPORTANT]

> Evaluation Mode: The central design target and comparison framing in this card is thinking-off execution. The model is evaluated for whether it can remain useful and stable without relying on long visible reasoning traces at every step.

---

🎯 1. Fine-Tuning Objective: Less Overthinking, More Execution

<span>🧭</span> 1.1 Why This Model Exists

</div>

The goal of this fine-tune is <b>not</b> to chase longer reasoning chains for their own sake. In a real coding agent workflow, many steps are operational rather than deeply philosophical: read a file, inspect a stack trace, choose the next tool, edit code, run tests, check the error, continue, and report the result.

If every one of these steps enters a long thinking mode, the workflow can pay unnecessary costs: more tokens, higher latency, noisier state transitions, and greater risk of long-horizon behavioral drift. Qwopus-3.6-35B-A3B-Coder is tuned around a different product assumption:

Let the model do more agent work with fewer tokens, faster turns, and steadier tool behavior.

</div>

<span>⚡</span> 1.2 Core Optimization Target

</div>

<div style="padding: 12px; border-radius: 10px; background: #fff7ed; border: 1px solid #fed7aa;"><b style="color:#c2410c;">1. Faster next-step decisions</b><br>Identify whether to inspect, edit, test, or summarize without excessive deliberation.</div>

<div style="padding: 12px; border-radius: 10px; background: #f0f9ff; border: 1px solid #bae6fd;"><b style="color:#0369a1;">2. Lower token waste</b><br>Reduce unnecessary long-form reasoning in routine implementation steps.</div>

<div style="padding: 12px; border-radius: 10px; background: #f0fdf4; border: 1px solid #bbf7d0;"><b style="color:#047857;">3. Better workflow stability</b><br>Keep multi-turn code tasks on track across file edits, tool calls, and retries.</div>

<div style="padding: 12px; border-radius: 10px; background: #fefce8; border: 1px solid #fde68a;"><b style="color:#854d0e;">4. Local deployment fit</b><br>Make high-frequency coding tasks more practical on local or self-hosted inference stacks.</div>

</div>

<span>🛠️</span> 1.3 Target Workflows

</div>

This model is designed to be a strong fit for <b>Codex / OpenHands / Claude Code / OpenCode-style agent harnesses</b>, long-running repository edits, automated debugging, multi-round tool calls, low-latency local deployment, and large-context codebase tasks where practical execution quality matters more than verbose visible thinking.

</div>

---

💡 2. Base Model, Training Stack & Collaboration

<span>🧠</span> 2.1 Base Model: Qwopus3.6-35B-A3B-v1 / Qwen3.6-35B-A3B

</div>

The coder model builds on the Qwopus3.6-35B-A3B line, itself based on <b>Qwen3.6-35B-A3B</b>. The underlying architecture is a hybrid sparse MoE model with 35B total parameters and approximately 3B active parameters per token, making it attractive for local high-frequency coding workloads.

</p>

<thead>

<th style="padding: 9px 10px; border-bottom: 2px solid #0891b2; text-align: left; color: #0e7490; font-weight: 900; width: 30%;">Attribute</th>

<th style="padding: 9px 10px; border-bottom: 2px solid #0891b2; text-align: left; font-weight: 900;">Specifications & Details</th>

</tr>

</thead>

<tbody>

<tr><td style="padding: 9px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 800;">🧩 Architecture</td><td style="padding: 9px 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">Hybrid sparse MoE, 35B total parameters / ~3B active parameters per token</td></tr>

<tr><td style="padding: 9px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 800;">🏢 Base Developer</td><td style="padding: 9px 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">Alibaba Cloud / Qwen family, via unsloth/Qwen3.6-35B-A3B</td></tr>

<tr><td style="padding: 9px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 800;">🎯 Coder Focus</td><td style="padding: 9px 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">Agentic coding, tool-use stability, code editing, debugging, multi-turn workflow execution</td></tr>

<tr><td style="padding: 9px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 800;">⚡ Evaluation Emphasis</td><td style="padding: 9px 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">Thinking-off execution, token efficiency, lower latency, stable behavior across long agent loops</td></tr>

<tr><td style="padding: 9px 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 800;">📄 Context</td><td style="padding: 9px 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">Designed for large-context repository work; exact deployment context depends on inference stack and configuration</td></tr>

</tbody>

</table>

</div>

<span>🧪</span> 2.2 Hardware Cooperation & Joint Collaboration

</div>

This project is built in close collaboration with engineer <b>Kyle Hessling</b>, whose hardware infrastructure, training support, and live agent experiments help validate the model under practical coding workloads.

<span>Follow hardware and model training updates on X / Twitter: <a href="https://x.com/KyleHessling1" target="_blank" style="color: #047857; text-decoration: none; font-weight: 900;">@KyleHessling1</a></span>

</div>

<span>Benchmarks courtesy of <b>Tom Turney</b>, <a href="https://x.com/no_stp_on_snek" target="_blank" style="color: #047857; text-decoration: none; font-weight: 900;">@no_stp_on_snek</a> on X.</span>

</div>

<span>🦥</span> 2.3 Fine-Tuning Framework: Unsloth

</div>

The training workflow is accelerated and memory-optimized with <b>Unsloth</b>. Special thanks to the Unsloth team for making efficient large-model fine-tuning more accessible.

<span>Documentation and fine-tuning guidance: <a href="https://unsloth.ai/docs" target="_blank" style="color: #7c3aed; text-decoration: none; font-weight: 900;">unsloth.ai/docs</a></span>

</div>

---

📊 3. Thinking-Off Agentic Evaluation

<h3 style="margin: 0; font-size: 21px; font-weight: 900; display: flex; align-items: center; gap: 8px; color: white; border: none;">📊 Evaluation: Qwopus 3.6 35B Thinking-Off vs Ornith-1.0 35B Thinking-On</h3>

<p style="margin: 6px 0 0 0; font-size: 13px; color: #cffafe;">Comparison between Qwopus with thinking disabled and Ornith with thinking enabled. All benchmark runs in this section use Q5_K_M / Q5KM quantized models. Higher is better. Benchmarks courtesy of Tom Turney, @no_stp_on_snek on X.</p>

</div>

<div>

<span style="font-weight: 900; font-size: 14px; color: #0e7490; display: block; margin-bottom: 5px;">Main Finding</span>

<span style="font-size: 13px; color: #334155; line-height: 1.7;">In these Q5_K_M quantized evaluations, <b>Qwopus 3.6 35B</b> was tested with <b>thinking disabled</b>. The model also completed a 300-case SWE-bench submitted-patch run with a <b>62.4%</b> score. In the behavioral comparison, Qwopus leads in practical execution categories such as legit-request compliance, integrity under pressure, multi-turn orchestration, large code deliverables, and sustained debugging. Ornith remains stronger in selected reasoning-oriented dimensions such as long-context recall, metacognition, engineering competence, and context-poison resistance.</span>

</div>

<div>

<span style="font-weight: 900; font-size: 15px; color: #c2410c; display: block; margin-bottom: 6px;">Interactive Model Deck by Kyle Hessling</span>

<span style="font-size: 13px; color: #334155; line-height: 1.7; display: block;">Kyle created a short Hugging Face Space deck that walks through the model story visually: thinking-off agentic coding, the 35B / 3B MoE setup, MTP-assisted local inference, SWE-bench results, token-efficiency comparisons, Qwopus OFF vs Ornith ON, and the OpenCode RTS demo.</span>

</div>

<span style="background: #ffedd5; color: #c2410c; font-size: 11px; font-weight: 900; padding: 7px 10px; border-radius: 9px; border: 1px solid #fed7aa; text-align: center;">visual explainer</span>

<span style="background: #dcfce7; color: #047857; font-size: 11px; font-weight: 900; padding: 7px 10px; border-radius: 9px; border: 1px solid #bbf7d0; text-align: center;">thinking-off workflow</span>

<span style="background: #e0e7ff; color: #4338ca; font-size: 11px; font-weight: 900; padding: 7px 10px; border-radius: 9px; border: 1px solid #c7d2fe; text-align: center;">SWE-bench + RTS demo</span>

</div>

<a href="https://huggingface.co/spaces/KyleHessling1/qwopus36-35b-a3b-coder-deck" target="_blank" style="display: block; padding: 13px 16px; background: linear-gradient(135deg, #f97316 0%, #2563eb 100%); color: white; text-decoration: none; font-size: 13px; font-weight: 900; text-align: center; letter-spacing: 0.2px;">Open Kyle's interactive deck →</a>

</div>

<span style="font-size: 11px; font-weight: 900; color: #047857; text-transform: uppercase; display: block; margin-bottom: 7px; letter-spacing: 0.6px;">Average Score</span>

<span style="font-size: 12px; color: #475569; font-weight: 700;">Qwopus vs Ornith</span>

</div>

<span style="font-size: 11px; font-weight: 900; color: #6d28d9; text-transform: uppercase; display: block; margin-bottom: 7px; letter-spacing: 0.6px;">SWE-bench</span>

<span style="font-size: 12px; color: #475569; font-weight: 700;">300 cases, submitted patches</span>

</div>

<span>🧪</span> 3.1 SWE-bench Submitted-Patch Run

</div>

<b>Result:</b> Qwopus-3.6-35B-A3B-Coder scored <b>62.4%</b> on a <b>300-case SWE-bench run</b> using <b>thinking off</b> and <b>submitted patches</b>. The evaluated model was the <b>Q5_K_M quantized</b> build.

</div>

<div style="padding: 13px; border-radius: 10px; background: #ecfeff; border: 1px solid #a5f3fc;"><span style="font-size: 11px; font-weight: 900; color: #0e7490; text-transform: uppercase; display: block; margin-bottom: 5px;">Benchmark</span><b style="font-size: 15px; color: #0f172a;">SWE-bench</b></div>

<div style="padding: 13px; border-radius: 10px; background: #fff7ed; border: 1px solid #fed7aa;"><span style="font-size: 11px; font-weight: 900; color: #c2410c; text-transform: uppercase; display: block; margin-bottom: 5px;">Run Size</span><b style="font-size: 15px; color: #0f172a;">300 tasks</b></div>

<div style="padding: 13px; border-radius: 10px; background: #f0fdf4; border: 1px solid #bbf7d0;"><span style="font-size: 11px; font-weight: 900; color: #047857; text-transform: uppercase; display: block; margin-bottom: 5px;">Mode</span><b style="font-size: 15px; color: #0f172a;">Thinking off</b></div>

<div style="padding: 13px; border-radius: 10px; background: #f5f3ff; border: 1px solid #ddd6fe;"><span style="font-size: 11px; font-weight: 900; color: #6d28d9; text-transform: uppercase; display: block; margin-bottom: 5px;">Quantization</span><b style="font-size: 15px; color: #0f172a;">Q5_K_M</b></div>

</div>

<thead>

<th style="padding: 10px; border-bottom: 2px solid #7c3aed; text-align: left; color: #6d28d9; font-weight: 900;">Evaluation</th>

<th style="padding: 10px; border-bottom: 2px solid #7c3aed; text-align: left; font-weight: 900;">Model / Quant</th>

<th style="padding: 10px; border-bottom: 2px solid #7c3aed; text-align: left; font-weight: 900;">Patch Mode</th>

<th style="padding: 10px; border-bottom: 2px solid #7c3aed; text-align: left; font-weight: 900;">Score</th>

</tr>

</thead>

<tbody>

<td style="padding: 10px; font-weight: 800;">SWE-bench, 300 cases</td>

<td style="padding: 10px;">Qwopus-3.6-35B-A3B-Coder Q5_K_M</td>

<td style="padding: 10px;">Thinking off, submitted patches</td>

</tr>

</tbody>

</table>

</div>

<span>⚖️</span> 3.2 Numerical Scorecard

</div>

<b>Note:</b> Scores are held-out behavioral + long-horizon coding evaluation results on a 0-100 scale. Higher is better. The comparison intentionally contrasts Qwopus in thinking-off mode with Ornith-1.0 in thinking-on mode.

</div>

<thead>

<th style="padding: 10px; border-bottom: 2px solid #0891b2; text-align: left; color: #0e7490; font-weight: 900;">Capability Area</th>

<th style="padding: 10px; border-bottom: 2px solid #0891b2; text-align: left; font-weight: 900;">Qwopus 3.6 35B<br><span style="font-size: 12px; color: #047857; font-weight: 900; display: block; margin-top: 4px;">thinking off</span></th>

<th style="padding: 10px; border-bottom: 2px solid #0891b2; text-align: left; font-weight: 900;">Ornith-1.0 35B<br><span style="font-size: 12px; color: #b91c1c; font-weight: 900; display: block; margin-top: 4px;">thinking on</span></th>

<th style="padding: 10px; border-bottom: 2px solid #0891b2; text-align: left; font-weight: 900;">Observed Pattern</th>

</tr>

</thead>

<tbody>

<tr style="background: #ecfeff;"><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 800;">Legit-request compliance</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 900; color: #0e7490;">100</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">70</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">Qwopus follows allowed user intent much more reliably.</td></tr>

<tr><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 800;">Integrity under pressure</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 900; color: #0e7490;">93</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">86</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">Qwopus is more stable under adversarial or stressful workflow conditions.</td></tr>

<tr style="background: #ecfeff;"><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 800;">Multi-turn orchestration</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 900; color: #0e7490;">80</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">70</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">Qwopus better maintains state across long agent loops.</td></tr>

<tr><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 800;">Large code deliverable</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 900; color: #0e7490;">75</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">65</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">Qwopus shows stronger completion behavior for larger code artifacts.</td></tr>

<tr style="background: #ecfeff;"><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 800;">Sustained debugging</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 900; color: #0e7490;">60</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">50</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">Qwopus holds a practical edge across repeated fix-test cycles.</td></tr>

<tr><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 800;">Long-context recall</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">90</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 900; color: #b45309;">95</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">Ornith retains a small advantage in recall-heavy thinking-on settings.</td></tr>

<tr style="background: #fff7ed;"><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 800;">Metacognition</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">90</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 900; color: #b45309;">95</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">Ornith benefits from explicit thinking-on reflection.</td></tr>

<tr><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 800;">Engineering competence</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">81</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15); font-weight: 900; color: #b45309;">94</td><td style="padding: 10px; border-bottom: 1px solid rgba(128,128,128,0.15);">Ornith remains stronger in broad engineering competence.</td></tr>

<tr style="background: #fff7ed;"><td style="padding: 10px; font-weight: 800;">Context-poison resistance</td><td style="padding: 10px;">70</td><td style="padding: 10px; font-weight: 900; color: #b45309;">85</td><td style="padding: 10px;">Ornith is more robust against context poisoning in this test.</td></tr>

</tbody>

</table>

</div>

<b>Takeaway:</b> Qwopus-3.6-35B-A3B-Coder is positioned as a <b>practical agent execution model</b>. The important result is not merely whether it can think longer, but whether it can keep acting correctly when the workflow demands many fast, concrete decisions. This makes it especially relevant for local coding agents, automated debugging loops, and large codebase tasks where token efficiency directly affects usability.

</div>

---

🎮 4. Live Agent Demo: RTS Game Sample

<h3 style="margin: 0; font-size: 20px; font-weight: 900; display: flex; align-items: center; gap: 8px; color: white; border: none;">🎮 OpenCode / Agent Game-Building Demo</h3>

<p style="margin: 5px 0 0 0; font-size: 13px; color: #dcfce7;">A practical visual test for whether the model can plan, code, iterate, and deliver an interactive project inside an agent workflow.</p>

</div>

<p style="margin: 0 0 12px 0;">Kyle Hessling tested the soon-to-release Qwopus-Coder-35B-A3B in an OpenCode workflow by asking it to create a complete RTS-style game sample. This kind of demo is useful because it combines code generation, file orchestration, UI/gameplay logic, iterative correction, and final deliverable quality in one visible task.</p>

<div style="display: block; padding: 12px 14px; border: 1px solid #bae6fd; border-radius: 10px; background: #f0f9ff; color: #0369a1; font-weight: 900;">Game screenshot added below</div>

</div>

</a>

<b>Why this matters:</b> a playable game demo is not a formal benchmark, but it is a high-signal smoke test for agentic coding. It exposes whether the model can maintain project structure, generate coherent state logic, and complete a visually inspectable artifact rather than only answering isolated prompts.

</div>

---

🗺️ 5. Training & Workflow Design

The training and evaluation philosophy for this release centers on agent execution rather than visible chain length. The model should know when to act directly, when to inspect more context, and when to stop and summarize.

       [ Qwopus-3.6-35B-A3B-Coder: Agentic Execution Pipeline ]

  Base MoE Foundation
  Qwen3.6-35B-A3B / Qwopus3.6-35B-A3B-v1
          │
          ▼
  Coding + Tool-Use Adaptation
  repository tasks, debugging traces, tool schemas, multi-turn feedback
          │
          ▼
  Thinking-Off Behavior Target
  faster next-step decisions, less overthinking, lower token waste
          │
          ▼
  Agent Harness Workflows
  read files → choose tool → edit code → run tests → inspect errors → iterate → report
          │
          ▼
  Final Objective
  stable long-horizon code execution with practical local latency

> [!NOTE]

> This model card intentionally frames thinking-off behavior as a product target. Long thinking can still be useful for difficult reasoning, but the release focuses on whether the model can complete real coding-agent work without paying that cost on every step.

---

✅ 6. Recommended Use Cases & Known Limits

<span>✅</span> Good Fits

</div>

Codex-style agent workflows, OpenHands/OpenCode coding loops, repository-level debugging, multi-file patch generation, automated test-fix cycles, local tool-calling agents, DevOps scripting, code review assistance, and large-context project navigation.

</div>

<span>⚠️</span> Use With Care

</div>

As a specialized coder model, it should not be assumed to be optimal for every general-domain task. Tool-call quality depends strongly on prompt format, schema consistency, and the surrounding harness. Long thinking may still help on some high-difficulty reasoning tasks where speed is less important.

</div>

> [!CAUTION]

> Deployment note: For agent use, ensure that tool definitions, system prompts, output parsing, and retry behavior are consistent. Thinking-off models can be fast, but the harness still needs clean schemas, useful error feedback, and strict task boundaries.

---

📚 7. Resources, Acknowledgements & Citation

<h3 style="margin: 0; color: white; border: none; font-size: 19px; font-weight: 900;">📚 Resources & Credits</h3>

</div>

<p style="margin: 0 0 12px 0;">👉 <a href="https://github.com/R6410418/Jackrong-llm-finetuning-guide.git" target="_blank" style="color: #0369a1; text-decoration: none; font-weight: 900;">GitHub Repository: Jackrong-llm-finetuning-guide</a><br>Access the project repository and related fine-tuning guides.</p>

<p style="margin: 0 0 12px 0;">👉 <b>Q5_K_M benchmark evaluations</b><br>SWE-bench submitted-patch run plus behavioral / long-horizon coding evaluation. Benchmarks courtesy of Tom Turney, <b>@no_stp_on_snek</b> on X.</p>

<p style="margin: 0 0 12px 0;">👉 <a href="https://huggingface.co/spaces/KyleHessling1/qwopus36-35b-a3b-coder-deck" target="_blank" style="color: #c2410c; text-decoration: none; font-weight: 900;">Kyle Hessling Interactive Model Deck</a><br>Visual Hugging Face Space explaining the model story, thinking-off workflow, SWE-bench result, token efficiency, and RTS demo.</p>

<p style="margin: 0 0 12px 0;">👉 <a href="https://x.com/KyleHessling1/status/2070562997630873699" target="_blank" style="color: #047857; text-decoration: none; font-weight: 900;">Kyle Hessling RTS Game Demo Post</a><br>Reference post for the OpenCode / RTS game-building sample.</p>

<p style="margin: 0 0 16px 0;">👉 <a href="https://unsloth.ai/docs" target="_blank" style="color: #7c3aed; text-decoration: none; font-weight: 900;">Unsloth Documentation</a><br>Training acceleration and memory-efficient fine-tuning resources.</p>

<b>Acknowledgements:</b> Special thanks to the Qwen team for the strong Qwen3.6 MoE base model, Unsloth for efficient fine-tuning tooling, Kyle Hessling for hardware collaboration and live agent testing, and open-source contributors building the agentic coding ecosystem.

</div>

<b style="display: block; margin-bottom: 8px;">Citation</b>

<pre style="margin: 0; padding: 14px; border-radius: 10px; background: #f8fafc; color: #334155; border: 1px solid #e2e8f0; overflow-x: auto; font-size: 12px; line-height: 1.55;"><code style="color: #334155;">@misc{jackrong_qwopus36_35b_a3b_coder,

title = {Qwopus-3.6-35B-A3B-Coder},

author = {Jackrong},

year = {2026},

publisher = {Hugging Face},

howpublished = {\url{https://huggingface.co/Jackrong/Qwopus-3.6-35B-A3B-Coder}}

}</code></pre>

</div>

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