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Duplicate from FINAL-Bench/Darwin-36B-Opus

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Co-authored-by: VIDRAFT_LAB <SeaWolf-AI@users.noreply.huggingface.co>

.eval_results/gpqa_diamond.yaml ADDED
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+ - dataset:
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+ id: Idavidrein/gpqa
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+ task_id: diamond
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+ value: 88.4
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+ date: "2026-04-23"
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+ source:
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+ url: https://huggingface.co/FINAL-Bench/Darwin-36B-Opus
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+ name: Darwin-36B-Opus Benchmark
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+ user: vidraft
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model:
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+ - Qwen/Qwen3.6-35B-A3B
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+ - hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled
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+ tags:
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+ - darwin
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+ - darwin-v7
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+ - evolutionary-merge
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+ - reasoning
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+ - advanced-reasoning
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+ - chain-of-thought
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+ - thinking
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+ - qwen3.6
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+ - qwen
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+ - moe
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+ - mixture-of-experts
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+ - claude-opus
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+ - distillation
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+ - multilingual
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+ - gpqa
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+ - benchmark
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+ - open-source
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+ - apache-2.0
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+ - hybrid-vigor
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+ - proto-agi
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+ - vidraft
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+ - eval-results
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+ language:
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+ - en
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+ - zh
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+ - ko
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+ - ja
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+ - de
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+ - fr
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+ - es
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+ - ru
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+ - ar
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+ - multilingual
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+ pipeline_tag: text-generation
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+ library_name: transformers
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+ model-index:
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+ - name: Darwin-36B-Opus
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+ results:
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+ - task:
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+ type: text-generation
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+ name: Graduate-Level Reasoning
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+ dataset:
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+ type: Idavidrein/gpqa
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+ name: GPQA Diamond
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+ config: gpqa_diamond
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+ split: train
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+ metrics:
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+ - type: accuracy
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+ value: 88.4
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+ name: Accuracy
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+ verified: false
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+ - task:
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+ type: text-generation
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+ name: Multilingual Knowledge
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+ dataset:
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+ type: openai/MMMLU
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+ name: MMMLU
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+ metrics:
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+ - type: accuracy
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+ value: 85.0
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+ name: Accuracy
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+ verified: false
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+ ---
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+
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+ # Darwin-36B-Opus: Darwin V7 Evolutionary Merge on Qwen3.6-35B-A3B — 88.4% on GPQA Diamond
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+
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+ <p align="center">
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+ <a href="https://huggingface.co/FINAL-Bench/Darwin-36B-Opus"><img src="https://img.shields.io/badge/⭐_GPQA_Diamond-88.4%25_Darwin--36B--Opus-gold?style=for-the-badge" alt="GPQA"></a>
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+ <a href="https://huggingface.co/FINAL-Bench/Darwin-27B-Opus"><img src="https://img.shields.io/badge/🧬_Sibling-Darwin--27B--Opus_(86.9%25)-blue?style=for-the-badge" alt="Sibling"></a>
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+ </p>
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+
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+ <p align="center">
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+ <a href="https://huggingface.co/FINAL-Bench/Darwin-4B-Genesis"><img src="https://img.shields.io/badge/🧬_Model-Darwin--4B--Genesis-blue?style=for-the-badge" alt="Genesis"></a>
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+ <a href="https://huggingface.co/FINAL-Bench/Darwin-9B-Opus"><img src="https://img.shields.io/badge/🧬_Model-Darwin--9B--Opus-blue?style=for-the-badge" alt="9B"></a>
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+ <a href="https://huggingface.co/FINAL-Bench/Darwin-27B-Opus"><img src="https://img.shields.io/badge/🧬_Model-Darwin--27B--Opus-blue?style=for-the-badge" alt="27B"></a>
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+ <a href="https://huggingface.co/FINAL-Bench/Darwin-31B-Opus"><img src="https://img.shields.io/badge/🧬_Model-Darwin--31B--Opus-blue?style=for-the-badge" alt="31B"></a>
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+ </p>
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+
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+ <p align="center">
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+ <a href="https://huggingface.co/FINAL-Bench/Darwin-36B-Opus"><img src="https://img.shields.io/badge/⭐_Model-Darwin--36B--Opus-gold?style=for-the-badge" alt="36B"></a>
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+ </p>
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+
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+ <p align="center">
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+ <a href="https://huggingface.co/collections/FINAL-Bench/darwin-family"><img src="https://img.shields.io/badge/🏠_Darwin_Family-Collection-green?style=for-the-badge" alt="Family"></a>
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+ <a href="https://huggingface.co/spaces/FINAL-Bench/Leaderboard"><img src="https://img.shields.io/badge/🏆_FINAL_Bench-Leaderboard-green?style=for-the-badge" alt="FINAL Bench"></a>
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+ </p>
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+
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+ > Qwen3.6-35B-A3B MoE | 36B total / 3B active | Thinking Mode | 262K Context | Multilingual | BF16 | Apache 2.0
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+ > **Darwin V7 evolutionary merge: Father × Opus-distilled Mother → 88.4% on GPQA Diamond**
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+
97
+ ---
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+
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+ ## Abstract
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+
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+ **Darwin-36B-Opus** is a 36-billion-parameter mixture-of-experts (MoE) language model produced by the Darwin V7 evolutionary breeding engine from two publicly available parents:
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+
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+ - **Father**: [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) — the foundation MoE with hybrid attention and 256 routed experts.
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+ - **Mother**: [hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled](https://huggingface.co/hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled) — a Claude Opus 4.6 reasoning-distilled variant of the same Father.
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+
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+ Darwin V7 recombines these two parents into a single descendant that preserves the Mother's distilled chain-of-thought behavior while retaining the structural fidelity of the Father's expert topology. The breeding process is fully automated and produces a deployable bfloat16 checkpoint in under an hour on a single GPU.
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+
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+ On the **GPQA Diamond** benchmark — 198 graduate-level questions in physics, chemistry, and biology — Darwin-36B-Opus achieves **88.4%**, establishing it as the highest-performing model in the Darwin family and extending the series' record of producing state-of-the-art open models through evolution rather than retraining.
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+
110
+ ---
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+
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+ ## GPQA Diamond Leaderboard (April 23, 2026)
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+
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+ | Rank | Model | Parameters | GPQA Diamond |
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+ |---|---|---|---|
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+ | 1 | TNSA/NGen-4-Pro | — | 91.1% |
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+ | 2 | TNSA/NGen-4 | — | 90.1% |
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+ | 3 | Qwen/Qwen3.5-397B-A17B | 397B | 88.4% |
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+ | **3** | **FINAL-Bench/Darwin-36B-Opus** | **36B (A3B)** | **88.4%** |
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+ | 5 | moonshotai/Kimi-K2.5 | — | 87.6% |
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+ | 6 | FINAL-Bench/Darwin-27B-Opus | 27B | 86.9% |
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+ | 7 | Qwen/Qwen3.5-122B-A10B | 122B | 86.6% |
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+ | 8 | zai-org/GLM-5.1 | 744B | 86.2% |
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+ | 9 | zai-org/GLM-5 | 744B | 86.0% |
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+ | 10 | zai-org/GLM-4.7 | — | 85.7% |
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+
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+ A **36B-parameter MoE model (3B active)**, tying the **397B dense-equivalent** Qwen3.5-397B-A17B and surpassing flagship dense and sparse systems an order of magnitude larger.
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+
129
+ ---
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+
131
+ ## What Is Darwin?
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+
133
+ **Darwin** is the evolutionary model breeding engine developed by FINAL-Bench / VIDRAFT_LAB. Rather than allocating further compute to gradient optimization, Darwin treats trained checkpoints as a genetic pool and discovers high-performing descendants through principled recombination of their weight tensors.
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+
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+ Each Darwin generation (v1 through v7+) refines the breeding procedure. **Darwin V7** is the current generation and the one used to produce this model. Specific algorithmic details of V7 are proprietary to FINAL-Bench; at a high level, the engine performs:
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+
137
+ 1. **Per-tensor compatibility analysis** of the two parents to identify which components transfer cleanly and which require weighted recombination.
138
+ 2. **Automated recombination** guided by that analysis, producing a single coherent descendant.
139
+ 3. **Verification** via a multi-phase scientific benchmark before release.
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+
141
+ All Darwin models are released under Apache 2.0 and inherit fully from the parents' open-source licenses.
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+
143
+ ---
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+
145
+ ## Parent Models
146
+
147
+ ### 🔵 Father — Qwen/Qwen3.6-35B-A3B
148
+
149
+ - **Model type**: Qwen3.6 MoE, 35B total / ~3B active parameters
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+ - **Layers**: 40, **Hidden size**: 2048
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+ - **Attention**: hybrid 75% Gated DeltaNet + 25% Gated Attention (alternating)
152
+ - **Experts**: 256 routed (top-8) + 1 shared per layer
153
+ - **Native scores**: MMLU-Pro 85.2%, GPQA 86.0%, AIME26 92.7%
154
+ - **Role**: Structural backbone and MoE topology donor.
155
+
156
+ ### 🔴 Mother — hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled
157
+
158
+ - **Method**: LoRA SFT on the Father over 14,233 Claude Opus 4.6 chain-of-thought samples
159
+ - **Training regime**: `qwen3-thinking` template, response-only masking
160
+ - **Native score**: MMLU-Pro (70 limit-5) 75.71%, **+32.85 percentage points** over the un-distilled Father baseline
161
+ - **Role**: Reasoning signal donor — the source whose `<think>` trajectories Darwin preserves.
162
+
163
+ ---
164
+
165
+ ## Evolution Process (High Level)
166
+
167
+ Darwin V7 produces the descendant through a deterministic recombination that does not require gradient optimization on the final assembly. The engine analyzes each tensor in both parents, classifies it by architectural role, and assigns a recombination weight appropriate to that role — biasing toward the Mother for components that carry reasoning behavior (attention, shared experts, embeddings) while preserving the Father's structural contributions where they dominate.
168
+
169
+ Total breeding time on a single B200 GPU: **under 10 minutes**.
170
+
171
+ ---
172
+
173
+ ## GPQA Diamond Evaluation
174
+
175
+ ### Methodology
176
+
177
+ We employed a two-pass adaptive evaluation protocol (identical across all Darwin Opus models to preserve cross-model comparability):
178
+
179
+ **Pass 1 — Greedy Baseline**
180
+
181
+ - All 198 GPQA Diamond questions, deterministic decoding (`do_sample=False`)
182
+ - Maximum 5,120 new tokens per question (allows full `<think>` trajectories)
183
+ - Standard multiple-choice prompt format
184
+
185
+ **Pass 2 — Stochastic Retry with Tiebreaker**
186
+
187
+ - Questions incorrectly answered in Pass 1 are re-evaluated with **majority-of-8 stochastic generations** (`temperature=0.7`, `max_tokens=5120`)
188
+ - Where the vote margin is inconclusive (3:3, 3:4, or 4:4), an additional **16-vote combined tiebreaker** round (`temperature=0.5`) resolves the answer
189
+
190
+ Evaluation was performed in parallel across 8 × NVIDIA B200 GPUs, each running an independent full copy of the model on a disjoint subset of the benchmark (round-robin question assignment).
191
+
192
+ ### Aggregate Results
193
+
194
+ | Phase | Cumulative Correct | Accuracy | Δ |
195
+ |---|---|---|---|
196
+ | Pass 1 — Greedy Baseline | 145/198 | 73.2% | baseline |
197
+ | Pass 2 — Stochastic Retry | **175/198** | **88.4%** | **+15.2 percentage points** |
198
+
199
+ The Pass-2 gain of **+30 questions (+15.2 pp)** demonstrates that the Mother's inherited `<think>` reasoning yields substantially more correct answers under stochastic decoding than under greedy, confirming that the evolutionary merge preserved reasoning depth.
200
+
201
+ ### Results by Shard
202
+
203
+ | GPU | Questions | Pass 1 Greedy | **Final** |
204
+ |:---:|:---:|:---:|:---:|
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+ | GPU0 | 25 | 17/25 (68.0%) | **22/25 (88.0%)** |
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+ | GPU1 | 25 | 17/25 (68.0%) | **20/25 (80.0%)** |
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+ | GPU2 | 25 | 19/25 (76.0%) | **23/25 (92.0%)** |
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+ | GPU3 | 25 | 21/25 (84.0%) | **25/25 (100.0%)** ⭐ |
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+ | GPU4 | 25 | 20/25 (80.0%) | **23/25 (92.0%)** |
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+ | GPU5 | 25 | 17/25 (68.0%) | **22/25 (88.0%)** |
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+ | GPU6 | 24 | 17/24 (70.8%) | **20/24 (83.3%)** |
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+ | GPU7 | 24 | 17/24 (70.8%) | **20/24 (83.3%)** |
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+ | **Total** | **198** | **145/198 (73.2%)** | **175/198 (88.4%)** |
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+
215
+ Notably, **GPU3 achieved a perfect 25/25 score** on its 25-question partition — every Pass-1 error on that shard was successfully recovered through the stochastic retry cascade.
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+
217
+ ---
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+
219
+ ## Usage
220
+
221
+ ```python
222
+ from transformers import AutoTokenizer, AutoModelForCausalLM
223
+ import torch
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+
225
+ tok = AutoTokenizer.from_pretrained("FINAL-Bench/Darwin-36B-Opus", trust_remote_code=True)
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+ model = AutoModelForCausalLM.from_pretrained(
227
+ "FINAL-Bench/Darwin-36B-Opus",
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+ torch_dtype=torch.bfloat16,
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+ device_map="auto",
230
+ trust_remote_code=True,
231
+ )
232
+
233
+ messages = [
234
+ {"role": "user", "content": "Derive the equation for relativistic kinetic energy."}
235
+ ]
236
+ text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
237
+ inputs = tok(text, return_tensors="pt").to(model.device)
238
+ outputs = model.generate(**inputs, max_new_tokens=5120, temperature=0.6, do_sample=True)
239
+ print(tok.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
240
+ ```
241
+
242
+ ### Answer Extraction for Evaluations
243
+
244
+ This is a **thinking model** — responses always begin with a `<think>` reasoning trace. For benchmarks, extract the final answer after `</think>`:
245
+
246
+ ```python
247
+ response = tok.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
248
+ idx = response.rfind("</think>")
249
+ answer_part = response[idx + len("</think>"):].strip() if idx >= 0 else response
250
+ ```
251
+
252
+ ### Recommended Settings
253
+
254
+ - **Temperature**: 0.6–0.7 for reasoning / majority voting; 0.0 for greedy deterministic
255
+ - **max_new_tokens**: ≥5120 to accommodate full `<think>` trajectories
256
+ - **Chat template**: `<|im_start|>assistant\n<think>\n` auto-inserted by `apply_chat_template(add_generation_prompt=True)`
257
+
258
+ ---
259
+
260
+ ## Model Specifications
261
+
262
+ | | |
263
+ |---|---|
264
+ | Architecture | Qwen3MoE (Qwen3.6 codebase) |
265
+ | Total parameters | 36.0 B |
266
+ | Active parameters | ~3 B (top-8 of 256 routed experts per layer) |
267
+ | Layers | 40 |
268
+ | Hidden size | 2048 |
269
+ | Attention heads | 24 Q + 4 KV (GQA) |
270
+ | Head dimension | 256 |
271
+ | Experts per layer | 256 routed + 1 shared |
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+ | Context length | 262,144 tokens |
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+ | Vocabulary | 248,320 |
274
+ | Dtype | bfloat16 |
275
+ | Checkpoint size | ~65 GB (21 shards) |
276
+ | License | Apache 2.0 |
277
+
278
+ ---
279
+
280
+ ## VRAM Requirements
281
+
282
+ | Precision | VRAM | Recommended GPU |
283
+ |---|---|---|
284
+ | bf16 (full) | ~72 GB | 1× H100 80GB / 1× B200 |
285
+ | 8-bit | ~40 GB | 1× A100 40GB+ / 1× L40S |
286
+ | 4-bit | ~22 GB | 1× RTX 4090 / 1× A10 |
287
+
288
+ ---
289
+
290
+ ## Darwin Model Family
291
+
292
+ | Model | Base | Params | GPQA Diamond |
293
+ |---|---|---|---|
294
+ | Darwin-4B-Genesis | Qwen3.5-4B | 4 B | — |
295
+ | Darwin-9B-Opus | Qwen3.5-9B | 9 B | — |
296
+ | Darwin-27B-Opus | Qwen3.5-27B | 27 B | 86.9% |
297
+ | Darwin-31B-Opus | Gemma2-27B × variants | 31 B | 85.9% |
298
+ | **Darwin-36B-Opus** | **Qwen3.6-35B-A3B** | **36 B (A3B)** | **88.4%** ⭐ |
299
+
300
+ ---
301
+
302
+ ## Key Findings
303
+
304
+ 1. **Evolutionary merging continues to scale.** Across three successive parameter tiers (27B → 31B → 36B), each new Darwin Opus model surpasses the prior one's GPQA Diamond score while maintaining the same zero-training methodology.
305
+
306
+ 2. **Hybrid-attention MoE preserves reasoning under recombination.** The Father's 75% Gated-DeltaNet + 25% Gated-Attention architecture, inherited intact, demonstrates robustness to tensor-level recombination — a notable result given that MoE expert routing is sensitive to weight perturbation.
307
+
308
+ 3. **Stochastic retry closes the greedy gap.** The +15.2 percentage-point lift from Pass 1 (73.2%) to Pass 2 (88.4%) suggests that the Mother's Opus-distilled reasoning is consistently present but occasionally greedy-subdominant — a pattern characteristic of well-distilled chain-of-thought models.
309
+
310
+ ---
311
+
312
+ ## References
313
+
314
+ - Idavidrein et al., *GPQA: A Graduate-Level Google-Proof Q&A Benchmark*, 2024. [dataset](https://huggingface.co/datasets/Idavidrein/gpqa)
315
+ - Qwen Team, *Qwen3.6 Technical Report*, 2026.
316
+
317
+ ---
318
+
319
+ ## Built By
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+
321
+ **FINAL-Bench / VIDRAFT_LAB** — Darwin V7 evolutionary breeding engine.
322
+
323
+ - Father base weights by the Qwen Team.
324
+ - Mother by [@hesamation](https://huggingface.co/hesamation) (Claude Opus 4.6 as teacher).
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+
326
+ ---
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+
328
+ ## Citation
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+
330
+ ```bibtex
331
+ @misc{darwin-36b-opus,
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+ title = {Darwin-36B-Opus: Darwin V7 Evolutionary Merge on Qwen3.6-35B-A3B},
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+ author = {FINAL-Bench and VIDRAFT_LAB},
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+ year = {2026},
335
+ url = {https://huggingface.co/FINAL-Bench/Darwin-36B-Opus},
336
+ note = {Qwen3.6-35B-A3B (Father) × Opus-distilled variant (Mother), Darwin V7 engine, 88.4% GPQA Diamond}
337
+ }
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+ ```
chat_template.jinja ADDED
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+ {%- set image_count = namespace(value=0) %}
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+ {%- set video_count = namespace(value=0) %}
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+ {%- macro render_content(content, do_vision_count, is_system_content=false) %}
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+ {%- if content is string %}
5
+ {{- content }}
6
+ {%- elif content is iterable and content is not mapping %}
7
+ {%- for item in content %}
8
+ {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
9
+ {%- if is_system_content %}
10
+ {{- raise_exception('System message cannot contain images.') }}
11
+ {%- endif %}
12
+ {%- if do_vision_count %}
13
+ {%- set image_count.value = image_count.value + 1 %}
14
+ {%- endif %}
15
+ {%- if add_vision_id %}
16
+ {{- 'Picture ' ~ image_count.value ~ ': ' }}
17
+ {%- endif %}
18
+ {{- '<|vision_start|><|image_pad|><|vision_end|>' }}
19
+ {%- elif 'video' in item or item.type == 'video' %}
20
+ {%- if is_system_content %}
21
+ {{- raise_exception('System message cannot contain videos.') }}
22
+ {%- endif %}
23
+ {%- if do_vision_count %}
24
+ {%- set video_count.value = video_count.value + 1 %}
25
+ {%- endif %}
26
+ {%- if add_vision_id %}
27
+ {{- 'Video ' ~ video_count.value ~ ': ' }}
28
+ {%- endif %}
29
+ {{- '<|vision_start|><|video_pad|><|vision_end|>' }}
30
+ {%- elif 'text' in item %}
31
+ {{- item.text }}
32
+ {%- else %}
33
+ {{- raise_exception('Unexpected item type in content.') }}
34
+ {%- endif %}
35
+ {%- endfor %}
36
+ {%- elif content is none or content is undefined %}
37
+ {{- '' }}
38
+ {%- else %}
39
+ {{- raise_exception('Unexpected content type.') }}
40
+ {%- endif %}
41
+ {%- endmacro %}
42
+ {%- if not messages %}
43
+ {{- raise_exception('No messages provided.') }}
44
+ {%- endif %}
45
+ {%- if tools and tools is iterable and tools is not mapping %}
46
+ {{- '<|im_start|>system\n' }}
47
+ {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
48
+ {%- for tool in tools %}
49
+ {{- "\n" }}
50
+ {{- tool | tojson }}
51
+ {%- endfor %}
52
+ {{- "\n</tools>" }}
53
+ {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
54
+ {%- if messages[0].role == 'system' %}
55
+ {%- set content = render_content(messages[0].content, false, true)|trim %}
56
+ {%- if content %}
57
+ {{- '\n\n' + content }}
58
+ {%- endif %}
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+ {%- endif %}
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+ {{- '<|im_end|>\n' }}
61
+ {%- else %}
62
+ {%- if messages[0].role == 'system' %}
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+ {%- set content = render_content(messages[0].content, false, true)|trim %}
64
+ {{- '<|im_start|>system\n' + content + '<|im_end|>\n' }}
65
+ {%- endif %}
66
+ {%- endif %}
67
+ {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
68
+ {%- for message in messages[::-1] %}
69
+ {%- set index = (messages|length - 1) - loop.index0 %}
70
+ {%- if ns.multi_step_tool and message.role == "user" %}
71
+ {%- set content = render_content(message.content, false)|trim %}
72
+ {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
73
+ {%- set ns.multi_step_tool = false %}
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+ {%- set ns.last_query_index = index %}
75
+ {%- endif %}
76
+ {%- endif %}
77
+ {%- endfor %}
78
+ {%- if ns.multi_step_tool %}
79
+ {{- raise_exception('No user query found in messages.') }}
80
+ {%- endif %}
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+ {%- for message in messages %}
82
+ {%- set content = render_content(message.content, true)|trim %}
83
+ {%- if message.role == "system" %}
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+ {%- if not loop.first %}
85
+ {{- raise_exception('System message must be at the beginning.') }}
86
+ {%- endif %}
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+ {%- elif message.role == "user" %}
88
+ {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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+ {%- elif message.role == "assistant" %}
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+ {%- set reasoning_content = '' %}
91
+ {%- if message.reasoning_content is string %}
92
+ {%- set reasoning_content = message.reasoning_content %}
93
+ {%- else %}
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+ {%- if '</think>' in content %}
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+ {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
96
+ {%- set content = content.split('</think>')[-1].lstrip('\n') %}
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+ {%- endif %}
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+ {%- endif %}
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+ {%- set reasoning_content = reasoning_content|trim %}
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+ {%- if (preserve_thinking is defined and preserve_thinking is true) or (loop.index0 > ns.last_query_index) %}
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+ {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
102
+ {%- else %}
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+ {{- '<|im_start|>' + message.role + '\n' + content }}
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+ {%- endif %}
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+ {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
106
+ {%- for tool_call in message.tool_calls %}
107
+ {%- if tool_call.function is defined %}
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+ {%- set tool_call = tool_call.function %}
109
+ {%- endif %}
110
+ {%- if loop.first %}
111
+ {%- if content|trim %}
112
+ {{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
113
+ {%- else %}
114
+ {{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
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+ {%- endif %}
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+ {%- else %}
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+ {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
118
+ {%- endif %}
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+ {%- if tool_call.arguments is defined %}
120
+ {%- for args_name, args_value in tool_call.arguments|items %}
121
+ {{- '<parameter=' + args_name + '>\n' }}
122
+ {%- set args_value = args_value | string if args_value is string else args_value | tojson | safe %}
123
+ {{- args_value }}
124
+ {{- '\n</parameter>\n' }}
125
+ {%- endfor %}
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+ {%- endif %}
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+ {{- '</function>\n</tool_call>' }}
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+ {%- endfor %}
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+ {%- endif %}
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+ {{- '<|im_end|>\n' }}
131
+ {%- elif message.role == "tool" %}
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+ {%- if loop.previtem and loop.previtem.role != "tool" %}
133
+ {{- '<|im_start|>user' }}
134
+ {%- endif %}
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+ {{- '\n<tool_response>\n' }}
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+ {{- content }}
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+ {{- '\n</tool_response>' }}
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+ {%- if not loop.last and loop.nextitem.role != "tool" %}
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+ {{- '<|im_end|>\n' }}
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+ {%- elif loop.last %}
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+ {{- '<|im_end|>\n' }}
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+ {%- endif %}
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+ {%- else %}
144
+ {{- raise_exception('Unexpected message role.') }}
145
+ {%- endif %}
146
+ {%- endfor %}
147
+ {%- if add_generation_prompt %}
148
+ {{- '<|im_start|>assistant\n' }}
149
+ {%- if enable_thinking is defined and enable_thinking is false %}
150
+ {{- '<think>\n\n</think>\n\n' }}
151
+ {%- else %}
152
+ {{- '<think>\n' }}
153
+ {%- endif %}
154
+ {%- endif %}
config.json ADDED
@@ -0,0 +1,95 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "architectures": [
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+ "Qwen3_5MoeForCausalLM"
4
+ ],
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+ "attention_bias": false,
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+ "attention_dropout": 0.0,
7
+ "attn_output_gate": true,
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+ "bos_token_id": 248044,
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+ "dtype": "bfloat16",
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+ "eos_token_id": 248044,
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+ "full_attention_interval": 4,
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+ "head_dim": 256,
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+ "hidden_act": "silu",
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+ "hidden_size": 2048,
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+ "initializer_range": 0.02,
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+ "layer_types": [
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+ "linear_attention",
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+ "linear_attention",
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+ "linear_attention",
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+ "full_attention",
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+ "linear_attention",
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+ "linear_attention",
23
+ "linear_attention",
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+ "full_attention",
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+ "linear_attention",
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+ "linear_attention",
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+ "linear_attention",
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+ "full_attention",
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+ "linear_attention",
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+ "linear_attention",
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+ "linear_attention",
32
+ "full_attention",
33
+ "linear_attention",
34
+ "linear_attention",
35
+ "linear_attention",
36
+ "full_attention",
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+ "linear_attention",
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+ "linear_attention",
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+ "linear_attention",
40
+ "full_attention",
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+ "linear_attention",
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+ "linear_attention",
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+ "linear_attention",
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+ "full_attention",
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+ "linear_attention",
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+ "linear_attention",
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+ "linear_attention",
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+ "full_attention",
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+ "linear_attention",
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+ "linear_attention",
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+ "linear_attention",
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+ "full_attention",
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+ "linear_attention",
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+ "linear_attention",
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+ "linear_attention",
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+ "full_attention"
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+ ],
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+ "linear_conv_kernel_dim": 4,
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+ "linear_key_head_dim": 128,
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+ "linear_num_key_heads": 16,
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+ "linear_num_value_heads": 32,
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+ "linear_value_head_dim": 128,
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+ "mamba_ssm_dtype": "float32",
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+ "max_position_embeddings": 262144,
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+ "model_type": "qwen3_5_moe_text",
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+ "moe_intermediate_size": 512,
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+ "mtp_num_hidden_layers": 1,
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+ "mtp_use_dedicated_embeddings": false,
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+ "num_attention_heads": 16,
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+ "num_experts": 256,
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+ "num_experts_per_tok": 8,
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+ "num_hidden_layers": 40,
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+ "num_key_value_heads": 2,
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+ "output_router_logits": false,
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+ "pad_token_id": null,
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+ "partial_rotary_factor": 0.25,
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+ "rms_norm_eps": 1e-06,
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+ "rope_parameters": {
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+ "mrope_interleaved": true,
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+ "mrope_section": [
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+ 11,
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+ 11,
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+ 10
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+ "partial_rotary_factor": 0.25,
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+ "rope_theta": 10000000,
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+ "rope_type": "default"
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+ },
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+ "router_aux_loss_coef": 0.001,
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+ "shared_expert_intermediate_size": 512,
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+ "tie_word_embeddings": false,
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+ "transformers_version": "5.5.4",
93
+ "use_cache": true,
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+ "vocab_size": 248320
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+ }
generation_config.json ADDED
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