FINAL-Bench

Ourbox-35B-JGOS

README

License: apache-2.0

Abstract

Ourbox-35B-JGOS is a 35-billion-parameter mixture-of-experts (MoE) reasoning model produced by the Darwin evolutionary breeding platform (FINAL-Bench / VIDRAFT_LAB). Rather than retraining from scratch, Darwin recombines the feed-forward (FFN / MoE expert) tensors of the Qwen3.6-35B-A3B backbone with those of additional specialized donor models, then evolves the merged descendant toward a target objective — here, Korean-language specialization.

Because the merge operates at the expert-FFN level, Ourbox inherits complementary domain and language competencies from multiple sources while preserving the backbone's hybrid-attention topology and 262K long-context behavior. The result is a Korean-specialized reasoning model that remains highly competitive on English graduate-level science: on GPQA Diamond (198 questions across physics, chemistry, biology), Ourbox-35B-JGOS scores 86.36% (171/198) under a majority-of-8+ protocol. On Hugging Face's live GPQA leaderboard this improves on its own Qwen3.6-35B-A3B backbone (86.0) by +0.36 points and edges past GLM-5.1 (86.2) and GLM-5 (86.0) — with only ~3B active parameters.

GPQA Diamond Leaderboard — Hugging Face `Idavidrein/gpqa` (2026-07-11)

Ourbox-35B-JGOS on the official Hugging Face GPQA Diamond leaderboard (Idavidrein/gpqa, base-model view, 50 models). FINAL-Bench models in bold:

Table with columns: #, Model, GPQA Diamond
#	Model	GPQA Diamond
1	zai-org/GLM-5.2	91.2
2	FINAL-Bench/Darwin-398B-JGOS	90.9
3	moonshotai/Kimi-K2.6	90.5
4	tencent/Hy3	90.4
5	deepseek-ai/DeepSeek-V4-Pro	90.1
6	FINAL-Bench/Darwin-28B-REASON	89.39
7	Qwen/Qwen3.5-397B-A17B	88.4

The FINAL-Bench Darwin family dominates the upper board — 5 of the 20 models ranked above Ourbox are Darwin models (Darwin-398B-JGOS #2, Darwin-28B-REASON #6, Darwin-36B-Opus #8, Darwin-60B-DUO #9, Darwin-27B-Opus #19). At 86.36%, Ourbox-35B-JGOS ranks #21 of 50 on the live leaderboard and — most notably — improves on its own Qwen3.6-35B-A3B backbone (86.0, #24) by +0.36 points, confirming that the Darwin FFN-merge and Korean specialization added capability rather than eroding it. It also edges past GLM-5.1 (86.2, #22) and GLM-5 (86.0, #23) while activating only ~3B parameters.

Ranks reflect the live leaderboard as of 2026-07-11 (which counts quantized/duplicate entries); positions shift as it updates. Ourbox-35B-JGOS is live and listed at #21.

What Is Darwin?

Darwin is the evolutionary model-breeding platform 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 — with a particular focus on the FFN / MoE expert subspace, where domain and language competence is concentrated.

At a high level, the platform performs:

Per-tensor compatibility analysis across the backbone and donor models to identify which FFN experts and components transfer cleanly and which require weighted recombination.
FFN-level merge & evolution — the descendant's expert tensors are assembled from the pool and iteratively evolved toward a target objective (Korean specialization for Ourbox).
Verification via a multi-phase scientific benchmark before release.

Specific algorithmic details of the Darwin engine are proprietary to FINAL-Bench. All Darwin models are released under the base model's open-source license (Apache 2.0).

JGOS is the reasoning-model line built with Darwin; Ourbox is its Korean-specialized 35B-A3B member.

Evolution Process

Ourbox-35B-JGOS is bred, not trained:

Backbone: Qwen/Qwen3.6-35B-A3B — the foundation MoE, contributing its hybrid-attention topology (¾ linear + ¼ full), 256-expert routing, MTP head, and 262K context.
FFN donors: additional specialized models whose feed-forward / expert tensors are recombined into the backbone by the Darwin engine, contributing complementary domain and Korean-language competence.
Evolution objective: Korean specialization — the evolutionary selection biases the merged expert population toward stronger Korean reasoning and generation, while structural and long-context behavior is inherited intact from the backbone.

The merge operates without gradient optimization on the final assembly; a deployable bfloat16 checkpoint is produced by the Darwin pipeline directly.

🇰🇷 Korean Specialization

Ourbox-35B-JGOS is specialized for Korean. The Darwin evolutionary process selects and recombines FFN experts to strengthen Korean-language reasoning, comprehension, and generation — targeting natural Korean output, robust handling of Korean scientific/technical text, and reduced character-level corruption on large Korean inputs.

Crucially, this specialization does not come at the cost of general capability: the model's 86.36% GPQA Diamond (in English) improves on its own Qwen3.6-35B-A3B backbone (86.0) on Hugging Face's live leaderboard, evidence that the FFN-merge preserved scientific reasoning depth while adding Korean strength. The model remains fully multilingual (Korean-first), with English, Chinese, Japanese, and other languages inherited from the backbone.

Architecture

Ourbox-35B-JGOS retains the full Qwen3.6-35B-A3B architecture (qwen3_5_moe codebase):

Table

Foundation	Qwen3.6-35B-A3B (`Qwen3_5MoeForCausalLM`)
Breeding platform	Darwin (FFN-level evolutionary merge)
Total parameters	~35 B
Active parameters	~3 B (top-8 of 256 routed experts per layer)
Layers	40
Hidden size	2048
Attention	Hybrid — 30 linear-attention + 10 full-attention layers (`full_attention_interval = 4`)
Full-attention heads

The hybrid attention design (¾ linear + ¼ full) gives near-linear KV-cache scaling across the 262K window, and the Multi-Token Prediction head provides a built-in draft for speculative decoding.

GPQA Diamond Evaluation

Methodology

Ourbox-35B-JGOS was evaluated on all 198 GPQA Diamond questions using a two-pass majority-of-8+ protocol (identical to sibling FINAL-Bench reasoning models, for cross-model comparability):

Pass 1 — Greedy baseline

All 198 questions, deterministic decoding (do_sample=False)
Up to 5,120 new tokens per question (full <think> trajectories)
Standard multiple-choice prompt format

Pass 2 — Stochastic majority vote with tiebreaker

Each question is answered by 8 independent stochastic generations (temperature=0.7, max_tokens=5120); the majority answer is taken
Where the 8-vote margin is inconclusive (e.g. 3:3 / 3:4 / 4:4), an additional 16-vote tiebreaker round (temperature=0.5) resolves the answer

The final answer for each question is extracted after the </think> delimiter.

Result

Table with columns: Metric, Value
Metric	Value
Correct	171 / 198
GPQA Diamond accuracy (maj@8+)	86.36%

Evaluated against the Idavidrein/gpqa gpqa_diamond split. The majority-of-8+ protocol surfaces answers that greedy decoding leaves subdominant — a pattern characteristic of well-formed chain-of-thought models — carrying Ourbox above its Qwen3.6-35B-A3B backbone (86.0) and past GLM-5.1 (86.2) on graduate-level science.

Usage

python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tok = AutoTokenizer.from_pretrained("FINAL-Bench/Ourbox-35B-JGOS", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "FINAL-Bench/Ourbox-35B-JGOS",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {"role": "user", "content": "상대론적 운동에너지 식을 유도해줘."}
]
text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=5120, temperature=0.6, do_sample=True)
print(tok.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Answer extraction for evaluations

This is a thinking model — responses always begin with a <think> reasoning trace. For benchmarks, extract the final answer after </think>:

python
response = tok.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
idx = response.rfind("</think>")
answer_part = response[idx + len("</think>"):].strip() if idx >= 0 else response

Recommended settings

Temperature: 0.6–0.7 for reasoning / majority voting; 0.0 for greedy deterministic
max_new_tokens: ≥5120 to accommodate full <think> trajectories
Chat template: assistant turn opens with <think> when apply_chat_template(add_generation_prompt=True) is used

VRAM Requirements

Table with columns: Precision, VRAM, Recommended GPU
Precision	VRAM	Recommended GPU
bf16 (full)	~72 GB	1× H100 80GB / 1× B200
8-bit	~40 GB	1× A100 40GB+ / 1× L40S
4-bit	~22 GB	1× RTX 4090 / 1× A10

Key Findings

Korean specialization without capability loss. Darwin's FFN-level merge adds Korean-language strength while retaining 86.36% GPQA Diamond — above the model's own Qwen3.6-35B-A3B backbone (86.0). Specialization and general reasoning are not a zero-sum trade under expert-level recombination.
Specialization improves on the backbone. On Hugging Face's live GPQA Diamond leaderboard, Ourbox (86.36) exceeds its own Qwen3.6-35B-A3B backbone (86.0) and edges past GLM-5.1 (86.2) and GLM-5 (86.0) — the Darwin FFN-merge added Korean capability without eroding scientific reasoning, at ~3B active parameters.
Breeding beats retraining for specialization. A deployable, Korean-specialized 35B checkpoint is produced by evolutionary FFN recombination — no full-model gradient training on the final assembly — demonstrating Darwin as an efficient route to targeted, high-capability models.

References

Rein et al., GPQA: A Graduate-Level Google-Proof Q&A Benchmark, 2024. dataset
Qwen Team, Qwen3.6 Technical Report, 2026.

Built By

FINAL-Bench / VIDRAFT_LAB — Darwin evolutionary breeding platform, JGOS Korean-specialized reasoning line. Backbone weights by the Qwen Team (Qwen3.6-35B-A3B). Released under Apache 2.0.

Citation

bibtex
@misc{ourbox-35b-jgos,
  title   = {Ourbox-35B-JGOS: Korean-Specialized, Darwin-Evolved 35B-A3B Reasoning MoE},
  author  = {FINAL-Bench and VIDRAFT_LAB},
  year    = {2026},
  url     = {https://huggingface.co/FINAL-Bench/Ourbox-35B-JGOS},
  note    = {Qwen3.6-35B-A3B backbone, Darwin FFN-level evolutionary merge, Korean-specialized, 86.36% GPQA Diamond (maj@8+)}
}

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README

License: apache-2.0

Abstract

GPQA Diamond Leaderboard — Hugging Face `Idavidrein/gpqa` (2026-07-11)

Ourbox-35B-JGOS on the official Hugging Face GPQA Diamond leaderboard (Idavidrein/gpqa, base-model view, 50 models). FINAL-Bench models in bold:

Table with columns: #, Model, GPQA Diamond
#	Model	GPQA Diamond
1	zai-org/GLM-5.2	91.2
2	FINAL-Bench/Darwin-398B-JGOS	90.9
3	moonshotai/Kimi-K2.6	90.5
4	tencent/Hy3	90.4
5	deepseek-ai/DeepSeek-V4-Pro	90.1
6	FINAL-Bench/Darwin-28B-REASON	89.39
7	Qwen/Qwen3.5-397B-A17B	88.4

Ranks reflect the live leaderboard as of 2026-07-11 (which counts quantized/duplicate entries); positions shift as it updates. Ourbox-35B-JGOS is live and listed at #21.

What Is Darwin?

At a high level, the platform performs:

Per-tensor compatibility analysis across the backbone and donor models to identify which FFN experts and components transfer cleanly and which require weighted recombination.
FFN-level merge & evolution — the descendant's expert tensors are assembled from the pool and iteratively evolved toward a target objective (Korean specialization for Ourbox).
Verification via a multi-phase scientific benchmark before release.

Specific algorithmic details of the Darwin engine are proprietary to FINAL-Bench. All Darwin models are released under the base model's open-source license (Apache 2.0).

JGOS is the reasoning-model line built with Darwin; Ourbox is its Korean-specialized 35B-A3B member.

Evolution Process

Ourbox-35B-JGOS is bred, not trained:

Backbone: Qwen/Qwen3.6-35B-A3B — the foundation MoE, contributing its hybrid-attention topology (¾ linear + ¼ full), 256-expert routing, MTP head, and 262K context.
FFN donors: additional specialized models whose feed-forward / expert tensors are recombined into the backbone by the Darwin engine, contributing complementary domain and Korean-language competence.
Evolution objective: Korean specialization — the evolutionary selection biases the merged expert population toward stronger Korean reasoning and generation, while structural and long-context behavior is inherited intact from the backbone.

The merge operates without gradient optimization on the final assembly; a deployable bfloat16 checkpoint is produced by the Darwin pipeline directly.

🇰🇷 Korean Specialization

Architecture

Ourbox-35B-JGOS retains the full Qwen3.6-35B-A3B architecture (qwen3_5_moe codebase):

Table

Foundation	Qwen3.6-35B-A3B (`Qwen3_5MoeForCausalLM`)
Breeding platform	Darwin (FFN-level evolutionary merge)
Total parameters	~35 B
Active parameters	~3 B (top-8 of 256 routed experts per layer)
Layers	40
Hidden size	2048
Attention	Hybrid — 30 linear-attention + 10 full-attention layers (`full_attention_interval = 4`)
Full-attention heads

GPQA Diamond Evaluation

Methodology

Ourbox-35B-JGOS was evaluated on all 198 GPQA Diamond questions using a two-pass majority-of-8+ protocol (identical to sibling FINAL-Bench reasoning models, for cross-model comparability):

Pass 1 — Greedy baseline

All 198 questions, deterministic decoding (do_sample=False)
Up to 5,120 new tokens per question (full <think> trajectories)
Standard multiple-choice prompt format

Pass 2 — Stochastic majority vote with tiebreaker

Each question is answered by 8 independent stochastic generations (temperature=0.7, max_tokens=5120); the majority answer is taken
Where the 8-vote margin is inconclusive (e.g. 3:3 / 3:4 / 4:4), an additional 16-vote tiebreaker round (temperature=0.5) resolves the answer

The final answer for each question is extracted after the </think> delimiter.

Result

Table with columns: Metric, Value
Metric	Value
Correct	171 / 198
GPQA Diamond accuracy (maj@8+)	86.36%

Usage

python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tok = AutoTokenizer.from_pretrained("FINAL-Bench/Ourbox-35B-JGOS", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "FINAL-Bench/Ourbox-35B-JGOS",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {"role": "user", "content": "상대론적 운동에너지 식을 유도해줘."}
]
text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=5120, temperature=0.6, do_sample=True)
print(tok.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Answer extraction for evaluations

This is a thinking model — responses always begin with a <think> reasoning trace. For benchmarks, extract the final answer after </think>:

python
response = tok.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
idx = response.rfind("</think>")
answer_part = response[idx + len("</think>"):].strip() if idx >= 0 else response

Recommended settings

Temperature: 0.6–0.7 for reasoning / majority voting; 0.0 for greedy deterministic
max_new_tokens: ≥5120 to accommodate full <think> trajectories
Chat template: assistant turn opens with <think> when apply_chat_template(add_generation_prompt=True) is used

VRAM Requirements

Table with columns: Precision, VRAM, Recommended GPU
Precision	VRAM	Recommended GPU
bf16 (full)	~72 GB	1× H100 80GB / 1× B200
8-bit	~40 GB	1× A100 40GB+ / 1× L40S
4-bit	~22 GB	1× RTX 4090 / 1× A10

Key Findings

Korean specialization without capability loss. Darwin's FFN-level merge adds Korean-language strength while retaining 86.36% GPQA Diamond — above the model's own Qwen3.6-35B-A3B backbone (86.0). Specialization and general reasoning are not a zero-sum trade under expert-level recombination.
Specialization improves on the backbone. On Hugging Face's live GPQA Diamond leaderboard, Ourbox (86.36) exceeds its own Qwen3.6-35B-A3B backbone (86.0) and edges past GLM-5.1 (86.2) and GLM-5 (86.0) — the Darwin FFN-merge added Korean capability without eroding scientific reasoning, at ~3B active parameters.
Breeding beats retraining for specialization. A deployable, Korean-specialized 35B checkpoint is produced by evolutionary FFN recombination — no full-model gradient training on the final assembly — demonstrating Darwin as an efficient route to targeted, high-capability models.

References

Rein et al., GPQA: A Graduate-Level Google-Proof Q&A Benchmark, 2024. dataset
Qwen Team, Qwen3.6 Technical Report, 2026.

Built By

FINAL-Bench / VIDRAFT_LAB — Darwin evolutionary breeding platform, JGOS Korean-specialized reasoning line. Backbone weights by the Qwen Team (Qwen3.6-35B-A3B). Released under Apache 2.0.

Citation

bibtex
@misc{ourbox-35b-jgos,
  title   = {Ourbox-35B-JGOS: Korean-Specialized, Darwin-Evolved 35B-A3B Reasoning MoE},
  author  = {FINAL-Bench and VIDRAFT_LAB},
  year    = {2026},
  url     = {https://huggingface.co/FINAL-Bench/Ourbox-35B-JGOS},
  note    = {Qwen3.6-35B-A3B backbone, Darwin FFN-level evolutionary merge, Korean-specialized, 86.36% GPQA Diamond (maj@8+)}
}

Learn more

On-device, sovereign LLMs without a GPU: Can you run a large LLM without a GPU?

Ourbox-35B-JGOS

README

Abstract

GPQA Diamond Leaderboard — Hugging Face Idavidrein/gpqa (2026-07-11)

What Is Darwin?

Evolution Process

🇰🇷 Korean Specialization

Architecture

GPQA Diamond Evaluation

Methodology

Result

Usage

Answer extraction for evaluations

Recommended settings

VRAM Requirements

Key Findings

References

Built By

Citation

Learn more

Explore FriendliAI today

README

Abstract

GPQA Diamond Leaderboard — Hugging Face Idavidrein/gpqa (2026-07-11)

What Is Darwin?

Evolution Process

🇰🇷 Korean Specialization

Architecture

GPQA Diamond Evaluation

Methodology

Result

Usage

Answer extraction for evaluations

Recommended settings

VRAM Requirements

Key Findings

References

Built By

Citation

Learn more

GPQA Diamond Leaderboard — Hugging Face `Idavidrein/gpqa` (2026-07-11)

GPQA Diamond Leaderboard — Hugging Face `Idavidrein/gpqa` (2026-07-11)