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Darwin-35B-A3B-Opus-Q8-GGUF

Text Generation
GGUF
qwen3_5_moe
llama-cpp
quantized
Q8_0
Merge
evolutionary-merge
darwin
darwin-v5
reasoning
qwen3.5
qwen
Mixture of Experts
mixture-of-experts
claude-opus
distillation
gpqa
open-source
apache-2.0
layer-wise-merge
coding-agent
tool-calling
long-context
262k-context
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metadata 
license: apache-2.0
base_model:
  - FINAL-Bench/Darwin-35B-A3B-Opus
tags:
  - llama-cpp
  - gguf
  - quantized
  - Q8_0
  - merge
  - evolutionary-merge
  - darwin
  - darwin-v5
  - reasoning
  - qwen3.5
  - qwen
  - moe
  - mixture-of-experts
  - claude-opus
  - distillation
  - multilingual
  - gpqa
  - open-source
  - apache-2.0
  - layer-wise-merge
  - coding-agent
  - tool-calling
  - long-context
  - 262k-context
language:
  - en
  - zh
  - ko
  - ja
  - de
  - fr
  - es
  - ru
  - ar
  - multilingual
pipeline_tag: text-generation
library_name: gguf
quantized_by: VIDRAFT
model-index:
  - name: Darwin-35B-A3B-Opus-Q8_0-GGUF
    results:
      - task:
          type: text-generation
          name: Graduate-Level Reasoning
        dataset:
          type: Idavidrein/gpqa
          name: GPQA Diamond
          config: gpqa_diamond
          split: train
        metrics:
          - type: accuracy
            value: 90
            name: Accuracy
            verified: false
      - task:
          type: text-generation
          name: Multilingual Knowledge
        dataset:
          type: openai/MMMLU
          name: MMMLU
        metrics:
          - type: accuracy
            value: 85
            name: Accuracy
            verified: false

Darwin-35B-A3B-Opus-Q8_0-GGUF

Gen1 Gen2 Gen3

9B 9B Space 31B 31B Space

35B 35B Space Q8 GGUF bartowski GGUF

FINAL Bench ALL Bench

Q8_0 GGUF of Darwin-35B-A3B-Opus | ~37GB (3 shards) | GPQA Diamond 90.0% | Near-lossless quality | MoE 35B (3B active) | 201 Languages | 262K Context | Apache 2.0

About This Quantization

Q8_0 GGUF of FINAL-Bench/Darwin-35B-A3B-Opus.

Original (BF16) This Model (Q8_0 GGUF)
Format SafeTensors GGUF
Size 65.5 GB ~37 GB (3 shards)
Quality Baseline Near-lossless (~99.9% of BF16)
VRAM Required 65+ GB ~37 GB
Runs on H100, A100 80GB A100 40GB, Mac 64GB, 2x RTX 4090
Framework Transformers, vLLM, SGLang llama.cpp, Ollama, LM Studio

Files

File Size Description
darwin-35b-a3b-opus-q8_0-00001-of-00003.gguf ~13.6 GB Shard 1 of 3
darwin-35b-a3b-opus-q8_0-00002-of-00003.gguf ~12.5 GB Shard 2 of 3
darwin-35b-a3b-opus-q8_0-00003-of-00003.gguf ~10.7 GB Shard 3 of 3
Total ~36.8 GB All 3 shards required

Download all 3 shard files. llama.cpp and Ollama will automatically load them together.

Hardware Requirements

Setup Memory Status
NVIDIA A100 40GB 40 GB VRAM Fits
NVIDIA A100 80GB 80 GB VRAM Comfortable
NVIDIA H100 93GB 93 GB VRAM Comfortable
2x RTX 4090 (24GB each) 48 GB VRAM With tensor parallel
Mac Studio M2/M3 Ultra 64GB 64 GB Unified Fits
Mac M3 Max 48GB 48 GB Unified Fits
Single RTX 4090 24GB 24 GB VRAM Insufficient (use Q4_K_M)

As a MoE model, only 3B parameters are active per token. Inference is fast despite the 37GB model size.

Usage

llama.cpp (CLI) 

llama-cli \
  --hf-repo FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF \
  --hf-file darwin-35b-a3b-opus-q8_0-00001-of-00003.gguf \
  -p "The meaning to life and the universe is" \
  -n 512 -ngl 99

llama.cpp (Server) 

llama-server \
  --hf-repo FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF \
  --hf-file darwin-35b-a3b-opus-q8_0-00001-of-00003.gguf \
  -c 32768 -ngl 99

Ollama 

echo 'FROM ./darwin-35b-a3b-opus-q8_0-00001-of-00003.gguf' > Modelfile
ollama create darwin-opus -f Modelfile
ollama run darwin-opus

LM Studio

  1. Download all 3 .gguf shard files
  2. Place them in the same folder
  3. Open LM Studio, load the first shard
  4. LM Studio auto-detects and loads all shards

MoE Expert Offload (Limited VRAM) 

llama-cli \
  --hf-repo FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF \
  --hf-file darwin-35b-a3b-opus-q8_0-00001-of-00003.gguf \
  -ot ".ffn_.*_exps.=CPU" \
  -ngl 99 -c 32768

Benchmark Results (Original Model)

Q8_0 preserves near-identical performance to BF16.

GPQA Diamond (198 Questions, Graduate-Level Reasoning)

Model Accuracy
Darwin-35B-A3B-Opus 90.0%
Mother (Jackrong Claude 4.6 Opus Distilled) 85.0%
Father (Qwen3.5-35B-A3B Official) 84.2%

MMMLU (Multilingual Knowledge, 29 Languages)

Model Accuracy
Darwin-35B-A3B-Opus 85.0%
Father (Qwen3.5-35B-A3B Official) 85.2%

How Darwin Was Created

Darwin-35B-A3B-Opus was created using Darwin V5, a diagnostic-guided evolutionary merge engine built on mergekit.

Both parent models share the identical Qwen3.5-35B-A3B architecture. The Mother is a LoRA SFT on the same base — not a different architecture.

Darwin V5 adds three phases over standard mergekit evolve:

  1. Pre-merge parent profiling (40 layers x 256 experts: activation frequency, routing entropy, probe cosine distance)
  2. Evolution with diagnostic-informed initial population and constrained search space
  3. Post-merge child validation (layer-by-layer comparison against both parents)

Key diagnostic finding: Mother had 50-65% dead experts (activation < 5%) from text-only LoRA SFT. Darwin compensated by reducing Mother density and using Father's living experts to fill inactive slots.

Merge configuration:

# Method: DARE-TIES via mergekit
L0-L37:  t=0.5988 (Mother 60%) — router from Mother
L38:     t=0.9000 (Mother 90%) — reasoning core (peak probe cosine distance)
L39:     t=0.5336 (Father 47%) — router from Father (output routing)

For full technical details, diagnostics, and health check results, see the original model card.

Other Quantizations

Quantization Size Quality Use Case
Q8_0 (this) ~37 GB Near-lossless Maximum quality
Q4_K_M (coming soon) ~20 GB Good RTX 4090, Mac 32GB

Model Specifications

Base Model FINAL-Bench/Darwin-35B-A3B-Opus
Architecture Qwen3.5 MoE (Gated DeltaNet + MoE)
Total Parameters 35B
Active Parameters 3B per forward pass
Experts 256 (8 routed + 1 shared active)
Context Length 262,144 native
Languages 201
Quantization Q8_0 (8-bit integer)
GGUF Shards 3 files
License Apache 2.0
Quantized by VIDRAFT via llama.cpp

Acknowledgements

  • Korean Government — GPU Support Program research grant
  • Qwen Team — Qwen3.5-35B-A3B base architecture
  • Jackrong — Claude 4.6 Opus Reasoning Distilled model
  • mergekit — Merge backend infrastructure
  • llama.cpp — GGUF conversion and quantization

Citation 

@misc{vidraft_darwin_35b_opus_gguf,
  title        = {Darwin-35B-A3B-Opus-Q8_0-GGUF},
  author       = {VIDRAFT},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF}}
}