Instructions to use FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF:Q8_0

Use Docker

docker model run hf.co/FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF:Q8_0

Ollama
How to use FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF with Ollama:
```
ollama run hf.co/FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF:Q8_0
```

Unsloth Studio

How to use FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF to start chatting

How to use FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF:Q8_0
```

Lemonade

How to use FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF:Q8_0

Run and chat with the model

lemonade run user.Darwin-35B-A3B-Opus-Q8-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF:Q8_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

📱 Run it on your phone or a GPU-less PC → POCKET · 🚀 Try it live (CPU chat)

VIDRAFT's on-device family: a 35B model that runs on iPhone and on CPU with no GPU — stock llama.cpp, no fork.

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

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

Download all 3 .gguf shard files
Place them in the same folder
Open LM Studio, load the first shard
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:

Pre-merge parent profiling (40 layers x 256 experts: activation frequency, routing entropy, probe cosine distance)
Evolution with diagnostic-informed initial population and constrained search space
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}}
}

This model is introduced in Darwin Family.