Instructions to use FINAL-Bench/POCKET-35B-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/POCKET-35B-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/POCKET-35B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf FINAL-Bench/POCKET-35B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FINAL-Bench/POCKET-35B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf FINAL-Bench/POCKET-35B-GGUF:Q4_K_M

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/POCKET-35B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf FINAL-Bench/POCKET-35B-GGUF:Q4_K_M

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/POCKET-35B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FINAL-Bench/POCKET-35B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/FINAL-Bench/POCKET-35B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use FINAL-Bench/POCKET-35B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/POCKET-35B-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/POCKET-35B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/POCKET-35B-GGUF:Q4_K_M

Ollama
How to use FINAL-Bench/POCKET-35B-GGUF with Ollama:
```
ollama run hf.co/FINAL-Bench/POCKET-35B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use FINAL-Bench/POCKET-35B-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/POCKET-35B-GGUF:Q4_K_M

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/POCKET-35B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use FINAL-Bench/POCKET-35B-GGUF with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/POCKET-35B-GGUF:Q4_K_M
```

Lemonade

How to use FINAL-Bench/POCKET-35B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FINAL-Bench/POCKET-35B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.POCKET-35B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use FINAL-Bench/POCKET-35B-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/POCKET-35B-GGUF:Q4_K_M

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/POCKET-35B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use FINAL-Bench/POCKET-35B-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/POCKET-35B-GGUF:Q4_K_M

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/POCKET-35B-GGUF:Q4_K_M" \
  --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"

🆕 POCKET-Qwen3.8-Flash-Next — a 180B model running on a laptop with 8 GB VRAM + 32 GB RAM · 4.17 tok/s measured.

🆕 POCKET-Zimage-CPU — photoreal images in 46 s on a CPU only. No GPU, no CUDA, no Python.

📚 Collections

▶ POCKET Models — this family (on-device, no GPU) Darwin Family · Aether Foundation · VKAE Accelerated

POCKET-35B-GGUF

A 35B model that runs on your PC with no GPU — and on your phone. Just stock `llama.cpp`. No fork, no CUDA, no cloud.

🚀 Try it live, no install → — both answering on a CPU-only box (no GPU). POCKET-26B is Gemma4-based.

Pick your build →

The POCKET lineup — pick by your device

Repo	File	Size	Runs on	Best for	Korean PPL*
POCKET-35B-GGUF	`Q4_K_M`	21 GB	PC / server (32 GB RAM)	top quality	5.79
POCKET-35B-GGUF	`Q2_K` ⭐	13 GB	mini-PC, no GPU	daily driver	6.49
POCKET-35B-GGUF	`IQ1_M`	8.2 GB	16 GB RAM box	smallest full model	9.69
POCKET-KR-GGUF	`IQ2_M`	5.1 GB	Android 8 GB+	🇰🇷 Korean phone	7.95
POCKET-KR-MLX	2-bit	5.1 GB	🍎 iPhone / iPad / Mac	🇰🇷 Korean, Apple-native	7.95
POCKET-EN-GGUF	`iPhone-mix`	5.3 GB	🍎 iPhone (PocketPal)	🌍 English phone	—
POCKET-EN-GGUF	`PC-mix`	6.8 GB	PC / Android	🌍 English, best quality	—
POCKET-Qwen3.8-Flash-Next-GGUF	`Q4_K_M`	111 GiB	💻 laptop, 8 GB VRAM + 32 GB RAM	180B on a laptop	6.03†

*Wikipedia-Korean perplexity, lower is better. Q4_K_M = 5.79 baseline. English builds are tuned on English; see each repo. †Separate 80-chunk run (40,960 tokens) on a different model — compare within a model, not across rows.

🍎 Why MLX for Korean but GGUF for English on iPhone? Apple-native MLX only does uniform quantization. Korean survives it; English needs our proprietary quantization, which only GGUF supports — so the English iPhone build ships as a GGUF you run with PocketPal. Honest, not lazy.

🆕 POCKET-26B — a Gemma4-26B-A4B-based sibling that loads in any app today (Ollama · LM Studio · PocketPal · MLX), no bleeding-edge runtime needed: GGUF (Q2_K 11 GB · Q4_K_M 17 GB · GPQA-Diamond 67%). Universal compatibility for 12 GB phones, PC, and browser.

Benchmarks — what is measured, what is not

We measure Bonsai on the same machine with the same stock llama.cpp, and we tell you where we lose.

[measured] Generation speed — POCKET wins on both CPU and GPU:

	POCKET-35B IQ1_M	Bonsai-27B Q1_0
CPU generate (Xeon, 16t)	27.0 tok/s	10.1	🟢 2.69×
GPU generate (H100)	197 tok/s	89	🟢 2.22×
GPU prompt (H100)	753	1816	🔴 0.41×
Quality (HellaSwag, 400q)	61.0%	60.0%	⚪ tie (CI overlaps)

[measured on a MacBook M3 Pro, 18 GB] — and on a laptop, POCKET wins every axis, including prompt processing:

	POCKET-35B IQ1_M	Bonsai-27B Q1_0
Metal generate (tg64)	25.4 tok/s	12.8	🟢 1.99×
CPU generate (8 threads)	13.8 tok/s	4.4	🟢 3.13×
Metal prompt (pp128)	240.7 tok/s	73.4	🟢 3.28×
CPU prompt (pp128)	45.5 tok/s	9.6	🟢 4.75×

On a laptop GPU the arithmetic headroom that let Bonsai win prefill on an H100 is gone, so MoE sparsity wins across the board. POCKET-35B-Q2_K runs on the M3 Pro's CPU at 19.5 tok/s — on an 18 GB Mac, run Q2_K on CPU (-ngl 0); its 13 GB exceeds the recommended Metal budget.

[measured — GPQA Diamond, 198q, greedy] reasoning quality vs quantization:

Model	GPQA-Diamond (greedy)
Qwen3.6-35B-A3B	73.2%
POCKET-35B Q4_K_M	68.7%
POCKET-35B Q2_K	60.1%

[pending — community reports welcome] on-device iPhone and Strix Halo throughput. We publish only what we ran ourselves; help us fill the rest.

The same-size rival Ternary-Bonsai-27B-Q2_0 (7.2 GB) fails to load in upstream llama.cpp — it needs the PrismML fork. POCKET runs on the tools you already have.

Files in this repo

File	Size	bpw	Runs on	Korean PPL
`POCKET-35B-Q4_K_M.gguf`	21 GB	4.5	PC 32 GB RAM	5.79 (top)
`POCKET-35B-Q3_K_M.gguf`	16 GB	3.4	PC 24 GB	6.06
`POCKET-35B-Q2_K.gguf` ⭐	13 GB	2.6	mini-PC 16–24 GB	6.49 (best value)
`POCKET-35B-IQ1_M.gguf`	8.2 GB	1.9	16 GB RAM	9.69 (smallest)

Quickstart — no fork needed

# any recent llama.cpp — brew / winget / apt, or LM Studio / Ollama
llama-cli -m POCKET-35B-Q2_K.gguf -p "안녕하세요" -ngl 0 -t 8
# reproduce our CPU numbers:
llama-bench -m POCKET-35B-IQ1_M.gguf -p 128 -n 64 -ngl 0 -t 16

Use physical-core count for -t (max ~32). Do not pass all threads — it can slow down sharply.

Lineage — where POCKET comes from

POCKET is quantized from Darwin-36B-Opus, VIDRAFT's flagship — a model bred and evolved over several generations on the Darwin platform (crossbreeding, healing, expert surgery). Darwin-36B-Opus itself traces back to a Qwen3.5-family MoE architecture.

Component	Origin
Starting checkpoint	Darwin-36B-Opus — VIDRAFT, multi-generation Darwin evolution
Base architecture	Qwen3.5-family MoE (256 experts, top-8), unchanged
Quantization (`Q4_K_M`…`IQ1_M`)	stock llama.cpp — no custom format
Runtime	upstream llama.cpp / Apple MLX — unmodified
Proprietary language-specific tuning (KR/EN builds)	ours (VIDRAFT)

The CPU/GPU speed comes from the sparse-MoE architecture plus ordinary quantization — reproducible with the same base and the same tools. What we add is the Darwin-evolved weights, the honest measurement, the Korean tuning, and the pruning that makes the 5 GB phone builds.

Limitations

The iPhone/Mac speed is not yet measured by us — community reports welcome.
Extreme quants (IQ1_M) hurt Korean ~2.8× more than English; use Q2_K or larger for quality.
English phone builds trade quality for size; the PC build (PC-mix) is much closer to full quality.

License

Apache-2.0.

POCKET is a VIDRAFT model family. 35B, in your pocket. No GPU.

Learn more

On-device LLMs without a GPU — and how POCKET measures up: Can you run a large LLM without a GPU?
What model quantization is, and why a 4-bit model stays smart: What is model quantization?

🧩 The POCKET Family — On-device AI by VIDRAFT

Big models, small hardware. No GPU, no cloud.

Models

📦 POCKET-35B-GGUF — flagship, PC / server, no GPU
📦 POCKET-26B-GGUF — compact 26B
🇰🇷 POCKET-KR-GGUF — Korean, Android
🍎 POCKET-KR-MLX — Korean, iPhone / Mac
🌍 POCKET-EN-GGUF — English, phone / PC
💻 POCKET-Qwen3.8-Flash-Next-GGUF — 180B on a laptop (8 GB VRAM + 32 GB RAM)
🖼️ POCKET-Image-Zimage — character-perfect text in any image
🖥️ POCKET-Zimage-CPU — photoreal images on a CPU only

Demos & tools (Spaces)

🎨 POCKET-Image Studio — text-in-image, generate in-page
🖥️ POCKET-35B-CPU — 35B answering on a CPU
🖥️ POCKET-26B-CPU — 26B on a CPU
🖼️ POCKET-Zimage-CPU — image generation on a CPU

📚 Full POCKET collection

Downloads last month: 748,327

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

Model tree for FINAL-Bench/POCKET-35B-GGUF

Base model

FINAL-Bench/Darwin-36B-Opus

Quantized

(18)

this model

Spaces using FINAL-Bench/POCKET-35B-GGUF 4

Collection including FINAL-Bench/POCKET-35B-GGUF

POCKET-MODELs

Collection

12 items • Updated about 7 hours ago • 42

Article mentioning FINAL-Bench/POCKET-35B-GGUF

POCKET: a 35-billion-parameter model that runs on your iPhone — and on your PC with no GPU

FINAL-Bench
/

POCKET-35B-GGUF

🆕 POCKET-Qwen3.8-Flash-Next — a 180B model running on a laptop with 8 GB VRAM + 32 GB RAM · 4.17 tok/s measured.

🆕 POCKET-Zimage-CPU — photoreal images in 46 s on a CPU only. No GPU, no CUDA, no Python.

📚 Collections

POCKET-35B-GGUF

A 35B model that runs on your PC with no GPU — and on your phone. Just stock `llama.cpp`. No fork, no CUDA, no cloud.

The POCKET lineup — pick by your device

Benchmarks — what is measured, what is not

Files in this repo

Quickstart — no fork needed

Lineage — where POCKET comes from

Limitations

License

Learn more

🧩 The POCKET Family — On-device AI by VIDRAFT

Model tree for FINAL-Bench/POCKET-35B-GGUF

Spaces using FINAL-Bench/POCKET-35B-GGUF 4

Collection including FINAL-Bench/POCKET-35B-GGUF

POCKET-MODELs

Article mentioning FINAL-Bench/POCKET-35B-GGUF

POCKET: a 35-billion-parameter model that runs on your iPhone — and on your PC with no GPU

🆕 POCKET-Qwen3.8-Flash-Next — a 180B model running on a laptop with 8 GB VRAM + 32 GB RAM · 4.17 tok/s measured.

🆕 POCKET-Zimage-CPU — photoreal images in 46 s on a CPU only. No GPU, no CUDA, no Python.

📚 Collections

POCKET-35B-GGUF

A 35B model that runs on your PC with no GPU — and on your phone. Just stock llama.cpp. No fork, no CUDA, no cloud.

The POCKET lineup — pick by your device

Benchmarks — what is measured, what is not

Files in this repo

Quickstart — no fork needed

Lineage — where POCKET comes from

Limitations

License

Learn more

🧩 The POCKET Family — On-device AI by VIDRAFT

Model tree for FINAL-Bench/POCKET-35B-GGUF

Spaces using FINAL-Bench/POCKET-35B-GGUF 4

Collection including FINAL-Bench/POCKET-35B-GGUF

Article mentioning FINAL-Bench/POCKET-35B-GGUF

POCKET: a 35-billion-parameter model that runs on your iPhone — and on your PC with no GPU

A 35B model that runs on your PC with no GPU — and on your phone. Just stock `llama.cpp`. No fork, no CUDA, no cloud.