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-35B-GGUF / README.md

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	---
	license: apache-2.0
	library_name: llama.cpp
	pipeline_tag: text-generation
	base_model:
	- FINAL-Bench/Darwin-36B-Opus
	tags:
	- gguf
	- llama.cpp
	- conversational
	- on-device
	- mobile
	- iphone
	- android
	- cpu
	- local-llm
	- edge
	- mixture-of-experts
	- moe
	- quantized
	- pocket
	- vidraft
	- qwen3_5_moe
	- darwin
	---
	> ### 📚 Collections
	> ▶ [POCKET Models](https://huggingface.co/collections/FINAL-Bench/pocket-models-6a618ee5d23eafb7e185a5c6) — this family (on-device, no GPU)
	> [Darwin Family](https://huggingface.co/collections/FINAL-Bench/darwin-family-699987b1f652864af0122193) · [Aether Foundation](https://huggingface.co/collections/FINAL-Bench/aether-foundation-model-6a5c7f2fa1a4165c0414e53a) · [VKAE Accelerated](https://huggingface.co/collections/FINAL-Bench/vkae-accelerated-6a47231d7e7999dd8227675a)

	![POCKET](./pocket_hero.svg)

	# 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 → [![POCKET-35B demo](https://img.shields.io/badge/🤗_Space-POCKET--35B_CPU_chat-ffce3a)](https://huggingface.co/spaces/FINAL-Bench/POCKET-35B-CPU) [![POCKET-26B demo](https://img.shields.io/badge/🤗_Space-POCKET--26B_CPU_chat-0f9d6e)](https://huggingface.co/spaces/FINAL-Bench/POCKET-26B-CPU) — both answering on a CPU-only box (no GPU). POCKET-26B is Gemma4-based.

	[![License](https://img.shields.io/badge/License-Apache_2.0-0f6e56)](https://www.apache.org/licenses/LICENSE-2.0) [![Runtime](https://img.shields.io/badge/runtime-stock_llama.cpp-f0992a)](https://github.com/ggml-org/llama.cpp) [![No GPU](https://img.shields.io/badge/GPU-not_required-1baf7a)]() [![Base](https://img.shields.io/badge/base-Qwen3.5--35B--A3B-185fa5)]()

	Pick your build → [![35B](https://img.shields.io/badge/POCKET--35B-GGUF-243456)](https://huggingface.co/FINAL-Bench/POCKET-35B-GGUF) [![KR GGUF](https://img.shields.io/badge/POCKET--KR-GGUF-7A1F3D)](https://huggingface.co/FINAL-Bench/POCKET-KR-GGUF) [![KR MLX](https://img.shields.io/badge/POCKET--KR-MLX_(iPhone)-0f6e56)](https://huggingface.co/FINAL-Bench/POCKET-KR-MLX) [![EN GGUF](https://img.shields.io/badge/POCKET--EN-GGUF-185fa5)](https://huggingface.co/FINAL-Bench/POCKET-EN-GGUF) [![26B](https://img.shields.io/badge/POCKET--26B-GGUF-2a9d8f)](https://huggingface.co/FINAL-Bench/POCKET-26B-GGUF)

	## 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 \| — \|

	*Wikipedia-Korean perplexity, lower is better. `Q4_K_M` = 5.79 baseline. English builds are tuned on English; see each repo.

	> 🍎 Why MLX for Korean but GGUF for English on iPhone? Apple-native MLX only does uniform quantization. Korean survives it (96 experts hold up); English needs our proprietary quantization, which only GGUF supports — so the English iPhone build ships as a GGUF you run with [PocketPal](https://github.com/a-ghorbani/pocketpal-ai). 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](https://huggingface.co/FINAL-Bench/POCKET-26B-GGUF) (`Q2_K` 11 GB · `Q4_K_M` 17 GB · GPQA-Diamond 67%). Universal compatibility for 12 GB phones, PC, and browser.

	![Speed vs Bonsai](./pocket_speed.svg)

	## 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
	```bash
	# 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](https://huggingface.co/FINAL-Bench/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](https://huggingface.co/FINAL-Bench/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?](https://vidraft.net/insights/on-device-llm-without-gpu.html)
	- What model quantization is, and why a 4-bit model stays smart: [What is model quantization?](https://vidraft.net/insights/what-is-quantization-llm.html)

	<!-- POCKET-FAMILY -->

	---

	## 🧩 The POCKET Family — On-device AI by VIDRAFT

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

	Models
	- 📦 [POCKET-35B-GGUF](https://huggingface.co/FINAL-Bench/POCKET-35B-GGUF) — flagship, PC / server, no GPU
	- 📦 [POCKET-26B-GGUF](https://huggingface.co/FINAL-Bench/POCKET-26B-GGUF) — compact 26B
	- 🇰🇷 [POCKET-KR-GGUF](https://huggingface.co/FINAL-Bench/POCKET-KR-GGUF) — Korean, Android
	- 🍎 [POCKET-KR-MLX](https://huggingface.co/FINAL-Bench/POCKET-KR-MLX) — Korean, iPhone / Mac
	- 🌍 [POCKET-EN-GGUF](https://huggingface.co/FINAL-Bench/POCKET-EN-GGUF) — English, phone / PC
	- 🖼️ [POCKET-Image-Zimage](https://huggingface.co/FINAL-Bench/POCKET-Image-Zimage) — character-perfect text in any image

	Demos & tools (Spaces)
	- 🎨 [POCKET-Image Studio](https://huggingface.co/spaces/FINAL-Bench/POCKET-Image-Studio) — text-in-image, generate in-page
	- 🖥️ [POCKET-35B-CPU](https://huggingface.co/spaces/FINAL-Bench/POCKET-35B-CPU) — 35B answering on a CPU
	- 🖥️ [POCKET-26B-CPU](https://huggingface.co/spaces/FINAL-Bench/POCKET-26B-CPU) — 26B on a CPU

	📚 [Full POCKET collection](https://huggingface.co/collections/FINAL-Bench/pocket-models-6a618ee5d23eafb7e185a5c6)

	<!-- /POCKET-FAMILY -->