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

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -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

Llama new
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"

SeaWolf-AI commited on Sep 25

Commit

f309ced

verified ·

1 Parent(s): 715e01e

POCKET family: add POCKET-Qwen3.8-Flash-Next (180B on a laptop), cross-link all 8 POCKET repos

Browse files

Files changed (1) hide show

README.md +9 -1

README.md CHANGED Viewed

@@ -23,6 +23,9 @@ tags:
 - qwen3_5_moe
 - darwin
 ---
 > ### 🆕 **[POCKET-Zimage-CPU](https://huggingface.co/FINAL-Bench/POCKET-Zimage-CPU)** — photoreal **images** in **46 s** on a **CPU only**. No GPU, no CUDA, no Python.
 > [![New](https://img.shields.io/badge/🆕_NEW-POCKET--Zimage--CPU-ff6b35)](https://huggingface.co/FINAL-Bench/POCKET-Zimage-CPU) [![Space](https://img.shields.io/badge/🤗_Space-try_on_CPU-ffce3a)](https://huggingface.co/spaces/FINAL-Bench/POCKET-Zimage-CPU) [![RAM](https://img.shields.io/badge/RAM-6.4_GB-1baf7a)]() <!-- POCKET-Zimage-CPU-BADGE -->
@@ -40,7 +43,7 @@ tags:
 [![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
@@ -53,8 +56,10 @@ tags:
 | **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.
@@ -158,12 +163,15 @@ Apache-2.0.
 - 🇰🇷 [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)

 - qwen3_5_moe
 - darwin
 ---
+> ### 🆕 **[POCKET-Qwen3.8-Flash-Next](https://huggingface.co/FINAL-Bench/POCKET-Qwen3.8-Flash-Next-GGUF)** — a **180B** model running on a **laptop with 8 GB VRAM + 32 GB RAM** · **4.17 tok/s measured**.
+> [![New](https://img.shields.io/badge/🆕_NEW-POCKET--180B_on_a_laptop-6c3fd1)](https://huggingface.co/FINAL-Bench/POCKET-Qwen3.8-Flash-Next-GGUF) [![VRAM](https://img.shields.io/badge/VRAM-8_GB-1baf7a)]() [![RAM](https://img.shields.io/badge/RAM-32_GB-1baf7a)]() [![Speed](https://img.shields.io/badge/measured-4.17_tok%2Fs-243456)]() <!-- POCKET-FLASHNEXT-BADGE -->
 > ### 🆕 **[POCKET-Zimage-CPU](https://huggingface.co/FINAL-Bench/POCKET-Zimage-CPU)** — photoreal **images** in **46 s** on a **CPU only**. No GPU, no CUDA, no Python.
 > [![New](https://img.shields.io/badge/🆕_NEW-POCKET--Zimage--CPU-ff6b35)](https://huggingface.co/FINAL-Bench/POCKET-Zimage-CPU) [![Space](https://img.shields.io/badge/🤗_Space-try_on_CPU-ffce3a)](https://huggingface.co/spaces/FINAL-Bench/POCKET-Zimage-CPU) [![RAM](https://img.shields.io/badge/RAM-6.4_GB-1baf7a)]() <!-- POCKET-Zimage-CPU-BADGE -->
 [![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) [![26B](https://img.shields.io/badge/POCKET--26B-GGUF-2a9d8f)](https://huggingface.co/FINAL-Bench/POCKET-26B-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) [![180B laptop](https://img.shields.io/badge/POCKET--180B-Qwen3.8_Flash--Next_·_laptop-6c3fd1)](https://huggingface.co/FINAL-Bench/POCKET-Qwen3.8-Flash-Next-GGUF) [![Image NF4](https://img.shields.io/badge/POCKET--Image-Z--Image_NF4-d35400)](https://huggingface.co/FINAL-Bench/POCKET-Image-Zimage) [![Image CPU](https://img.shields.io/badge/POCKET--Zimage-CPU-ff6b35)](https://huggingface.co/FINAL-Bench/POCKET-Zimage-CPU)
 ## The POCKET lineup — pick by your device
 | **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 (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-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-Qwen3.8-Flash-Next-GGUF](https://huggingface.co/FINAL-Bench/POCKET-Qwen3.8-Flash-Next-GGUF) — **180B on a laptop** (8 GB VRAM + 32 GB RAM)
 - 🖼️ [POCKET-Image-Zimage](https://huggingface.co/FINAL-Bench/POCKET-Image-Zimage) — character-perfect text in any image
+- 🖥️ [POCKET-Zimage-CPU](https://huggingface.co/FINAL-Bench/POCKET-Zimage-CPU) — photoreal images on a CPU only
 **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
+- 🖼️ [POCKET-Zimage-CPU](https://huggingface.co/spaces/FINAL-Bench/POCKET-Zimage-CPU) — image generation on a CPU
 📚 [Full POCKET collection](https://huggingface.co/collections/FINAL-Bench/pocket-models-6a618ee5d23eafb7e185a5c6)