Text Generation
GGUF
llama.cpp
conversational
on-device
mobile
iphone
android
cpu
local-llm
edge
mixture-of-experts
Mixture of Experts
quantized
pocket
vidraft
qwen3_5_moe
darwin
imatrix
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
- llama.cpp
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
- Pi
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"
Feature POCKET-27B model card and downloads
Browse files
README.md
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<!-- SUPERCLUE-2026-11:START -->
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## 馃弳 SuperCLUE November 2026: #1 open-weight model in math and science reasoning
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<!-- POCKET-27B-DISCOVERY:START -->
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<p align="center">
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<a href="https://huggingface.co/FINAL-Bench/POCKET-27B-GGUF"><img src="https://img.shields.io/badge/NEW%20POCKET--27B-Q6__K%20%C2%B7%2022.43%20GB-55cbb4?style=for-the-badge" alt="New POCKET-27B: Qwen3.8-27B, Q6_K, 22.43 GB - model card and downloads"></a>
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</p>
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<p align="center"><b>Meet POCKET-27B.</b> Qwen3.8-27B in ARCHsloth Q6_K GGUF. <a href="https://huggingface.co/FINAL-Bench/POCKET-27B-GGUF/tree/main">Choose files & download</a> 路 <a href="https://huggingface.co/FINAL-Bench/POCKET-27B-GGUF#quality-and-evaluation">Quality & limits</a></p>
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<!-- POCKET-27B-DISCOVERY:END -->
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<!-- SUPERCLUE-2026-11:START -->
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## 馃弳 SuperCLUE November 2026: #1 open-weight model in math and science reasoning
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