Text Generation
MLX
Safetensors
qwen3_5_moe
conversational
on-device
mobile
iphone
android
cpu
local-llm
edge
mixture-of-experts
Mixture of Experts
quantized
pocket
vidraft
apple-silicon
korean
korean-llm
darwin
2-bit
Instructions to use FINAL-Bench/POCKET-KR-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use FINAL-Bench/POCKET-KR-MLX with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("FINAL-Bench/POCKET-KR-MLX") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use FINAL-Bench/POCKET-KR-MLX with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "FINAL-Bench/POCKET-KR-MLX"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "FINAL-Bench/POCKET-KR-MLX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use FINAL-Bench/POCKET-KR-MLX with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "FINAL-Bench/POCKET-KR-MLX"
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-KR-MLX
Run Hermes
hermes
- OpenClaw new
How to use FINAL-Bench/POCKET-KR-MLX with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "FINAL-Bench/POCKET-KR-MLX"
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-KR-MLX" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- MLX LM
How to use FINAL-Bench/POCKET-KR-MLX with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "FINAL-Bench/POCKET-KR-MLX"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "FINAL-Bench/POCKET-KR-MLX" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FINAL-Bench/POCKET-KR-MLX", "messages": [ {"role": "user", "content": "Hello"} ] }'
File size: 3,046 Bytes
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<title>POCKET-35B — a 35B model that runs on iPhone and on PC with no GPU, using stock llama.cpp</title>
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<text x="72" y="178" fill="#eef2fa" font-size="30" font-weight="700">A 35B model that runs on your <tspan fill="#5dd0a8">iPhone</tspan> —</text>
<text x="72" y="218" fill="#eef2fa" font-size="30" font-weight="700">and on your PC with <tspan fill="#ffd76a">no GPU</tspan>.</text>
<text x="72" y="262" fill="#9fb0d4" font-size="22" font-weight="500">Just stock <tspan fill="#e8edf7" font-weight="700">llama.cpp</tspan>. <tspan fill="#f0992a" font-weight="700">No fork</tspan>, no CUDA, no cloud.</text>
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<text x="92" y="366" fill="#8fa0c4" font-size="16" font-weight="500">5 GB · Korean & English</text>
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<text x="364" y="366" fill="#8fa0c4" font-size="16" font-weight="500">27 tok/s · no GPU needed</text>
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<text x="72" y="428" fill="#5b6b8f" font-size="16">34.66B total · ~3B active/token · sparse Mixture-of-Experts, Korean-tuned</text>
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<text x="90" y="162" fill="#5dd0a8" font-size="20" font-weight="700" text-anchor="middle">runs here.</text>
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