Text Generation
MLX
Safetensors
English
lfm2
fact-extraction
structured-extraction
on-device
memory
conversational
8-bit precision
Instructions to use mindi-dev/experience-extractor-1.2b-v1-mlx-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mindi-dev/experience-extractor-1.2b-v1-mlx-8bit 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("mindi-dev/experience-extractor-1.2b-v1-mlx-8bit") 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 mindi-dev/experience-extractor-1.2b-v1-mlx-8bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "mindi-dev/experience-extractor-1.2b-v1-mlx-8bit"
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": "mindi-dev/experience-extractor-1.2b-v1-mlx-8bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mindi-dev/experience-extractor-1.2b-v1-mlx-8bit 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 "mindi-dev/experience-extractor-1.2b-v1-mlx-8bit"
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 mindi-dev/experience-extractor-1.2b-v1-mlx-8bit
Run Hermes
hermes
- OpenClaw new
How to use mindi-dev/experience-extractor-1.2b-v1-mlx-8bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "mindi-dev/experience-extractor-1.2b-v1-mlx-8bit"
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 "mindi-dev/experience-extractor-1.2b-v1-mlx-8bit" \ --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 mindi-dev/experience-extractor-1.2b-v1-mlx-8bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "mindi-dev/experience-extractor-1.2b-v1-mlx-8bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mindi-dev/experience-extractor-1.2b-v1-mlx-8bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mindi-dev/experience-extractor-1.2b-v1-mlx-8bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
| # NOTICE — license & attribution | |
| This model is a fine-tune of **LiquidAI/LFM2-1.2B-Extract** and is therefore governed by the **LFM Open License | |
| v1.0** (the LiquidAI `lfm1.0` license), NOT the MIT license of the `experience` source code. | |
| **Change notice (Sec. 4):** fine-tuned from LiquidAI/LFM2-1.2B-Extract by the experience/mindi-dev project into a | |
| structured fact extractor, by LoRA (rank 32) fine-tune (mlx-lm) on real-distribution chat transcripts with | |
| teacher-generated labels. Architecture unchanged (`lfm2`). | |
| **Attribution:** base model © Liquid AI, used under the LFM Open License v1.0. Not endorsed by | |
| or affiliated with Liquid AI; no LiquidAI trademark/naming rights granted (Sec. 7). | |
| **Commercial-use gate (Sec. 5), inherited by you:** Commercial Use by an entity with **≥ US$10M | |
| annual revenue is NOT licensed** without a Liquid AI commercial license. Below that, use is | |
| permitted under the LFM Open License v1.0. | |
| **You must include the full LFM Open License v1.0 in this repo** — fetch it from the base model: | |
| hf download LiquidAI/LFM2-1.2B-Extract LICENSE --local-dir . # or copy LICENSE/LICENSE.md from LiquidAI/LFM2-1.2B-Extract | |
| Training labels were teacher-generated (Claude / GPT-5.5-via-codex). Only weights are | |
| distributed here, not the dataset. | |