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

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

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"}
     ]
   }'

experience-extractor-1.2b-v1-mlx-8bit / README.md

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metadata

license: other
license_name: lfm1.0
license_link: https://huggingface.co/LiquidAI/LFM2-1.2B-Extract/blob/main/LICENSE
base_model: LiquidAI/LFM2-1.2B-Extract
language:
  - en
tags:
  - mlx
  - lfm2
  - fact-extraction
  - structured-extraction
  - on-device
  - memory
pipeline_tag: text-generation
library_name: mlx

experience-extractor-1.2b-v1 (MLX 8-bit)

A small, on-device structured fact extractor for memory engines, fine-tuned from LiquidAI/LFM2-1.2B-Extract (LoRA (rank 32) fine-tune (mlx-lm)). It reads a chat transcript and emits every storable fact as JSON in a fixed 8-field schema:

{"facts": [
  {"what": "...", "when": null, "where": null, "why": null,
   "who": ["..."], "fact_type": "world|experience",
   "entities": ["..."], "message_refs": ["id:m07"]}
]}

It powers the experience memory engine (EXPERIENCE_EXTRACTOR=lfm25). This repo holds the MLX 8-bit build for fast inference on Apple Silicon via Ollama's MLX engine or mlx-lm.

Evaluation (LongMemEval-cleaned "KU", content-recall)

Run it windowed. Whole-transcript extraction caps a small model near 0.62; sliding a 5-message window and unioning the per-window facts is the recall mechanism and the recommended deploy mode. Pairing the 350M + 1.2B as an ensemble reaches ~0.986 on KU.

mode	recall	mean facts/row	repeat
5-msg windowed (recommended)	0.958	~45	high (use dedup)
5-msg windowed + semantic dedup@0.6	0.931	~15	~0.16 (clean)
whole-transcript (single pass)	0.389 — free decoding understates; use windowing	low	low

This MLX 8-bit build, measured: windowed 0.958 (= the GGUF), dedup@0.6 0.931. The low whole-transcript 0.389 is a free-decoding artifact, not quant loss — windowing recovers it.

Files

MLX 8-bit model (config.json, model.safetensors, tokenizer, chat template); 1.2 GB.

Usage

Ollama (MLX engine, Apple Silicon):

ollama run hf.co/mindi-dev/experience-extractor-1.2b-v1-mlx-8bit

mlx-lm: mlx_lm.generate --model mindi-dev/experience-extractor-1.2b-v1-mlx-8bit --prompt "<transcript>"

For the recall numbers above, drive it windowed (5-msg sliding window + union + dedup) — e.g. via the experience crate's EXPERIENCE_EXTRACTION_WINDOW=5. A single whole-transcript pass under free decoding scores lower.

Other formats

GGUF (llama.cpp / Ollama / crate): mindi-dev/experience-extractor-1.2b-v1-GGUF

Training

Full pipeline at mindi-dev/experience (training/). Fine-tuned on real-distribution LongMemEval transcripts (leakage-safe; held-out KU never trained on) with grounded teacher-generated labels.

License

Fine-tune of LiquidAI/LFM2-1.2B-Extract under the LFM Open License v1.0. Redistribution permitted with attribution + change notice; commercial use by entities with ≥ US$10M revenue requires a Liquid AI commercial license (Sec. 5). The crate code is MIT and separate. See NOTICE.md and the full LICENSE in this repo.