Text Generation
GGUF
English
lfm2
fact-extraction
structured-extraction
on-device
memory
conversational
Instructions to use mindi-dev/experience-extractor-1.2b-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use mindi-dev/experience-extractor-1.2b-v1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="mindi-dev/experience-extractor-1.2b-v1-GGUF", filename="experience-extractor-1.2b-v1-Q4_0.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mindi-dev/experience-extractor-1.2b-v1-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 mindi-dev/experience-extractor-1.2b-v1-GGUF:Q4_0 # Run inference directly in the terminal: llama cli -hf mindi-dev/experience-extractor-1.2b-v1-GGUF:Q4_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mindi-dev/experience-extractor-1.2b-v1-GGUF:Q4_0 # Run inference directly in the terminal: llama cli -hf mindi-dev/experience-extractor-1.2b-v1-GGUF:Q4_0
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf mindi-dev/experience-extractor-1.2b-v1-GGUF:Q4_0 # Run inference directly in the terminal: ./llama-cli -hf mindi-dev/experience-extractor-1.2b-v1-GGUF:Q4_0
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf mindi-dev/experience-extractor-1.2b-v1-GGUF:Q4_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf mindi-dev/experience-extractor-1.2b-v1-GGUF:Q4_0
Use Docker
docker model run hf.co/mindi-dev/experience-extractor-1.2b-v1-GGUF:Q4_0
- LM Studio
- Jan
- vLLM
How to use mindi-dev/experience-extractor-1.2b-v1-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mindi-dev/experience-extractor-1.2b-v1-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": "mindi-dev/experience-extractor-1.2b-v1-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mindi-dev/experience-extractor-1.2b-v1-GGUF:Q4_0
- Ollama
How to use mindi-dev/experience-extractor-1.2b-v1-GGUF with Ollama:
ollama run hf.co/mindi-dev/experience-extractor-1.2b-v1-GGUF:Q4_0
- Unsloth Studio
How to use mindi-dev/experience-extractor-1.2b-v1-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mindi-dev/experience-extractor-1.2b-v1-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mindi-dev/experience-extractor-1.2b-v1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mindi-dev/experience-extractor-1.2b-v1-GGUF to start chatting
- Pi
How to use mindi-dev/experience-extractor-1.2b-v1-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mindi-dev/experience-extractor-1.2b-v1-GGUF:Q4_0
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "mindi-dev/experience-extractor-1.2b-v1-GGUF:Q4_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mindi-dev/experience-extractor-1.2b-v1-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 mindi-dev/experience-extractor-1.2b-v1-GGUF:Q4_0
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-GGUF:Q4_0
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use mindi-dev/experience-extractor-1.2b-v1-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mindi-dev/experience-extractor-1.2b-v1-GGUF:Q4_0
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-GGUF:Q4_0" \ --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"
- Docker Model Runner
How to use mindi-dev/experience-extractor-1.2b-v1-GGUF with Docker Model Runner:
docker model run hf.co/mindi-dev/experience-extractor-1.2b-v1-GGUF:Q4_0
- Lemonade
How to use mindi-dev/experience-extractor-1.2b-v1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mindi-dev/experience-extractor-1.2b-v1-GGUF:Q4_0
Run and chat with the model
lemonade run user.experience-extractor-1.2b-v1-GGUF-Q4_0
List all available models
lemonade list
| # Ollama Modelfile — experience-extractor-1.2b-v1 (GGUF), 8-field fact extractor. | |
| # Build: ollama create experience-extractor-1.2b -f Modelfile (run from this dir) | |
| # Note: single-pass extraction with the trained system prompt. For the validated windowed/ | |
| # ensemble recall, drive it via the `experience` crate (EXPERIENCE_EXTRACTION_WINDOW=5). | |
| FROM ./experience-extractor-1.2b-v1-Q4_0.gguf | |
| PARAMETER temperature 0 | |
| PARAMETER num_ctx 8192 | |
| SYSTEM """You extract structured facts from a group-chat transcript. | |
| Read the transcript and emit every storable fact as a JSON object with these eight fields, in this order: | |
| - what: a single, self-contained statement of the fact, with attribution resolved to real speaker names (string). | |
| - when: the absolute time the fact refers to, as an ISO 8601 timestamp; null when no absolute time is stated or resolvable. | |
| - where: the place the fact refers to; null when none is stated. | |
| - why: the reason or cause stated for the fact; null when none is stated. | |
| - who: the real speaker names the fact is about (array of strings; may be empty). | |
| - fact_type: "world" for facts about the world or other people, "experience" for the assistant's own statements about itself, what it will do, or what it will remember. | |
| - entities: the salient named entities mentioned in the fact (array of strings; may be empty). | |
| - message_refs: the messages this fact came from (array of strings), each either "id:<message_id>" or "index:<n>" where n is the zero-based position in the transcript. | |
| Resolution rules: | |
| - Resolve "you", "we", "I", and all pronouns to the real speaker names from the transcript. Never leave a pronoun in "what". | |
| - Questions, greetings, acknowledgements, and other filler assert nothing; they are not facts. | |
| - Details that are not stated stay null (for when/where/why) or empty (for who/entities); never invent them. | |
| Empty-answer contract: when nothing in the transcript is extractable, output exactly {"facts": []}. | |
| Output ONLY the JSON object {"facts": [...]} and nothing else.""" | |