Text Generation
GGUF
llama.cpp
agent
agentic
tool-use
function-calling
react
local-government
agenda-parser
conversational
Instructions to use rdubwiley/agenda-parser-lite 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 rdubwiley/agenda-parser-lite 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 rdubwiley/agenda-parser-lite:Q8_0 # Run inference directly in the terminal: llama cli -hf rdubwiley/agenda-parser-lite:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf rdubwiley/agenda-parser-lite:Q8_0 # Run inference directly in the terminal: llama cli -hf rdubwiley/agenda-parser-lite:Q8_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 rdubwiley/agenda-parser-lite:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf rdubwiley/agenda-parser-lite:Q8_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 rdubwiley/agenda-parser-lite:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf rdubwiley/agenda-parser-lite:Q8_0
Use Docker
docker model run hf.co/rdubwiley/agenda-parser-lite:Q8_0
- LM Studio
- Jan
- vLLM
How to use rdubwiley/agenda-parser-lite with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rdubwiley/agenda-parser-lite" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rdubwiley/agenda-parser-lite", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/rdubwiley/agenda-parser-lite:Q8_0
- Ollama
How to use rdubwiley/agenda-parser-lite with Ollama:
ollama run hf.co/rdubwiley/agenda-parser-lite:Q8_0
- Unsloth Studio
How to use rdubwiley/agenda-parser-lite 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 rdubwiley/agenda-parser-lite 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 rdubwiley/agenda-parser-lite to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for rdubwiley/agenda-parser-lite to start chatting
- Pi
How to use rdubwiley/agenda-parser-lite with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf rdubwiley/agenda-parser-lite:Q8_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": "rdubwiley/agenda-parser-lite:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use rdubwiley/agenda-parser-lite with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf rdubwiley/agenda-parser-lite:Q8_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 rdubwiley/agenda-parser-lite:Q8_0
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use rdubwiley/agenda-parser-lite with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf rdubwiley/agenda-parser-lite:Q8_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 "rdubwiley/agenda-parser-lite:Q8_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 rdubwiley/agenda-parser-lite with Docker Model Runner:
docker model run hf.co/rdubwiley/agenda-parser-lite:Q8_0
- Lemonade
How to use rdubwiley/agenda-parser-lite with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull rdubwiley/agenda-parser-lite:Q8_0
Run and chat with the model
lemonade run user.agenda-parser-lite-Q8_0
List all available models
lemonade list
Upload README.md with huggingface_hub
Browse files
README.md
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---
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license: gemma
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base_model: google/gemma-4-E4B-it
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tags:
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- gguf
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- llama.cpp
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- agent
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- tool-use
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- agenda-parser
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---
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# agenda-parser-lite (GGUF)
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LoRA SFT fine-tune of [`google/gemma-4-E4B-it`](https://huggingface.co/google/gemma-4-E4B-it) for the
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**Agenda Parser** research agent's ReAct tool-calling protocol — the model emits a
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single JSON action `{"thought", "tool", "args"}` per step over a public-meeting
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agenda packet. Quantized to **Q8_0** for [llama.cpp](https://github.com/ggml-org/llama.cpp).
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## Training data
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Distilled from strong teacher models (Kimi k2.6, DeepSeek 4 pro via OpenCode Go) on
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the [`rdubwiley/agenda-parser-tool-traces`](https://huggingface.co/datasets/rdubwiley/agenda-parser-tool-traces) trace dataset
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(faithfulness-judged). One agent step per example; loss on the assistant JSON span.
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The source agenda packets the traces were generated over are published in that
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dataset's [`source_packets/`](https://huggingface.co/datasets/rdubwiley/agenda-parser-tool-traces/tree/main/source_packets)
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folder.
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## Run
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```bash
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huggingface-cli download rdubwiley/agenda-parser-lite agenda-parser-lite-Q8_0.gguf
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llama-server -m agenda-parser-lite-Q8_0.gguf --jinja # --jinja enables the chat/tool template
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```
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## Intended use & limitations
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For driving the Agenda Parser agent (in-process llama.cpp backend) over uploaded
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agenda PDFs. Not a general assistant; answers should be checked against the source
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packet. Inherits the [Gemma Terms of Use](https://ai.google.dev/gemma/terms).
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