Instructions to use forlop/microdata-copilot-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use forlop/microdata-copilot-v2 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 forlop/microdata-copilot-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf forlop/microdata-copilot-v2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf forlop/microdata-copilot-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf forlop/microdata-copilot-v2:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf forlop/microdata-copilot-v2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf forlop/microdata-copilot-v2:Q4_K_M

Use Docker

docker model run hf.co/forlop/microdata-copilot-v2:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use forlop/microdata-copilot-v2 with Ollama:
```
ollama run hf.co/forlop/microdata-copilot-v2:Q4_K_M
```
Unsloth Desktop

How to use forlop/microdata-copilot-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf forlop/microdata-copilot-v2:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "forlop/microdata-copilot-v2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use forlop/microdata-copilot-v2 with Docker Model Runner:
```
docker model run hf.co/forlop/microdata-copilot-v2:Q4_K_M
```

Lemonade

How to use forlop/microdata-copilot-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull forlop/microdata-copilot-v2:Q4_K_M

Run and chat with the model

lemonade run user.microdata-copilot-v2-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use forlop/microdata-copilot-v2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf forlop/microdata-copilot-v2:Q4_K_M

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 forlop/microdata-copilot-v2:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use forlop/microdata-copilot-v2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf forlop/microdata-copilot-v2:Q4_K_M

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 "forlop/microdata-copilot-v2:Q4_K_M" \
  --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"

forlop commited on May 14

Commit

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@@ -23,10 +23,10 @@ A small, locally-deployable AI assistant fine-tuned to help users write
 Norwegian register-data variables published by [SSB (Statistics
 Norway)](https://www.ssb.no/).
-This repo hosts the deployed **q4_k_m quantised GGUF** (2.7 GB) plus an
-Ollama `Modelfile` so the model can be pulled and run with one command.
-The full source code (training, RAG, eval, deployment) and the technical
-note live at **<https://github.com/forlop/microdata-no-copilot>**.
 ## Quick start
@@ -36,21 +36,40 @@ note live at **<https://github.com/forlop/microdata-no-copilot>**.
 #   macOS:      brew install ollama  (or download from ollama.com)
 #   Windows:    download OllamaSetup.exe from ollama.com
-# Pull and run
 ollama pull hf.co/forlop/microdata-copilot-v2:Q4_K_M
-ollama run hf.co/forlop/microdata-copilot-v2:Q4_K_M
 ```
-For the full RAG-wrapped experience (retrieval over the live microdata.no
-variable catalogue + a Streamlit web UI), clone the GitHub repo:
 ```bash
-git clone https://github.com/forlop/microdata-no-copilot
-cd microdata-no-copilot
 pip install -r requirements.txt streamlit
 streamlit run rag/app.py
 ```
 ## What this is
 - **Base model:** Qwen3.5-4B (Apache-2.0, via Unsloth's pre-quantised release).

 Norwegian register-data variables published by [SSB (Statistics
 Norway)](https://www.ssb.no/).
+This repo hosts the deployed **q4_k_m quantised GGUF** (2.7 GB) and the
+companion **Ollama `Modelfile`**. The full source code (training, RAG,
+eval, deployment) and the technical note live at
+**<https://github.com/forlop/microdata-no-copilot>**.
 ## Quick start
 #   macOS:      brew install ollama  (or download from ollama.com)
 #   Windows:    download OllamaSetup.exe from ollama.com
+# 1. Pull the base GGUF from this repo (~2.7 GB, one-time)
 ollama pull hf.co/forlop/microdata-copilot-v2:Q4_K_M
+# 2. Clone the GitHub repo (contains the Modelfile + RAG layer)
+git clone https://github.com/forlop/microdata-no-copilot
+cd microdata-no-copilot
+# 3. Apply the SYSTEM prompt + refusal few-shots + stop-token parameters
+ollama create microdata-copilot -f deploy/Modelfile
+# 4. Try it
+ollama run microdata-copilot "What is INNTEKT_LONN?"
 ```
+> **Why two steps?** `ollama pull` from Hugging Face downloads the raw
+> GGUF plus the chat template embedded in its metadata — but **not** the
+> custom Modelfile in this repo. Ollama only applies curated Modelfiles
+> for models in its official library. For HF-hosted models, you apply
+> your own Modelfile locally via `ollama create`. Without step 3, the
+> model bleeds `<|endoftext|>` tokens and loops. With it, you get the
+> full deployed configuration (system prompt, refusal patterns, stop
+> tokens, greedy decoding).
+## Full RAG-wrapped Streamlit demo
 ```bash
+# After the four steps above, from the cloned repo directory:
 pip install -r requirements.txt streamlit
 streamlit run rag/app.py
 ```
+Streamlit prints a `http://localhost:8501` URL — open it in your browser.
+On CPU expect ~10–15 s per response; on a recent GPU, ~1–2 s.
 ## What this is
 - **Base model:** Qwen3.5-4B (Apache-2.0, via Unsloth's pre-quantised release).