Instructions to use distil-labs/distil-qwen3-0.6b-posthog-narrator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use distil-labs/distil-qwen3-0.6b-posthog-narrator 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 distil-labs/distil-qwen3-0.6b-posthog-narrator:F16
# Run inference directly in the terminal:
llama cli -hf distil-labs/distil-qwen3-0.6b-posthog-narrator:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf distil-labs/distil-qwen3-0.6b-posthog-narrator:F16
# Run inference directly in the terminal:
llama cli -hf distil-labs/distil-qwen3-0.6b-posthog-narrator:F16

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 distil-labs/distil-qwen3-0.6b-posthog-narrator:F16
# Run inference directly in the terminal:
./llama-cli -hf distil-labs/distil-qwen3-0.6b-posthog-narrator:F16

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 distil-labs/distil-qwen3-0.6b-posthog-narrator:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf distil-labs/distil-qwen3-0.6b-posthog-narrator:F16

Use Docker

docker model run hf.co/distil-labs/distil-qwen3-0.6b-posthog-narrator:F16

LM Studio
Jan

vLLM

How to use distil-labs/distil-qwen3-0.6b-posthog-narrator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "distil-labs/distil-qwen3-0.6b-posthog-narrator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "distil-labs/distil-qwen3-0.6b-posthog-narrator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/distil-labs/distil-qwen3-0.6b-posthog-narrator:F16

Ollama
How to use distil-labs/distil-qwen3-0.6b-posthog-narrator with Ollama:
```
ollama run hf.co/distil-labs/distil-qwen3-0.6b-posthog-narrator:F16
```
Unsloth Desktop

How to use distil-labs/distil-qwen3-0.6b-posthog-narrator with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf distil-labs/distil-qwen3-0.6b-posthog-narrator:F16

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": "distil-labs/distil-qwen3-0.6b-posthog-narrator:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use distil-labs/distil-qwen3-0.6b-posthog-narrator with Docker Model Runner:
```
docker model run hf.co/distil-labs/distil-qwen3-0.6b-posthog-narrator:F16
```

Lemonade

How to use distil-labs/distil-qwen3-0.6b-posthog-narrator with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull distil-labs/distil-qwen3-0.6b-posthog-narrator:F16

Run and chat with the model

lemonade run user.distil-qwen3-0.6b-posthog-narrator-F16

List all available models

lemonade list

Hermes Agent

How to use distil-labs/distil-qwen3-0.6b-posthog-narrator with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf distil-labs/distil-qwen3-0.6b-posthog-narrator:F16

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 distil-labs/distil-qwen3-0.6b-posthog-narrator:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use distil-labs/distil-qwen3-0.6b-posthog-narrator with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf distil-labs/distil-qwen3-0.6b-posthog-narrator:F16

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 "distil-labs/distil-qwen3-0.6b-posthog-narrator:F16" \
  --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"

distil-qwen3-0.6b-posthog-narrator

A 0.6B specialist that turns a raw PostHog session (timestamped event stream) into a 3-sentence plain-English story of what the user did. One of three tools in the distil-posthog-traffic-analyser harness; trained on the Distil Labs platform.

Task contract

Input — a rendered session prompt:

Session: <sessionId>
User: <distinctId>
Span: <startedAt> → <endedAt> (<duration>, <n> events)

Events:
- <ISO timestamp>  <event_name> { key=value, ... }
...

Write exactly 3 sentences describing what this user did.

Output — exactly 3 sentences of past-tense prose. No preamble, no lists, no markdown. Faithful to the events: concrete page names, button labels, error messages, and search queries; frustration signals (rage clicks, repeated failures, abandonment) called out; nothing invented.

Training

Base model: Qwen3-0.6B (Apache 2.0)
Teacher: openai.gpt-oss-120b (Apache 2.0)
Seed data: 25 hand-authored, schema-validated session/narration pairs, committed at examples/seeds/narrator.jsonl (20 train / 5 held-out test), generated from typed source by scripts/build_seeds.ts
Synthetic expansion: 10,033 examples generated and validated by the Distil Labs platform from the seed set
Method: platform-managed fine-tune (task type: question-answering)

Evaluation

Held-out test set, scored by the platform's LLM judge:

	Untrained Qwen3-0.6B	This model
LLM-as-a-Judge	0.00%	100.00%
ROUGE	39.20%	63.98%

Live contract check (exactly 3 sentences, length bounds) on 10 sessions — 5 held-out seed sessions plus the repo's 5 bundled demo sessions: 10/10, reproduced on both the platform's hosted vLLM endpoint and this GGUF running locally via Ollama. In the same live setup the untrained base violated the 3-sentence format on 3–4 of 10 sessions per run and fabricated events (e.g. reporting a successful login on a failed password-reset session).

Usage (Ollama)

ollama create posthog-narrator -f Modelfile   # FROM ./<this gguf>

Then in the harness .env:

TOOL_NARRATOR_MODEL=posthog-narrator

See the repo README for the full three-tool pipeline (bun run demo runs it end-to-end, no API key).

Downloads last month: 392

Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for distil-labs/distil-qwen3-0.6b-posthog-narrator

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Quantized

(428)

this model