Instructions to use vishinvents/distil-qwen3-1.7b-posthog-extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use vishinvents/distil-qwen3-1.7b-posthog-extractor 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 vishinvents/distil-qwen3-1.7b-posthog-extractor:F16
# Run inference directly in the terminal:
llama cli -hf vishinvents/distil-qwen3-1.7b-posthog-extractor:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vishinvents/distil-qwen3-1.7b-posthog-extractor:F16
# Run inference directly in the terminal:
llama cli -hf vishinvents/distil-qwen3-1.7b-posthog-extractor: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 vishinvents/distil-qwen3-1.7b-posthog-extractor:F16
# Run inference directly in the terminal:
./llama-cli -hf vishinvents/distil-qwen3-1.7b-posthog-extractor: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 vishinvents/distil-qwen3-1.7b-posthog-extractor:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vishinvents/distil-qwen3-1.7b-posthog-extractor:F16

Use Docker

docker model run hf.co/vishinvents/distil-qwen3-1.7b-posthog-extractor:F16

LM Studio
Jan

vLLM

How to use vishinvents/distil-qwen3-1.7b-posthog-extractor with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vishinvents/distil-qwen3-1.7b-posthog-extractor"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vishinvents/distil-qwen3-1.7b-posthog-extractor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vishinvents/distil-qwen3-1.7b-posthog-extractor:F16

Ollama
How to use vishinvents/distil-qwen3-1.7b-posthog-extractor with Ollama:
```
ollama run hf.co/vishinvents/distil-qwen3-1.7b-posthog-extractor:F16
```
Unsloth Desktop

How to use vishinvents/distil-qwen3-1.7b-posthog-extractor with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vishinvents/distil-qwen3-1.7b-posthog-extractor: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": "vishinvents/distil-qwen3-1.7b-posthog-extractor:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use vishinvents/distil-qwen3-1.7b-posthog-extractor with Docker Model Runner:
```
docker model run hf.co/vishinvents/distil-qwen3-1.7b-posthog-extractor:F16
```

Lemonade

How to use vishinvents/distil-qwen3-1.7b-posthog-extractor with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vishinvents/distil-qwen3-1.7b-posthog-extractor:F16

Run and chat with the model

lemonade run user.distil-qwen3-1.7b-posthog-extractor-F16

List all available models

lemonade list

Hermes Agent

How to use vishinvents/distil-qwen3-1.7b-posthog-extractor with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vishinvents/distil-qwen3-1.7b-posthog-extractor: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 vishinvents/distil-qwen3-1.7b-posthog-extractor:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use vishinvents/distil-qwen3-1.7b-posthog-extractor with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vishinvents/distil-qwen3-1.7b-posthog-extractor: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 "vishinvents/distil-qwen3-1.7b-posthog-extractor: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-qwen3-1.7b-posthog-extractor

A 1.7B specialist that reads a short user-behavior narration and emits product findings (bugs and UX gaps) as strict JSON. One of three tools in the distil-posthog-traffic-analyser harness; trained on the Distil Labs platform.

Task contract

Input:

Narration:
"""
<3-sentence session narration>
"""

Return JSON now.

Output — valid JSON only, matching:

{"findings":[{"kind":"bug|gap","severity":1-5,"title":"...","evidence":"..."}]}

bug = something visibly broke or errored; gap = something the user clearly wanted to do but couldn't
severity: 1 (cosmetic) to 5 (blocks a core flow)
A clean session correctly yields {"findings":[]}
No markdown, no commentary, nothing invented beyond the narration

Training

Base model: Qwen3-1.7B (Apache 2.0)
Teacher: openai.gpt-oss-120b (Apache 2.0)
Seed data: 25 hand-authored, schema-validated narration/findings pairs — including 6 clean-session examples with empty findings — committed at examples/seeds/extractor.jsonl (20 train / 5 held-out test)
Synthetic expansion: 10,078 examples generated and validated by the Distil Labs platform from the seed set
Method: platform-managed fine-tune (task type: question-answering, JSON output)

A 0.6B variant was trained first and scored similarly on the platform judge, but live testing showed it missed low-signal findings (dead-end searches with no error keyword) entirely; the 1.7B student catches them, which is why this size ships.

Evaluation

Held-out test set (n=5), scored by the platform's LLM judge:

	Untrained Qwen3-1.7B	Teacher (gpt-oss-120b)	This model
LLM-as-a-Judge	40.00% (2/5)	80.00% (4/5)	80.00% (4/5)
ROUGE	60.25%	67.61%	68.88%

The tuned 1.7B student matches its 120B teacher's judge score on this task.

Live checks on this GGUF via Ollama match the hosted endpoint: 8/10 on an adversarial suite (held-out narrations plus narrations authored by the narrator SLM). The two misses are bug-vs-gap label confusion on ambiguous "silent failure" cases — the finding is still surfaced with a sensible title and severity; only the kind tag wobbles.

Head-to-head vs a frontier teacher (bun run eval, gpt-5-mini as opponent and judge, 5 demo narrations): 3 ties, 1 win for this model, 1 win for the teacher — with this model running locally at $0 per call.

Usage (Ollama)

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

Then in the harness .env:

TOOL_EXTRACTOR_MODEL=posthog-extractor

Downloads last month: 76

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for vishinvents/distil-qwen3-1.7b-posthog-extractor

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Quantized

(417)

this model