Instructions to use vishinvents/distil-qwen3-1.7b-posthog-prioritizer 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-prioritizer 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-prioritizer:F16
# Run inference directly in the terminal:
llama cli -hf vishinvents/distil-qwen3-1.7b-posthog-prioritizer: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-prioritizer:F16
# Run inference directly in the terminal:
llama cli -hf vishinvents/distil-qwen3-1.7b-posthog-prioritizer: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-prioritizer:F16
# Run inference directly in the terminal:
./llama-cli -hf vishinvents/distil-qwen3-1.7b-posthog-prioritizer: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-prioritizer:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vishinvents/distil-qwen3-1.7b-posthog-prioritizer:F16

Use Docker

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

LM Studio
Jan

vLLM

How to use vishinvents/distil-qwen3-1.7b-posthog-prioritizer 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-prioritizer"
# 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-prioritizer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

How to use vishinvents/distil-qwen3-1.7b-posthog-prioritizer 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-prioritizer: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-prioritizer:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use vishinvents/distil-qwen3-1.7b-posthog-prioritizer 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-prioritizer: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-prioritizer:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use vishinvents/distil-qwen3-1.7b-posthog-prioritizer 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-prioritizer: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-prioritizer: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-prioritizer

A 1.7B specialist that ranks a batch of deduplicated product findings by impact, frequency, and effort, returning strict JSON with a reason per item. One of three tools in the distil-posthog-traffic-analyser harness; trained on the Distil Labs platform.

Task contract

Input:

Findings (N):
[ {"id": "...", "kind": "bug|gap", "severity": 1-5,
   "occurrences": <int>, "title": "...", "evidence": "..."}, ... ]

Return JSON now. Every id above must appear in "ranked" exactly once.

Output — valid JSON only, matching:

{"ranked":[{"id":"<input id>","rank":1,"reason":"<=2 sentences citing the numbers"}]}

Hard constraint: every input id appears exactly once — no dropped, invented, or duplicated ids. Ranking judgment encoded in training: security issues lead regardless of frequency; revenue blockers rank above equally severe non-revenue issues; silent data corruption outranks loud errors; at equal severity, reach wins; cosmetic items rank last.

Training

Base model: Qwen3-1.7B (Apache 2.0)
Teacher: openai.gpt-oss-120b (Apache 2.0)
Seed data: 22 hand-authored, schema-validated ranking batches (single-item to 4-item, including equal-severity ties and security/revenue trade-offs), committed at examples/seeds/prioritizer.jsonl (18 train / 4 held-out test); a coverage validator guarantees every seed batch ranks each finding exactly once
Synthetic expansion: 10,004 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; it ranked held-out batches correctly but violated the exactly-once coverage constraint on a batch containing near-duplicate findings. The 1.7B student holds exact coverage on that same adversarial batch, which is why this size ships.

Evaluation

Held-out test set (n=4), scored by the platform's LLM judge (an answer fails outright on any coverage violation, making this judge deliberately strict):

	Untrained Qwen3-1.7B	Teacher (gpt-oss-120b)	This model
LLM-as-a-Judge	75.00% (3/4)	75.00% (3/4)	75.00% (3/4)
ROUGE	38.96%	47.17%	50.31%

The judge score saturates because both pass its format bar on most batches; live behavior separates them. This model, on live batches (this GGUF via Ollama, matching the hosted endpoint): exact id coverage on 6/6 — 4 held-out seed batches, one adversarial near-duplicate batch, and one live pipeline batch fed by the other two tools — with rank-1 choices matching the hand-written gold ranking on 3 of the 4 held-out batches and a defensible judgment call on the fourth.

Usage (Ollama)

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

Then in the harness .env:

TOOL_PRIORITIZER_MODEL=posthog-prioritizer

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Safetensors

Model size

2B params

Tensor type

BF16

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

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Quantized

(417)

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