Instructions to use deepquillapp/trait-verifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepquillapp/trait-verifier with Laya:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use deepquillapp/trait-verifier 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 deepquillapp/trait-verifier:Q8_0
# Run inference directly in the terminal:
llama cli -hf deepquillapp/trait-verifier:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf deepquillapp/trait-verifier:Q8_0
# Run inference directly in the terminal:
llama cli -hf deepquillapp/trait-verifier: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 deepquillapp/trait-verifier:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf deepquillapp/trait-verifier: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 deepquillapp/trait-verifier:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf deepquillapp/trait-verifier:Q8_0

Use Docker

docker model run hf.co/deepquillapp/trait-verifier:Q8_0

Llama new
LM Studio
Jan
Ollama
How to use deepquillapp/trait-verifier with Ollama:
```
ollama run hf.co/deepquillapp/trait-verifier:Q8_0
```
Unsloth Desktop

How to use deepquillapp/trait-verifier with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf deepquillapp/trait-verifier:Q8_0

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": "deepquillapp/trait-verifier:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use deepquillapp/trait-verifier with Docker Model Runner:
```
docker model run hf.co/deepquillapp/trait-verifier:Q8_0
```

Lemonade

How to use deepquillapp/trait-verifier with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull deepquillapp/trait-verifier:Q8_0

Run and chat with the model

lemonade run user.trait-verifier-Q8_0

List all available models

lemonade list

Hermes Agent

How to use deepquillapp/trait-verifier with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf deepquillapp/trait-verifier: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 deepquillapp/trait-verifier:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use deepquillapp/trait-verifier with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf deepquillapp/trait-verifier: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 "deepquillapp/trait-verifier: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"

DeepQuill trait verifier

A fine-tune of Laya (ModernBERT-large encoder plus a typed-decision head). DeepQuill uses it to check character-trait claims that its local extractor pulls from fiction.

For each claim (a target character, field = value, the quote, and the passage), the model answers four yes/no questions:

aboutTarget: is it about that character?
lasting: a lasting attribute, not a momentary state?
literal: literal, not figurative?
asserted: asserted by the narration?

The claim is kept when all four pass.

Files

model.w8.onnx: weight-only 8-bit (MatMulNBits). Activations run in fp32, so a claim's result doesn't depend on what it's batched with.
tokenizer.json
verifier_config.json: the exact question texts, sequence limits and calibration temperature.

Training data

Everything the model was trained on is public:

3,760 labelled claims from real extractor output over US-public-domain novels from 1894–1930 (Project Gutenberg).
Synthetic contemporary passages written for this purpose.

The model was never trained on user manuscripts.

Evaluation

The model was scored on held-out sets that share no books or characters with the training data. Metric: keep-F1.

Test set	This model	Qwen3-4B LLM verifier	No verifier
1920s public-domain claims (524)	0.75	0.32	0.45
Contemporary manuscript claims (157)	0.88	0.24	0.45

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GGUF

Model size

2B params

Architecture

qwen35

Hardware compatibility

8-bit

Model tree for deepquillapp/trait-verifier

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