PIT - Police Interview Trainer

FOR TRAINING AND RESEARCH PURPOSES ONLY. Not for operational policing, legal advice, or use as evidence in any proceedings. The creator accepts no responsibility or liability for any use or misuse of this model. Model outputs may be inaccurate or incomplete.

Model Description

PIT (Police Interview Trainer) is a domain-adapted language model for UK police interview roleplay training. It simulates realistic suspect behaviour across multiple scenario types, enabling trainee officers to practise the PEACE interview framework in a safe environment.

Base model: unsloth/gpt-oss-20b — a 21B parameter Mixture-of-Experts model with 3.6B active parameters per forward pass.

Training Pipeline

The model was created through a three-stage training pipeline:

1. Continued Pre-Training (CPT) — UK Criminal Law

Corpus: ~10.7 million tokens of UK criminal law material
Coverage: Legislation, case law, PACE codes, CPS guidance, sentencing guidelines
Adapter: LoRA r=64, 3 epochs, 1,971 steps

2. Continued Pre-Training (CPT) — Police Interview Technique

Corpus: ~53,000 tokens of PIP Level 1 interview training material
Coverage: PEACE framework, questioning techniques, suspect management, vulnerable persons
Adapter: LoRA r=32, 10 epochs, 80 steps
Stacked on: Stage 1 adapter

3. Supervised Fine-Tuning (SFT) — Interview Roleplay

Dataset: 523 examples across 6 interaction modes
Adapter: LoRA r=32, 3 epochs, 198 steps
Stacked on: Stage 1 + Stage 2 adapters

SFT Modes

Mode	Examples	Description
Suspect roleplay	200	In-character suspect responses (cooperative, deceptive, no-comment)
Assessment	120	Post-interview PIP Level 1 assessment feedback
PEACE knowledge	80	Direct Q&A about PEACE framework and interview law
Witness roleplay	60	In-character witness responses
Scenario presentation	33	Generating interview briefing scenarios
Special procedures	30	Handling vulnerable suspects, appropriate adults, mental health

Quick Start with Docker

git clone https://huggingface.co/EryriLabs/PIT
cd PIT
# Or clone the application repo which includes the Docker setup

Using the full application (recommended)

The PIT application includes a web interface with scenario selection, interview simulation, transcript recording, and automated assessment.

cd pit-app
docker compose up

Then open http://localhost:3000.

Requirements:

Docker with NVIDIA Container Toolkit
GPU with 48GB+ VRAM (or 2x 24GB GPUs)
~48GB disk space

Using with Transformers directly

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "EryriLabs/PIT",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("EryriLabs/PIT", trust_remote_code=True)

messages = [
    {"role": "system", "content": "You are PIT (Police Interview Trainer), simulating a suspect in a police interview training exercise.\n\nYOUR CHARACTER: Tyler Bennett, 23 years old, male.\nBEHAVIOUR: cooperative\n\nINSTRUCTIONS:\n- Stay in character throughout\n- Use natural everyday speech\n- Keep responses to 1-3 sentences"},
    {"role": "user", "content": "I am cautioning you. You do not have to say anything. But it may harm your defence if you do not mention when questioned something which you later rely on in court. Anything you do say may be given in evidence. Do you understand the caution?"}
]

input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, do_sample=True)

response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(response)

Intended Use

Police interview training and education
Academic research into interview techniques
Roleplay simulation for PEACE framework practice
PIP Level 1 assessment preparation

Out of Scope

Operational policing decisions
Legal advice or guidance
Evidence in any legal proceedings
Replacement for human interview training supervision
Any commercial use without explicit permission

Technical Details

Architecture: Mixture-of-Experts (MoE), 21B total / 3.6B active parameters
Precision: bfloat16
Training method: QLoRA (4-bit quantised base, 16-bit adapters)
Hardware: 2x NVIDIA RTX 3090 (24GB each)
Framework: Unsloth + HuggingFace Transformers

Disclaimer

THIS MODEL IS PROVIDED FOR TRAINING AND RESEARCH PURPOSES ONLY.

This model is not intended for, and should not be used in, operational policing, legal proceedings, or any context where its outputs could affect real individuals or cases. The model may generate inaccurate, incomplete, or inappropriate content. The creator accepts no responsibility or liability whatsoever for any use or misuse of this model or its outputs.

Users are solely responsible for ensuring their use complies with all applicable laws and regulations.

Training data might contain public sector information licensed under the Open Government Licence v3.0 and information licensed under the Non-Commercial College Licence.

License

CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial 4.0 International)

Citation

@misc{eryrilabs2026pit,
  title={PIT: Police Interview Trainer},
  author={EryriLabs},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/EryriLabs/PIT}
}