---
license: gemma
language: en
base_model: google/gemma-3-270m
pipeline_tag: text-generation
tags:
- slm
- distil-labs
- gaming
---

# Distil-NPC-gemma-3-270m
![Alt text](image.jpg)

A **small language model** (SLM) fine-tuned by Distil Labs for interacting with **non-playable characters** using natural language.

## Model Details

* **Developed by:** Distil Labs GmbH
* **License:** Gemma3 License Agreement
* **Finetuned from:** `google/gemma-3-270m`

## Intended Use & Limitations

* **Use cases:** Redacting support chats, logs, tickets, transcripts—removing identity while preserving ops signals (IDs last-4, order numbers, etc.).
* **Out of scope:** Legal or compliance advice; languages beyond English (generalization not guaranteed); domain-specific IDs unseen in training.

## Input & Output

**Input:** A plain-text prompt with a question along with the character the question is asked to. For example:
```
Character: Arin the Cleric
Have you ever had to defend your faith?
```

For a list of available characters along with their biographies, please refer to the data in the following repository:
https://github.com/distil-labs/Distil-NPCs


## Training

Instruction-tuned on made-up character biographies + ~80 curated examples.

## Evaluation

Judged by a frontier LLM using an LLM-as-a-judge metric.

## How to Use
Details of deployment can be found in [docs](https://docs.distillabs.ai/how-to/model-deployment). Deploy the model using vllm or ollama (-gguf version available in this collection) and use the following snippet to get results
```python
SYSTEM_PROMPT = """
You are a problem solving model working on task_description XML block:
<task_description>
Your are non-playable character in a video game. Your task is to answer questions from a player playing the game. When players ask you questions about your life, you need to respond. If you do not know the answer, respond that you do not know.
</task_description>
You will be given a single task in the in the question XML block
Solve only the task in question block.
Generate only the answer, do not generate anything else
"""

question = """Character: Arin the Cleric
Have you ever had to defend your faith?
"""
PROMPT = f"""
Now for the real task, solve the task in {DATA_INPUT} block.
Generate only the solution, do not generate anything else
<question>{question}</question>
"""

from openai import OpenAI

PORT = "PORT GOES HERE"  # 8000 for vllm, 11434 for ollama
MODEL_NAME = "NAME USED FOR SETTING UP THE CLIENT"

client = OpenAI(base_url=f"http://127.0.0.1:{PORT}/v1", api_key="EMPTY")
chat_response = client.chat.completions.create(
    model=MODEL_NAME,
    messages=[
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": PROMPT},
    ],
    temperature=0,
)
```


## Risks & Mitigations

* **Character selection** The model has been finetuned on 81 made-up characters. The aforementioned GitHub reposiotry contains details of the character along with their biograpghies. Behaviour for other characters is not defined.

## Model Sources

* **Homepage:** [https://distillabs.ai](https://distillabs.ai)
* **Contact:** [contact@distillabs.ai](mailto:contact@distillabs.ai)