README.md

---
base_model:
- mistralai/Mistral-Small-3.1-24B-Instruct-2503
tags:
- text adventure
- roleplay
license: apache-2.0
language:
- en
---

![image/png](harbinger.jpg)

# Harbinger-24B

Like our [Wayfarer line of finetunes](https://huggingface.co/LatitudeGames), Harbinger-24B was designed for immersive adventures and other stories where consequences feel real and every decision matters. Training focused on enhancing instruction following, improving mid-sequence continuation, and strengthening narrative coherence over long sequences of outputs without user intervention. The same DPO (direct preference optimization) techniques used [in our Muse model](https://huggingface.co/LatitudeGames/Muse-12B) were applied to Harbinger, resulting in polished outputs with fewer clichés, repetitive patterns, and other common artifacts. 

If you want to easily try this model, you can do so at [https://aidungeon.com](https://aidungeon.com/). Note that Harbinger requires a subscription while Muse and Wayfarer Small are free.

We plan to continue improving and open-sourcing similar models, so please share any and all feedback on how we can improve model behavior. Below we share more details on how Muse was created.

[Quantized GGUF weights can be downloaded here.](https://huggingface.co/LatitudeGames/Harbinger-24B-GGUF)

## Model details

Harbinger 24B was trained in two stages, on top of Mistral Small 3.1 Instruct.

**SFT** - Various multi-turn datasets from a multitude of sources, focused on [Wayfarer-style](https://huggingface.co/LatitudeGames/Wayfarer-12B) text adventures and general roleplay, each carefully balanced and rewritten to be free of common AI cliches. A small single-turn instruct dataset was included to send a stronger signal during finetuning.

**DPO** - Reward Model User Preference Data, [detailed in our blog](https://blog.latitude.io/all-posts/synthetic-data-preference-optimization-and-reward-models) - This stage refined Harbinger's narrative coherence while preserving its unforgiving essence, resulting in more consistent character behaviors and smoother storytelling flows.

## Inference

Mistral Small 3.1 is sensitive to higher temperatures, so the following settings are recommended as a baseline. Nothing stops you from experimenting with these, of course.

```
"temperature": 0.8,
"repetition_penalty": 1.05,
"min_p": 0.025
```

## Limitations

Harbinger was trained exclusively on second-person present tense data (using “you”) in a narrative style. Other styles will work as well but may produce suboptimal results.

## Prompt Format

ChatML was used during all training stages.

```
<|im_start|>system
You're a masterful storyteller and gamemaster. Write in second person present tense (You are), crafting vivid, engaging narratives with authority and confidence.<|im_end|>
<|im_start|>user
> You peer into the darkness.
<|im_start|>assistant
You have been eaten by a grue.

GAME OVER
```

## Credits

Thanks to [Gryphe Padar](https://huggingface.co/Gryphe) for collaborating on this finetune with us!