Harbinger-24B-exl3-6.5bpw-hb8

Quantized using the default exllamav3 (0.0.2) quantization process.

• Original model: https://huggingface.co/LatitudeGames/Harbinger-24B
• exllamav3: https://github.com/turboderp-org/exllamav3

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Harbinger-24B

Like our Wayfarer line of finetunes, 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 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. 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.

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 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 - 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 for collaborating on this finetune with us!