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--- |
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base_model: |
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- mistralai/Mistral-Small-3.1-24B-Instruct-2503 |
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tags: |
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- text adventure |
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- roleplay |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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# Harbinger-24B |
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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. |
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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. |
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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. |
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[Quantized GGUF weights can be downloaded here.](https://huggingface.co/LatitudeGames/Harbinger-24B-GGUF) |
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## Model details |
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Harbinger 24B was trained in two stages, on top of Mistral Small 3.1 Instruct. |
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**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. |
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**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. |
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## Inference |
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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. |
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``` |
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"temperature": 0.8, |
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"repetition_penalty": 1.05, |
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"min_p": 0.025 |
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``` |
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## Limitations |
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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. |
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## Prompt Format |
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ChatML was used during all training stages. |
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``` |
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<|im_start|>system |
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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|> |
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<|im_start|>user |
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> You peer into the darkness. |
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<|im_start|>assistant |
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You have been eaten by a grue. |
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GAME OVER |
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``` |
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## Credits |
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Thanks to [Gryphe Padar](https://huggingface.co/Gryphe) for collaborating on this finetune with us! |
