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| --- |
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| license: apache-2.0 |
| language: |
| - en |
| base_model: |
| - mistralai/Mistral-Nemo-Base-2407 |
| tags: |
| - text adventure |
| - roleplay |
| library_name: transformers |
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| --- |
| |
| [](https://hf.co/QuantFactory) |
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| # QuantFactory/Muse-12B-GGUF |
| This is quantized version of [LatitudeGames/Muse-12B](https://huggingface.co/LatitudeGames/Muse-12B) created using llama.cpp |
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| # Original Model Card |
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| # Muse-12B |
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| Muse brings an extra dimension to any tale—whether you're exploring a fantastical realm, court intrigue, or slice-of-life scenarios where a conversation can be as meaningful as a quest. While it handles adventure capably, Muse truly shines when character relationships and emotions are at the forefront, delivering impressive narrative coherence over long contexts. |
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| If you want to easily try this model for free, you can do so at [https://aidungeon.com](https://aidungeon.com/). |
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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/Muse-12B-GGUF) |
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| ## Model details |
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| Muse 12B was trained using Mistral Nemo 12B as its foundation, with training occurring in three stages: SFT (supervised fine-tuning), followed by two distinct DPO (direct preference optimization) phases. |
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| **SFT** - Various multi-turn datasets from a multitude of sources, combining text adventures of the kind used to finetune [our Wayfarer 12B model](https://huggingface.co/LatitudeGames/Wayfarer-12B), long emotional narratives 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 1** - Gutenberg DPO, [credit to Jon Durbin](https://huggingface.co/datasets/jondurbin/gutenberg-dpo-v0.1) - This stage introduces human writing techniques, significantly enhancing the model's potential outputs, albeit trading some intelligence for the stylistic benefits of human-created text. |
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| **DPO 2** - Reward Model User Preference Data, [detailed in our blog](https://blog.latitude.io/all-posts/synthetic-data-preference-optimization-and-reward-models) - This stage refines the Gutenberg stage's "wildness," restoring intelligence while maintaining enhanced writing quality and providing a final level of enhancement due to the reward model samples. |
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| The result is a model that writes like no other: versatile across genres, natural in expression, and suited to emotional depth. |
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| ## Inference |
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| The Nemo architecture is known for being 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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| ``` |
| "temperature": 0.8, |
| "repetition_penalty": 1.05, |
| "min_p": 0.025 |
| ``` |
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| ## Limitations |
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| Muse 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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| Average response lengths tend toward verbosity (1000+ tokens) due to the Gutenberg DPO influence, though this can be controlled through explicit instructions in the system prompt. |
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| ## Prompt Format |
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| ChatML was used during all training stages. |
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| ``` |
| <|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. |
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| GAME OVER |
| ``` |
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| ## Credits |
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| Thanks to [Gryphe Padar](https://huggingface.co/Gryphe) for collaborating on this finetune with us! |
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