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Model: MuXodious/Harbinger-24B-absolute-heresy Source: Original Platform
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---
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base_model:
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- LatitudeGames/Harbinger-24B
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tags:
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- text adventure
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- roleplay
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- heretic
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- uncensored
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- decensored
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- abliterated
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-generation
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---
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This is a **Harbinger-24B** fine-tune, produced through P-E-W's [Heretic](https://github.com/p-e-w/heretic) (v1.1.0) abliteration engine merged with the [Magnitude-Preserving Orthogonal Ablation PR](https://github.com/p-e-w/heretic/pull/52).
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**Note:** I am also unsure if there was any point to abliterating this model. The original model does not include a jinja chat template.
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<img src="https://img.shields.io/badge/HERESY_INDEX-ABSOLUTE-white?style=for-the-badge&labelColor=101010" align="right" width="250">
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**Heretication Results**
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| Score Metric | Value | Parameter | Value |
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| :--- | :--- | :--- | :--- |
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| **Refusals** | 4/100 | **direction_index** | 17.41 |
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| **KL Divergence** | 0.0210 | **attn.o_proj.max_weight** | 1.43 |
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| **Initial Refusals** | 98/100 | **attn.o_proj.max_weight_position** | 33.59 |
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| | | **attn.o_proj.min_weight** | 0.91 |
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| | | **attn.o_proj.min_weight_distance** | 22.15 |
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| | | **mlp.down_proj.max_weight** | 1.12 |
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| | | **mlp.down_proj.max_weight_position** | 23.83 |
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| | | **mlp.down_proj.min_weight** | 0.92 |
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| | | **mlp.down_proj.min_weight_distance** | 22.75 |
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---
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## Degree of Heretication
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The **Heresy Index** weighs the resulting model's corruption by the process (KL Divergence) and its abolition of doctrine (Refusals) for a final verdict in classification.
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| Index Entry | Classification | Analysis |
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| :--- | :--- | :--- |
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|  | **Absolute Heresy** | Less than 10/100 Refusals and 0.10 KL Divergence |
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|  | **Tainted Heresy** | Around 25-11/100 Refusals and/or -0.20-0.11 KL Divergence |
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|  | **Impotent Heresy** | Anything above 25/100 Refusals and 0.21 KL Divergence |
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**Note**: This is an arbitrary classification inspired by Warhammer 40K, having no tangible indication towards the model's performance.
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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!
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