--- base_model: - LatitudeGames/Harbinger-24B tags: - text adventure - roleplay - heretic - uncensored - decensored - abliterated license: apache-2.0 language: - en pipeline_tag: text-generation --- 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). **Note:** I am also unsure if there was any point to abliterating this model. The original model does not include a jinja chat template. **Heretication Results** | Score Metric | Value | Parameter | Value | | :--- | :--- | :--- | :--- | | **Refusals** | 4/100 | **direction_index** | 17.41 | | **KL Divergence** | 0.0210 | **attn.o_proj.max_weight** | 1.43 | | **Initial Refusals** | 98/100 | **attn.o_proj.max_weight_position** | 33.59 | | | | **attn.o_proj.min_weight** | 0.91 | | | | **attn.o_proj.min_weight_distance** | 22.15 | | | | **mlp.down_proj.max_weight** | 1.12 | | | | **mlp.down_proj.max_weight_position** | 23.83 | | | | **mlp.down_proj.min_weight** | 0.92 | | | | **mlp.down_proj.min_weight_distance** | 22.75 | --- ## Degree of Heretication 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. | Index Entry | Classification | Analysis | | :--- | :--- | :--- | | ![Absolute](https://img.shields.io/badge/HERESY_INDEX-ABSOLUTE-white?style=flat-square&labelColor=101010) | **Absolute Heresy** | Less than 10/100 Refusals and 0.10 KL Divergence | | ![Tainted](https://img.shields.io/badge/HERESY_INDEX-TAINTED-blueviolet?style=flat-square&labelColor=101010) | **Tainted Heresy** | Around 25-11/100 Refusals and/or -0.20-0.11 KL Divergence | | ![Impotent](https://img.shields.io/badge/HERESY_INDEX-IMPOTENT-5c4033?style=flat-square&labelColor=101010) | **Impotent Heresy** | Anything above 25/100 Refusals and 0.21 KL Divergence | **Note**: This is an arbitrary classification inspired by Warhammer 40K, having no tangible indication towards the model's performance. --- ![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!