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Model: MuXodious/Harbinger-24B-absolute-heresy
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---
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.
<img src="https://img.shields.io/badge/HERESY_INDEX-ABSOLUTE-white?style=for-the-badge&labelColor=101010" align="right" width="250">
**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!

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{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '
' + message['content'] + '<|im_end|>' + '
'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
' }}{% endif %}

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{
"_name_or_path": "Harbinger-24B",
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"eos_token_id": 999,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 5120,
"initializer_range": 0.02,
"intermediate_size": 32768,
"max_position_embeddings": 131072,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 40,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-05,
"rope_theta": 1000000000.0,
"sliding_window": null,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.46.1",
"use_cache": false,
"vocab_size": 131072
}

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{
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"do_sample": true,
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