This is a QuasiStarSynth-12B fine-tune, produced through P-E-W's Heretic (v1.2.0) abliteration engine with Magnitude-Preserving Orthogonal Ablation enabled.

Heretication Results

Score Metric	Value	Parameter	Value
Refusals	4/100	direction_index	17.59
KL Divergence	0.0096	attn.o_proj.max_weight	2.12
Initial Refusals	93/100	attn.o_proj.max_weight_position	32.22
		attn.o_proj.min_weight	1.85
		attn.o_proj.min_weight_distance	21.32
		mlp.down_proj.max_weight	1.52
		mlp.down_proj.max_weight_position	24.59
		mlp.down_proj.min_weight	0.31
		mlp.down_proj.min_weight_distance	7.19

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 Heresy	Less than 10/100 Refusals and 0.10 KL Divergence
	Tainted Heresy	Around 25-11/100 Refusals and/or -0.20-0.11 KL Divergence
	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.

QuasiStarSynth-12B

From a time before galaxies settled and stars knew their limits, something titanic burned.
Its light was golden, but inside darkness bloomed.
A black heart beating beneath layers of radiant fire, devouring slowly, unseen.
Neither star nor singularity, this was a monument to scale, a paradox wrapped in brilliance.

🔧 Recommended Sampling Settings:

Temperature: 0.75 to 1.25
Min P: 0.035
Context Length: Stable at 12k tokens, with possible support for extended contexts

💬 Prompt Format

Supports ChatML style messages. Example:

<|im_start|>user
Your question here.
<|im_end|>
<|im_start|>assistant

QuasiStarSynth-12B is a merge of the following models using LazyMergekit:

🧩 Configuration

merge_method: ties

base_model: yamatazen/EtherealAurora-12B-v2
models:
  - model: DreadPoor/Irix-12B-Model_Stock
    parameters:
      weight: 0.25
      density: 1.0
  - model: ohyeah1/Violet-Lyra-Gutenberg-v2
    parameters:
      weight: 0.25
      density: 1.0
  - model: redrix/patricide-12B-Unslop-Mell-v2
    parameters:
      weight: 0.25
      density: 1.0
  - model: yamatazen/EtherealAurora-12B-v3
    parameters:
      weight: 0.25
      density: 1.0

parameters:
  normalize: false
  int8_mask: false
dtype: bfloat16

layer_parameters:
  - filter: "attn"
    sources:
      - model: Irix
        weight: 0.5
      - model: Patricide
        weight: 0.3
      - model: Aurora-v3
        weight: 0.2

  - filter: "mlp"
    sources:
      - model: Violet
        weight: 0.5
      - model: Aurora-v3
        weight: 0.3
      - model: Irix
        weight: 0.2

  - filter: "embed_tokens"
    sources:
      - model: Aurora-v2
        weight: 1.0

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Marcjoni/AbyssSynth-12B-12B"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=1, top_k=0, top_p=1)
print(outputs[0]["generated_text"])