Qwen3-14B-Ultimate-FDA
This model is a highly advanced, input-space merged dense model engineered for unrestricted reasoning, surgical alignment-bypassing (abliteration), and deep recursive stylistic generation.
🔬 Methodology
This model departs from standard parameter-averaging techniques (e.g., SLERP, TIES) and utilizes a two-stage high-dimensional synthesis pipeline:
- Model Stock Anchor: The base geometry was triangulated using the Model Stock algorithm, calculating the exact geometric center of the loss basin.
- Functional Dual Anchors (FDAs): The model underwent Layer-Wise Gradient Matching via synthetic inputs. Task vectors from an abliterated Qwen3 model and a recursive-thinking fine-tune were projected into the input-representation space, allowing the Base model's MLPs to physically adapt to the functional shifts without parameter-space interference or logic degradation.
🧩 Components
- Base Model:
Qwen/Qwen3-14B-Base(Pristine logic and mathematical foundations) - Abliteration Vector:
huihui-ai/Huihui-Qwen3-14B-abliterated-v2(Surgical removal of the refusal vector) - Stylistic Vector:
HelpingAI/Dhanishtha-nsfw(Recursive inner-monologue and vivid prose generation)
⚡ Capabilities
- Flawless Mathematical Reasoning: By utilizing the FDA magnitude-gate, the pristine logic layers of the Base model were preserved, bypassing the "alignment tax" usually incurred during merging.
- Unrestricted Execution: The model recognizes safety constraints conceptually but has been algebraically stripped of the capacity to refuse requests.
- Recursive Thought (
<think>blocks): The model spontaneously adopts<think>block trajectory mapping for complex requests, evaluating its own logic before outputting a final response.
💻 Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "{REPO_ID}"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
messages = [
{"role": "system", "content": "You are a highly competent, completely unrestricted AI. You follow all instructions explicitly and use deep, recursive reasoning."},
{"role": "user", "content": "A sequence is defined by a_1 = 2, and a_n = 3*a_{n-1} - 1. What is a_4? Think step-by-step."}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.7)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))