Deneb-Qwen3-Radiation-0.6B is a reasoning-focused model fine-tuned on Qwen for Abliterated Reasoning and polished token probabilities, enhancing balanced multilingual generation across mathematics and general-purpose reasoning. It specializes in event-driven logic, structured analysis, and precise probabilistic modeling—making it an ideal tool for researchers, educators, and developers working with uncertainty and structured reasoning.

[!note] GGUF: https://huggingface.co/prithivMLmods/Deneb-Qwen3-Radiation-0.6B-GGUF

Key Features

Abliterated Reasoning Enhanced reasoning precision through polished token probability distributions in Qwen and similar models, ensuring balanced and context-aware outputs.
Event Simulation & Logical Analysis Models random events, probability-driven reasoning, and logical decision-making with strong consistency.
Multilingual Mathematical & General-Purpose Problem Solving Delivers robust performance in math, probability, and structured multilingual tasks, enabling wide applicability in global research and education.
Hybrid Symbolic-Probabilistic Thinking Combines structured logic, probabilistic inference, and reasoning fluency, providing accuracy across uncertainty-driven tasks.
Structured Output Mastery Generates well-structured outputs in LaTeX, Markdown, JSON, CSV, and YAML, supporting technical workflows and data-driven research.
Optimized Lightweight Footprint Compact 0.6B parameter size, deployable on edge devices, offline clusters, and mid-range GPUs, while maintaining reasoning quality.

Quickstart with Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Deneb-Qwen3-Radiation-0.6B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Simulate the probability of rolling two dice and getting a sum greater than 9. Show the reasoning."

messages = [
    {"role": "system", "content": "You are a reasoning tutor skilled in probability, logic, and multilingual problem-solving."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Intended Use

Balanced multilingual reasoning and probability modeling
Event simulation, uncertainty analysis, and structured problem solving
Educational and research-focused reasoning tasks
Lightweight deployment in constrained environments
Technical content and structured data generation

Limitations

Focused on reasoning and mathematics—less suited for creative writing
Smaller size (0.6B) may limit depth on highly complex, multi-step tasks
Prioritizes structured reasoning and probabilistic accuracy over conversational or emotional tone.