prithivMLmods/Leporis-Qwen3-Radiation-1.7B

Leporis-Qwen3-Radiation-1.7B

Leporis-Qwen3-Radiation-1.7B 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/Leporis-Qwen3-Radiation-1.7B-GGUF

Key Features

1. 1.Abliterated Reasoning Enhanced reasoning precision through polished token probability distributions in Qwen and similar models, ensuring balanced and context-aware outputs.

1. 1.Event Simulation & Logical Analysis Models random events, probability-driven reasoning, and logical decision-making with strong consistency.

1. 1.Multilingual Mathematical & General-Purpose Problem Solving Delivers robust performance in math, probability, and structured multilingual tasks, enabling wide applicability in global research and education.

1. 1.Hybrid Symbolic-Probabilistic Thinking Combines structured logic, probabilistic inference, and reasoning fluency, providing accuracy across uncertainty-driven tasks.

1. 1.Structured Output Mastery Generates well-structured outputs in LaTeX, Markdown, JSON, CSV, and YAML, supporting technical workflows and data-driven research.

1. 1.Optimized Lightweight Footprint Compact 1.7B parameter size, deployable on edge devices, offline clusters, and mid-range GPUs, while maintaining reasoning quality.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Leporis-Qwen3-Radiation-1.7B"

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 compared to large-scale LLMs may limit performance on complex, multi-hop reasoning tasks
• —Prioritizes structured reasoning and probabilistic accuracy over conversational or emotional tone
• —May produce inconsistent outputs when dealing with very long contexts or cross-domain multi-document inputs