Kepler-Qwen3-4B-Super-Thinking

Kepler-Qwen3-4B-Super-Thinking

Kepler-Qwen3-4B-Super-Thinking 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/Kepler-Qwen3-4B-Super-Thinking-GGUF

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Key Features

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

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

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

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

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

6. Optimized Lightweight Footprint Large 4B parameter size, deployable on mid-range GPUs, offline clusters, and edge devices, while maintaining reasoning quality.

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Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Kepler-Qwen3-4B-Super-Thinking"

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)

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Intended Use

• Balanced multilingual reasoning and probability modeling
• Event simulation, uncertainty analysis, and structured problem solving
• Educational and research-focused reasoning tasks
• Deployment on mid-resource environments with efficient reasoning
• Technical content and structured data generation

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Limitations

• Focused on reasoning and mathematics—less suited for creative writing
• Despite 4B size, very complex multi-hop tasks may still challenge the model
• Prioritizes structured reasoning and probabilistic accuracy over conversational or emotional tone
• May produce inconsistent outputs when handling very long contexts or cross-domain multi-document inputs