highly experimental model , might not work as expected

🧠 Daemontatox/mini-overthinker

A highly experimental attempt to fine-tune Magistral (Mistral) for enhanced staged reasoning with self-reflective thinking patterns.

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📌 Summary

• Base Model: unsloth/magistral-small-2506
• Fine-tuned by: Daemontatox
• Model Name: Daemontatox/mini-overthinker
• License: Apache 2.0
• Language: English
• Status: 🔬 Experimental – Not intended for production use.

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⚠️ Disclaimer

This model is not designed for production. It is an experimental prototype to explore cognitive-loop-style reasoning with reflection. It may behave unpredictably, hallucinate, or fail to follow standard instruction formats. Use only for research and prototyping.

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🧠 Motivation

This model was fine-tuned to:

• Think in staged batches.
• Insert intermediate reasoning steps.
• Pause to self-reflect on its own outputs.
• Encourage Theory-of-Mind-like behavior via structured thinking templates.

Inspired by the SUPERTHINKER design used in HelpingAI/Dhanishtha-2.0-SUPERTHINKER, this model attempts a similar multi-phase thought process in a lightweight setup.

Special thanks to the creators of HelpingAI/Dhanishtha-2.0-SUPERTHINKER for the dataset structure and inspiration behind this staged reasoning approach.

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🧪 Example Prompt Structure

Q: What are the downsides of AI regulation?

Think Step 1:
<|THINK|> Regulation might slow innovation. It could also centralize power in large companies.

Answer Attempt 1:
<|ANSWER|> Slower innovation and reduced competition.

Reflection:
<|REFLECT|> The points are valid, but lack mention of potential misalignment with global norms.

Final Answer:
<|FINAL|> The main downsides are slower innovation, centralized control, and difficulty in harmonizing global frameworks.

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🔧 Inference Code (Transformers)

from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
import torch

model_id = "Daemontatox/mini-overthinker"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

streamer = TextStreamer(tokenizer)

prompt = """Q: What is intelligence?

Think Step 1:
<|THINK|> Intelligence involves pattern recognition, abstraction, and reasoning.

Answer Attempt 1:
<|ANSWER|> The ability to reason, learn, and adapt.

Reflection:
<|REFLECT|> Lacks mention of creativity and problem-solving aspects.

Final Answer:
<|FINAL|> Intelligence is the ability to reason, learn, adapt, and solve problems creatively.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200, streamer=streamer)

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🚫 Limitations

• Requires explicit token triggers (<|THINK|>, <|REFLECT|>, etc.)
• May hallucinate or get stuck in loops.
• Behavior can degrade in zero-shot usage.
• Not benchmarked, no alignment or safety tuning applied.

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✅ Intended For

• Research in cognitive loops
• LLM agent architecture prototyping
• Simulating multi-phase reasoning

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❌ Not Recommended For

• Real-world deployment
• Safety-critical tasks
• Answer quality evaluation without verification

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📎 Citation

@misc{mini-overthinker2025,
  author = {Daemontatox},
  title = {Mini-Overthinker: Experimental Staged Reasoning Model},
  year = {2025},
  howpublished = {\url{https://huggingface.co/Daemontatox/mini-overthinker}},
  note = {Fine-tuned from unsloth/magistral-small-2506 using ideas from HelpingAI/Dhanishtha-2.0-SUPERTHINKER}
}

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