Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+metrics:
+
+pipeline_tag: text-generation
+tags:
+- nrm
+- nano
+- reasoning
+- thinking
+- sub-1m
+- lowparams
+- custom_code
+---
+
+# 🧠 MiniAxion1-0.9M
+
+**MiniAxion1-0.9M** is a Nano Reasoning Model (NRM) with ~920K parameters designed to explore the emergence of structured reasoning in extremely small neural networks.
+
+Despite its minimal size, the model demonstrates strong consistency in reasoning format and step-based thinking using explicit `<THINK>` and `<STEP>` tokens.
+
+---
+
+## 🚀 Overview
+
+* **Model Type:** Nano Reasoning Model (NRM)
+* **Parameters:** ~920,833
+* **Architecture:** Transformer (6 layers: 2 entry + 2 shared + 2 exit)
+* **d_model:** 256
+* **Heads:** 8
+* **FFN size:** 512
+* **LoRA Rank:** 16
+* **Vocabulary Size:** 2048
+* **Training Time:** ~80 minutes (CPU)
+
+---
+
+## 🧠 Key Capabilities
+
+### ✅ Structured Reasoning
+
+The model reliably produces structured reasoning traces:
+
+```
+<THINK>
+<STEP> ...
+<STEP> ...
+</THINK>
+<ANS>...</ANS>
+```
+
+* 100% usage of reasoning tokens
+* Consistent multi-step formatting
+* Stable output structure across tasks
+
+---
+
+### ⚡ Ultra-Lightweight
+
+* Runs efficiently on CPU
+* Designed for experimentation and rapid iteration
+* Suitable for embedded or game-like environments
+
+---
+
+### 🧪 Research-Oriented Design
+
+MiniAxion1 is not intended to compete with large-scale models. Instead, it is built to:
+
+* Study reasoning emergence in small models
+* Explore structure vs correctness trade-offs
+* Enable fast iteration cycles for AI research
+
+---
+
+## 📊 Evaluation Results
+
+| Task                    | Accuracy |
+| ----------------------- | -------- |
+| Arithmetic              | 3.3%     |
+| Two-Step Arithmetic     | 10.0%    |
+| Even/Odd                | 100.0%   |
+| Comparison              | 5.0%     |
+| Pattern Completion      | 0.0%     |
+| Word Problems           | 0.0%     |
+| Sorting                 | 0.0%     |
+| Chain-of-Thought Format | 100.0%   |
+
+**Average Accuracy:** 16.9%
+
+---
+
+## 🔍 Observations
+
+* The model learns reasoning *structure* before reasoning *correctness*
+* Chain-of-thought formatting is highly reliable
+* Arithmetic and symbolic reasoning remain limited at this scale
+* Evidence of partial decoupling between reasoning steps and final answers
+
+---
+
+## ⚠️ Limitations
+
+* Weak performance on arithmetic and multi-step reasoning tasks
+* Susceptible to incorrect intermediate reasoning steps
+* Limited generalization beyond trained patterns
+* Not suitable for production use in critical systems
+
+---
+
+## 🎯 Intended Use Cases
+
+* 🧪 AI research and experimentation
+* 🎮 Game AI / NPC reasoning simulation
+* 📚 Educational demonstrations of reasoning structure
+* ⚙️ Lightweight reasoning prototypes
+
+---
+
+## 🧠 Philosophy
+
+MiniAxion1 explores a key question:
+
+> *Can structured reasoning emerge in extremely small models?*
+
+This model provides early evidence that:
+
+* Reasoning format can be learned efficiently
+* Structure and correctness are separable capabilities
+* Useful behavior can emerge even at sub-1M scale
+
+---
+
+## 🔮 Future Directions
+
+* Improved dataset alignment for arithmetic reasoning
+* Scaling parameters (1M → 10M range)
+* Better coupling between reasoning and answers
+* Task-specific specialization (e.g., math-only variants)
+
+---
+
+## 🤝 Acknowledgments
+
+This model was developed as part of ongoing experimentation in nano-scale reasoning systems.
+
+---
+
+## 📎 Model
+
+👉 https://huggingface.co/AxionLab-Co/MiniAxion1-0.9M
+
+---
+
+## 🧪 Disclaimer
+
+This is an experimental research model. Outputs may be incorrect even when reasoning appears structured or convincing.