Update README.md

README.md

@@ -2,54 +2,164 @@
 library_name: transformers
 tags:
 - generated_from_trainer
+- text-generation
+- transformers
+- meta-math
+- qwen2
+- symbolic-ai
+- symbioticlm
+
 model-index:
 - name: SymLM
   results: []
+license: afl-3.0
+datasets:
+- meta-math/MetaMathQA
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-0.5B
+pipeline_tag: text-generation
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# 🧠 SymLM
+
+**SymbioticLM** is a hybrid symbolic–neural language model that integrates a frozen transformer backbone (`Qwen2ForCausalLM`) with a suite of symbolic cognitive modules for adaptive, interpretable reasoning.
+
+---
 
-# SymLM
+## 📐 Model Description
 
-This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
+The architecture fuses neural token-level generation with symbolic introspection and reasoning:
 
-## Model description
+- **Dynamic Thought Evolution with Helical Encoding and DNA-Inspired Memory (DTE-HDM)**  
+  Enables structured long-term memory and spiral-context encoding across tokens.
 
-More information needed
+- **Multi-Agent Symbiotic Response Mechanisms (M.A.S.R.M)**  
+  Coordinates symbolic-neural agents via gated attention and adaptive response layers.
 
-## Intended uses & limitations
+- **QwenExoCortex**  
+  Projects contextual hidden states from the Qwen model into a symbolic fusion space for reasoning and memory replay.
 
-More information needed
+- **Symbolic processors**  
+  Includes:
+  - `ThoughtDynamicsLNN`
+  - `Liquid / Crystalline Processors`
+  - `Graph Reasoning with DNAConv`
+  - A rolling `ThoughtMemory`
+
+This enables real-time fusion of symbolic thinking, token generation, and reasoning-aware language modeling.
+
+---
 
-## Training and evaluation data
+## 🎯 Intended Uses & Limitations
 
-More information needed
+### ✅ Intended Uses
+
+- **Mathematical reasoning and proof generation**  
+  Fine-tuned on *MetaMathQA*, optimized for symbolic Q&A, equation logic, and structured inference.
+
+- **Symbolic-cognitive AI research**  
+  Useful for studying attention modulation, memory replay, and neural-symbolic interface dynamics.
+
+- **Low-resource adaptation**  
+  Modular memory and projection design enables meaningful performance even with smaller datasets.
+
+- **Building adaptive cognition systems**  
+  Can serve as a symbolic kernel for reflective AI agents and knowledge evolution pipelines.
+
+---
 
-## Training procedure
+### ⚠️ Limitations
 
-### Training hyperparameters
+- **Limited training scale**  
+  Trained on 25,000 MetaMathQA examples. Effective for symbolic form, but not yet broad generalization.
 
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 32
-- eval_batch_size: 8
-- seed: 42
-- gradient_accumulation_steps: 64
-- total_train_batch_size: 2048
-- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_steps: 100
-- num_epochs: 1
-- mixed_precision_training: Native AMP
+- **No RLHF or alignment**  
+  Outputs are not tuned for safety or instruction alignment and may hallucinate.
 
-### Training results
+- **Fluency ≠ correctness**  
+  Symbolic fluency does not imply mathematically valid proofs. Verification is recommended.
 
+- **Not optimized for open-domain generation**  
+  This model prioritizes logic and structure over conversational depth.
+
+---
+
+## ⚙️ Training Procedure
+
+This checkpoint is currently in experimental phase.
+
+### 🧪 Training Hyperparameters
+
+- **learning_rate**: `3e-5`  
+- **train_batch_size**: `16`  
+- **eval_batch_size**: `16`  
+- **gradient_accumulation_steps**: `64`  
+- **total_train_batch_size**: `1024`  
+- **optimizer**: `AdamW`, betas=(0.9, 0.999), epsilon=1e-08  
+- **lr_scheduler_type**: `cosine`  
+- **warmup_steps**: `500`  
+- **num_epochs**: `3`  
+- **mixed_precision_training**: `Native AMP`
+
+---
+
+## 🧱 Framework Versions
+
+- 🤗 Transformers: `4.51.3`  
+- 🧠 PyTorch: `2.7.0+cu126`  
+- 📚 Datasets: `3.5.0`  
+- 🔤 Tokenizers: `0.21.1`
+
+---
 
+## 📚 Research Foundations
 
-### Framework versions
+SymbioticLM builds upon a cohesive theoretical framework for dynamic reasoning and neuro-symbolic learning:
+
+### 🔁 Multi-Agent Symbiosis and Dynamic Thought
+
+**Rapid Adaptation via Multi-Agent Symbiotic Response Mechanisms (M.A.S.R.M)**  
+> A framework where symbolic and neural agents dynamically adapt via gated feedback, memory modulation, and agent-based specialization.
+
+**Focus**: Multi-agent control, reflective learning, contextual responsiveness
+
+---
+
+### 🧬 Dynamic Thought Evolution with Helical Encoding and DNA-Inspired Memory (DTE-HDM)
+
+> A memory structure inspired by biological helices, enabling thought persistence through spiral-layered contextual encodings across time.
+
+**Focus**: Long-term token evolution, normalized replay, thought continuity
+
+---
+
+### 🧠 Integrating DTE-HDM + M.A.S.R.M for Adaptive AI
+
+> Combines symbolic evolution and multi-agent adaptation to construct an LLM that reflects, adapts, and deepens reasoning through internal dynamics.
+
+**Result**: A system that *learns faster*, *adapts deeper*, and *thinks symbolically*
+
+---
+
+### 📐 Theoretical Underpinning
+
+**The Analytic Foundations Theorem (AFT)**  
+> A rigorous, measure-theoretic replacement for classical calculus: replaces pointwise derivatives with discrepancy-driven integral convergence across vanishing sets.
+
+**Applies to**:  
+- Symbolic gradients  
+- Gradient-free optimization  
+- Discrete logic approximation in function spaces
+
+---
+
+These form the **mathematical and architectural core** of SymbioticLM, enabling:
+
+- 🧠 *Neuro-symbolic cognitive evolution*  
+- 🔁 *Multi-agent dynamic feedback coordination*  
+- 📏 *Formal memory through discrepancy-based logic*
+
+---
 
-- Transformers 4.51.3
-- Pytorch 2.7.0+cu126
-- Datasets 3.5.0
-- Tokenizers 0.21.1