---
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
- open-thoughts/OpenThoughts2-1M
language:
- en
base_model:
- Qwen/Qwen2.5-0.5B
pipeline_tag: text-generation
metrics:
- accuracy
---

# 🧠 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.

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## 📐 Model Description

The architecture fuses neural token-level generation with symbolic introspection and reasoning:

- **Dynamic Thought Evolution with Helical Encoding and DNA-Inspired Memory (DTE-HDM)**  
  Enables structured long-term memory and spiral-context encoding across tokens.

- **Multi-Agent Symbiotic Response Mechanisms (M.A.S.R.M)**  
  Coordinates symbolic-neural agents via gated attention and adaptive response layers.

- **QwenExoCortex**  
  Projects contextual hidden states from the Qwen model into a symbolic fusion space for reasoning and memory replay.

- **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.

---

## 🎯 Intended Uses & Limitations

### ✅ 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.

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### ⚠️ Limitations

- **Limited training scale**  
  Trained on 25,000 MetaMathQA examples. Effective for symbolic form, but not yet broad generalization.

- **No RLHF or alignment**  
  Outputs are not tuned for safety or instruction alignment and may hallucinate.

- **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.

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## ⚙️ 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`

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## 🧱 Framework Versions

- 🤗 Transformers: `4.51.3`  
- 🧠 PyTorch: `2.7.0+cu126`  
- 📚 Datasets: `3.5.0`  
- 🔤 Tokenizers: `0.21.1`

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## 📚 Research Foundations

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

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### 🧬 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

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### 🧠 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*

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### 📐 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

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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*

---