reaperdoesntknow
/

Symbiotic-Beta

@@ -2,165 +2,54 @@
 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
----
-# 🧠 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.
----
-## 📐 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.
----
-### ⚠️ 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.
 ---
-## ⚙️ 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
-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*
----

 library_name: transformers
 tags:
 - generated_from_trainer
 model-index:
 - name: SymLM
   results: []
 ---
+<!-- 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
+This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+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
+### Training results
+### Framework versions
+- Transformers 4.51.3
+- Pytorch 2.7.0+cu126
+- Datasets 3.5.0
+- Tokenizers 0.21.1