Improve language tag

Hi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.

README.md

@@ -1,166 +1,178 @@
----
-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.
-
----
-
-## 📐 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
+- text-generation
+- transformers
+- meta-math
+- qwen2
+- symbolic-ai
+- symbioticlm
+license: afl-3.0
+datasets:
+- meta-math/MetaMathQA
+- open-thoughts/OpenThoughts2-1M
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-0.5B
+pipeline_tag: text-generation
+metrics:
+- accuracy
+model-index:
+- name: SymLM
+  results: []
+---
+
+# 🧠 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*
+
 ---