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

@@ -1,67 +1,79 @@
----
-base_model:
-- Qwen/Qwen2.5-3B-Instruct
-tags:
-- gguf
-- q4
-- text-generation-inference
-- transformers
-- qwen2
-- trl
-- grpo
-license: apache-2.0
-language:
-- en
----
-
-# TBH.AI Base Reasoning (GGUF - Q4)
-
-- **Developed by:** TBH.AI  
-- **License:** apache-2.0  
-- **Fine-tuned from:** Qwen/Qwen2.5-3B-Instruct  
-- **GGUF Format:** 4-bit quantized (Q4) for optimized inference  
-
-## **Model Description**  
-TBH.AI Base Reasoning (GGUF - Q4) is a **4-bit GGUF quantized** version of `saishshinde15/TBH.AI_Base_Reasoning`, a fine-tuned model based on **Qwen 2.5**. This version is designed for **high-efficiency inference on CPU/GPU with minimal memory usage**, making it ideal for on-device applications and low-latency AI systems.  
-
-Trained using **GRPO (General Reinforcement with Policy Optimization)**, the model excels in **self-reasoning, logical deduction, and structured problem-solving**, comparable to **DeepSeek-R1**. The **Q4 quantization** ensures significantly lower memory requirements while maintaining strong reasoning performance.  
-
-## **Features**  
-- **4-bit Quantization (Q4 GGUF):** Optimized for low-memory, high-speed inference on compatible backends.  
-- **Self-Reasoning AI:** Can process complex queries autonomously, generating logical and structured responses.  
-- **GRPO Fine-Tuning:** Uses policy optimization for improved logical consistency and step-by-step reasoning.  
-- **Efficient On-Device Deployment:** Works seamlessly with **llama.cpp, KoboldCpp, GPT4All, and ctransformers**.  
-- **Ideal for Logical Tasks:** Best suited for **research, coding logic, structured Q&A, and decision-making applications**.  
-
-## **Limitations**  
-- This **Q4 GGUF version is inference-only** and does not support additional fine-tuning.  
-- Quantization may slightly reduce response accuracy compared to FP16/full-precision models.  
-- Performance depends on the execution environment and GGUF-compatible runtime.  
-
-## **Usage** 
-
-# Use this prompt for more detailed and personalized results. This is the recommended prompt as the model was tuned on it.
-
-```python
-You are a reasoning model made by researcher at TBH.AI and your role is to respond in the following format only and in detail :
-
-<reasoning>
-...
-</reasoning>
-<answer>
-...
-</answer>
-```
-
-# Use this prompt for concise representation of answers.
-
-```python
-SYSTEM_PROMPT = """
-Respond in the following format:
-<reasoning>
-...
-</reasoning>
-<answer>
-...
-</answer>
+---
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+tags:
+- gguf
+- q4
+- text-generation-inference
+- transformers
+- qwen2
+- trl
+- grpo
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+
+# TBH.AI Base Reasoning (GGUF - Q4)
+
+- **Developed by:** TBH.AI  
+- **License:** apache-2.0  
+- **Fine-tuned from:** Qwen/Qwen2.5-3B-Instruct  
+- **GGUF Format:** 4-bit quantized (Q4) for optimized inference  
+
+## **Model Description**  
+TBH.AI Base Reasoning (GGUF - Q4) is a **4-bit GGUF quantized** version of `saishshinde15/TBH.AI_Base_Reasoning`, a fine-tuned model based on **Qwen 2.5**. This version is designed for **high-efficiency inference on CPU/GPU with minimal memory usage**, making it ideal for on-device applications and low-latency AI systems.  
+
+Trained using **GRPO (General Reinforcement with Policy Optimization)**, the model excels in **self-reasoning, logical deduction, and structured problem-solving**, comparable to **DeepSeek-R1**. The **Q4 quantization** ensures significantly lower memory requirements while maintaining strong reasoning performance.  
+
+## **Features**  
+- **4-bit Quantization (Q4 GGUF):** Optimized for low-memory, high-speed inference on compatible backends.  
+- **Self-Reasoning AI:** Can process complex queries autonomously, generating logical and structured responses.  
+- **GRPO Fine-Tuning:** Uses policy optimization for improved logical consistency and step-by-step reasoning.  
+- **Efficient On-Device Deployment:** Works seamlessly with **llama.cpp, KoboldCpp, GPT4All, and ctransformers**.  
+- **Ideal for Logical Tasks:** Best suited for **research, coding logic, structured Q&A, and decision-making applications**.  
+
+## **Limitations**  
+- This **Q4 GGUF version is inference-only** and does not support additional fine-tuning.  
+- Quantization may slightly reduce response accuracy compared to FP16/full-precision models.  
+- Performance depends on the execution environment and GGUF-compatible runtime.  
+
+## **Usage** 
+
+# Use this prompt for more detailed and personalized results. This is the recommended prompt as the model was tuned on it.
+
+```python
+You are a reasoning model made by researcher at TBH.AI and your role is to respond in the following format only and in detail :
+
+<reasoning>
+...
+</reasoning>
+<answer>
+...
+</answer>
+```
+
+# Use this prompt for concise representation of answers.
+
+```python
+SYSTEM_PROMPT = """
+Respond in the following format:
+<reasoning>
+...
+</reasoning>
+<answer>
+...
+</answer>
 """