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

@@ -1,119 +1,131 @@
----
-license: apache-2.0
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-72B-Instruct
-pipeline_tag: text-generation
-library_name: transformers
-tags:
-- reasoning
-- logic
-- cot
-- text-generation-inference
-new_version: Daemontatox/Cogito-Maximus
----
-
-![image](./image.webp)
-
-
-## **Model Overview**
-
-This model, **Cogito-Maximus**, is a fine-tuned version of the `unsloth/qwen2.5-72b-instruct-bnb-4bit` base model, optimized for advanced text generation tasks. It leverages the power of **Unsloth** and **Huggingface's TRL (Transformer Reinforcement Learning)** library to achieve faster training and improved performance.
-
-### **Key Features**
-- **Base Model:** `unsloth/qwen2.5-72b-instruct`
-- **Training Acceleration:** Trained 2x faster using [Unsloth](https://github.com/unslothai/unsloth).
-- **Fine-Tuning Framework:** Utilizes Huggingface's [TRL](https://github.com/huggingface/trl) library.
-- **Optimized for Inference:** Ready for deployment in text-generation tasks with efficient inference capabilities.
-- **License:** Apache-2.0
-
----
-
-## **Model Details**
-
-### **Developed by**
-- **Author:** Daemontatox
-- **Organization:** Independent Contributor
-
-### **Tags**
-- Text Generation Inference
-- Transformers
-- Unsloth
-- Qwen2
-- TRL
-
-### **Language**
-- English (`en`)
-
-### **License**
-This model is released under the **Apache-2.0 License**, which allows for free use, modification, and distribution, provided the original license and copyright notice are included.
-
----
-
-## **Model Training**
-
-### **Base Model**
-The model is derived from the `unsloth/qwen2.5-72b-instruct`, a  version of the Qwen2.5-72B instruction-tuned model. The base model is optimized for efficiency using **bitsandbytes (bnb)** 4-bit quantization.
-
-### **Training Process**
-- **Framework:** The model was fine-tuned using **Unsloth**, a library designed to accelerate the training of large language models.
-- **Acceleration:** Training was completed **2x faster** compared to traditional methods, thanks to Unsloth's optimizations.
-- **Reinforcement Learning:** Fine-tuning incorporated techniques from Huggingface's **TRL** library, enabling advanced instruction-tuning and alignment with human preferences.
-
----
-
-## **Intended Use**
-
-### **Primary Use Case**
-This model is designed for **text generation tasks**, including but not limited to:
-- Instruction-following
-- Question answering
-- Content creation
-- Dialogue systems
-
-### **Limitations**
-- The model is trained primarily on English data and may not perform as well on other languages.
-- While fine-tuned for instruction-following, outputs should be reviewed for accuracy and relevance in critical applications.
-
----
-
-## **How to Use**
-
-### **Installation**
-To use this model, ensure you have the following libraries installed:
-```bash
-pip install transformers torch bitsandbytes unsloth trl
-```
-
-
-
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# Load the tokenizer and model
-model_name = "Daemontatox/Cogito-Maximus"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_4bit=True)
-
-# Generate text
-input_text = "Explain the concept of machine learning in simple terms."
-inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
-outputs = model.generate(**inputs, max_length=100)
-
-# Decode and print the output
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-
-```
-
-```
-@misc{daemontatox_cogito_maximus,
-  author = {Daemontatox},
-  title = {Cogito-Maximus: Fine-tuned Qwen2.5-72B Instruct Model},
-  year = {2025},
-  publisher = {Hugging Face},
-  journal = {Hugging Face Model Repository},
-  howpublished = {\url{https://huggingface.co/Daemontatox/Cogito-Maximus}}
-}
+---
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-72B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- reasoning
+- logic
+- cot
+- text-generation-inference
+new_version: Daemontatox/Cogito-Maximus
+---
+
+![image](./image.webp)
+
+
+## **Model Overview**
+
+This model, **Cogito-Maximus**, is a fine-tuned version of the `unsloth/qwen2.5-72b-instruct-bnb-4bit` base model, optimized for advanced text generation tasks. It leverages the power of **Unsloth** and **Huggingface's TRL (Transformer Reinforcement Learning)** library to achieve faster training and improved performance.
+
+### **Key Features**
+- **Base Model:** `unsloth/qwen2.5-72b-instruct`
+- **Training Acceleration:** Trained 2x faster using [Unsloth](https://github.com/unslothai/unsloth).
+- **Fine-Tuning Framework:** Utilizes Huggingface's [TRL](https://github.com/huggingface/trl) library.
+- **Optimized for Inference:** Ready for deployment in text-generation tasks with efficient inference capabilities.
+- **License:** Apache-2.0
+
+---
+
+## **Model Details**
+
+### **Developed by**
+- **Author:** Daemontatox
+- **Organization:** Independent Contributor
+
+### **Tags**
+- Text Generation Inference
+- Transformers
+- Unsloth
+- Qwen2
+- TRL
+
+### **Language**
+- English (`en`)
+
+### **License**
+This model is released under the **Apache-2.0 License**, which allows for free use, modification, and distribution, provided the original license and copyright notice are included.
+
+---
+
+## **Model Training**
+
+### **Base Model**
+The model is derived from the `unsloth/qwen2.5-72b-instruct`, a  version of the Qwen2.5-72B instruction-tuned model. The base model is optimized for efficiency using **bitsandbytes (bnb)** 4-bit quantization.
+
+### **Training Process**
+- **Framework:** The model was fine-tuned using **Unsloth**, a library designed to accelerate the training of large language models.
+- **Acceleration:** Training was completed **2x faster** compared to traditional methods, thanks to Unsloth's optimizations.
+- **Reinforcement Learning:** Fine-tuning incorporated techniques from Huggingface's **TRL** library, enabling advanced instruction-tuning and alignment with human preferences.
+
+---
+
+## **Intended Use**
+
+### **Primary Use Case**
+This model is designed for **text generation tasks**, including but not limited to:
+- Instruction-following
+- Question answering
+- Content creation
+- Dialogue systems
+
+### **Limitations**
+- The model is trained primarily on English data and may not perform as well on other languages.
+- While fine-tuned for instruction-following, outputs should be reviewed for accuracy and relevance in critical applications.
+
+---
+
+## **How to Use**
+
+### **Installation**
+To use this model, ensure you have the following libraries installed:
+```bash
+pip install transformers torch bitsandbytes unsloth trl
+```
+
+
+
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the tokenizer and model
+model_name = "Daemontatox/Cogito-Maximus"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_4bit=True)
+
+# Generate text
+input_text = "Explain the concept of machine learning in simple terms."
+inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_length=100)
+
+# Decode and print the output
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+```
+
+```
+@misc{daemontatox_cogito_maximus,
+  author = {Daemontatox},
+  title = {Cogito-Maximus: Fine-tuned Qwen2.5-72B Instruct Model},
+  year = {2025},
+  publisher = {Hugging Face},
+  journal = {Hugging Face Model Repository},
+  howpublished = {\url{https://huggingface.co/Daemontatox/Cogito-Maximus}}
+}
 ```