Improve language tag

README.md

@@ -1,96 +1,107 @@
----
-library_name: transformers
-tags:
-- text-generation-inference
-- Math
-- Code
-- Thinker
-license: apache-2.0
-language:
-- en
-- zh
-base_model:
-- Qwen/Qwen2.5-1.5B-Instruct
-pipeline_tag: text-generation
----
-
-![Thinker.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/fAOdz1WFMBNJdQM2UNEBe.png)
-
-# **Gamma-Velorum-1.5B-Thinker**
-
-> **Gamma-Velorum-1.5B-Thinker** is a **math and code reasoning model** fine-tuned from **Qwen2.5-1.5B**, crafted to tackle complex **mathematical** and **programming** problems using **chain-of-thought** methodology. It excels in **step-by-step explanations**, long-context understanding, and bilingual support — ideal for education, coding tutors, and logic-intensive applications.
-
-## **Key Features**
-
-1. **Math + Code Chain-of-Thought Reasoning**  
-   Trained to provide detailed, structured steps for both **mathematical** and **coding** problems. Gamma-Velorum-1.5B-Thinker explains not just the what, but the *why*, ensuring clarity in logic and computation.
-
-2. **Backed by Qwen2.5-1.5B**  
-   Built on the latest Qwen2.5 architecture, bringing improved accuracy, reasoning capabilities, and enhanced tokenizer efficiency.
-
-3. **Long-Context Problem Solving**  
-   Capable of handling **long multi-turn questions**, nested logic, and extended code/math scenarios — ideal for competitive exams or coding challenges.
-
-4. **Bilingual (English + Chinese)**  
-   Seamlessly understands and reasons through prompts in both **English** and **Simplified Chinese**, making it versatile for global education platforms.
-
-5. **Efficient and Lightweight**  
-   With only 1.5B parameters, it strikes a balance between **performance and deployability**, suitable for web, edge, and mobile environments.
-
-## **Quickstart with Transformers**
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "prithivMLmods/Gamma-Velorum-1.5B-Thinker"
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-prompt = "Write a Python function to calculate factorial of a number."
-messages = [
-    {"role": "system", "content": "You are a helpful tutor skilled in math and programming. Explain solutions step-by-step."},
-    {"role": "user", "content": prompt}
-]
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-
-generated_ids = model.generate(
-    **model_inputs,
-    max_new_tokens=512
-)
-generated_ids = [
-    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
-]
-
-response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-```
-
-## **Intended Use**
-
-- **Math & Coding Tutors**: Solves word problems, algebra, logic puzzles, and programming challenges with clarity and precision.
-- **Bilingual EdTech Apps**: Explains both math and code in English and Chinese for a broader learning reach.
-- **STEM Reasoning Engines**: Powers scientific reasoning tools, code-assist bots, and step-by-step logic solvers.
-- **Lightweight LLM Use Cases**: Browser-based, embedded systems, or mobile apps for learners and developers.
-
-## **Limitations**
-
-1. **Domain Focused**:  
-   Optimized for **STEM and code** tasks — general conversation or abstract creative writing may not be as strong.
-
-2. **Scale Limitations**:  
-   As a 1.5B parameter model, it may not match larger models on highly complex logic or long-form generation.
-
-3. **Bias Inheritance**:  
-   Carries forward biases from its Qwen2.5 base model — important for sensitive contexts.
-
-4. **Prompt Structuring Matters**:  
-   Performs best with explicit, structured prompts for math/code. Ambiguous or casual phrasing may reduce accuracy.
+---
+library_name: transformers
+tags:
+- text-generation-inference
+- Math
+- Code
+- Thinker
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-1.5B-Instruct
+pipeline_tag: text-generation
+---
+
+![Thinker.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/fAOdz1WFMBNJdQM2UNEBe.png)
+
+# **Gamma-Velorum-1.5B-Thinker**
+
+> **Gamma-Velorum-1.5B-Thinker** is a **math and code reasoning model** fine-tuned from **Qwen2.5-1.5B**, crafted to tackle complex **mathematical** and **programming** problems using **chain-of-thought** methodology. It excels in **step-by-step explanations**, long-context understanding, and bilingual support — ideal for education, coding tutors, and logic-intensive applications.
+
+## **Key Features**
+
+1. **Math + Code Chain-of-Thought Reasoning**  
+   Trained to provide detailed, structured steps for both **mathematical** and **coding** problems. Gamma-Velorum-1.5B-Thinker explains not just the what, but the *why*, ensuring clarity in logic and computation.
+
+2. **Backed by Qwen2.5-1.5B**  
+   Built on the latest Qwen2.5 architecture, bringing improved accuracy, reasoning capabilities, and enhanced tokenizer efficiency.
+
+3. **Long-Context Problem Solving**  
+   Capable of handling **long multi-turn questions**, nested logic, and extended code/math scenarios — ideal for competitive exams or coding challenges.
+
+4. **Bilingual (English + Chinese)**  
+   Seamlessly understands and reasons through prompts in both **English** and **Simplified Chinese**, making it versatile for global education platforms.
+
+5. **Efficient and Lightweight**  
+   With only 1.5B parameters, it strikes a balance between **performance and deployability**, suitable for web, edge, and mobile environments.
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Gamma-Velorum-1.5B-Thinker"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Write a Python function to calculate factorial of a number."
+messages = [
+    {"role": "system", "content": "You are a helpful tutor skilled in math and programming. Explain solutions step-by-step."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+
+## **Intended Use**
+
+- **Math & Coding Tutors**: Solves word problems, algebra, logic puzzles, and programming challenges with clarity and precision.
+- **Bilingual EdTech Apps**: Explains both math and code in English and Chinese for a broader learning reach.
+- **STEM Reasoning Engines**: Powers scientific reasoning tools, code-assist bots, and step-by-step logic solvers.
+- **Lightweight LLM Use Cases**: Browser-based, embedded systems, or mobile apps for learners and developers.
+
+## **Limitations**
+
+1. **Domain Focused**:  
+   Optimized for **STEM and code** tasks — general conversation or abstract creative writing may not be as strong.
+
+2. **Scale Limitations**:  
+   As a 1.5B parameter model, it may not match larger models on highly complex logic or long-form generation.
+
+3. **Bias Inheritance**:  
+   Carries forward biases from its Qwen2.5 base model — important for sensitive contexts.
+
+4. **Prompt Structuring Matters**:  
+   Performs best with explicit, structured prompts for math/code. Ambiguous or casual phrasing may reduce accuracy.