Improve language tag

README.md

@@ -1,96 +1,108 @@
----
-library_name: transformers
-tags:
-- math
-- cot
-- text-generation-inference
-- preview
-- experimental
-license: apache-2.0
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-1.5B-Instruct
-pipeline_tag: text-generation
----
-
-![DMC.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/WYWprTh49LUnIw-HiTcU-.png)
-
-# **Deepmath-Competitive-1.5B-Preview**
-
-> **Deepmath-Competitive-1.5B-Preview** is a **chain-of-thought reasoning model** fine-tuned from **Qwen-1.5B**, purpose-built for solving **mathematical problems** in both **English** and **Chinese** with a focus on **long-context understanding**. It enables advanced reasoning and detailed step-by-step problem solving in a compact form — ideal for competitive exam preparation, tutoring systems, and math-focused AI assistants.
-
-## **Key Features**
-
-1. **Chain-of-Thought Math Reasoning**  
-   Specifically trained to output detailed intermediate steps for math problems, Deepmath-Competitive-1.5B-Preview ensures interpretability and logical clarity — vital for learning and validation.
-
-2. **Bilingual Proficiency (English + Chinese)**  
-   Proficient in understanding and solving math problems in **both English and Simplified Chinese**, supporting diverse educational needs.
-
-3. **Long-Context Reasoning**  
-   Optimized for **long-form math problems** and word problem comprehension, enabling reasoning over extended contexts and compound queries.
-
-4. **Compact yet Powerful**  
-   With just 1.5B parameters, it delivers robust performance on arithmetic, algebra, geometry, logic, and competitive exam-style word problems with minimal computational cost.
-
-5. **Structured Step-by-Step Computation**  
-   Produces clean, stepwise outputs that mimic expert human problem-solving, helping learners follow the process and logic intuitively.
-
-## **Quickstart with Transformers**
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "prithivMLmods/Deepmath-Competitive-1.5B-Preview"
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-prompt = "Solve: A train travels 180 km in 3 hours. What is its average speed?"
-messages = [
-    {"role": "system", "content": "You are a helpful tutor skilled in solving math problems with step-by-step explanations."},
-    {"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 Tutoring Bots**: Delivers in-depth, multi-step solutions for students preparing for competitive and school-level math.
-- **Bilingual Educational Apps**: Effective in English and Chinese teaching environments.
-- **STEM Reasoning Tools**: Supports structured reasoning across science and engineering questions.
-- **Compact LLM Deployments**: Suitable for low-latency environments like mobile apps, edge devices, or web integrations.
-
-## **Limitations**
-
-1. **Domain Focus**:  
-   Primarily tuned for mathematics; performance may drop outside STEM or logical domains.
-
-2. **Model Scale**:  
-   While efficient, it may underperform on abstract or research-level problems compared to larger models.
-
-3. **Inherited Biases**:  
-   As a fine-tune of Qwen-1.5B, some pretraining biases may persist. Review is advised in critical applications.
-
-4. **Prompt Sensitivity**:  
+---
+library_name: transformers
+tags:
+- math
+- cot
+- text-generation-inference
+- preview
+- experimental
+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
+---
+
+![DMC.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/WYWprTh49LUnIw-HiTcU-.png)
+
+# **Deepmath-Competitive-1.5B-Preview**
+
+> **Deepmath-Competitive-1.5B-Preview** is a **chain-of-thought reasoning model** fine-tuned from **Qwen-1.5B**, purpose-built for solving **mathematical problems** in both **English** and **Chinese** with a focus on **long-context understanding**. It enables advanced reasoning and detailed step-by-step problem solving in a compact form — ideal for competitive exam preparation, tutoring systems, and math-focused AI assistants.
+
+## **Key Features**
+
+1. **Chain-of-Thought Math Reasoning**  
+   Specifically trained to output detailed intermediate steps for math problems, Deepmath-Competitive-1.5B-Preview ensures interpretability and logical clarity — vital for learning and validation.
+
+2. **Bilingual Proficiency (English + Chinese)**  
+   Proficient in understanding and solving math problems in **both English and Simplified Chinese**, supporting diverse educational needs.
+
+3. **Long-Context Reasoning**  
+   Optimized for **long-form math problems** and word problem comprehension, enabling reasoning over extended contexts and compound queries.
+
+4. **Compact yet Powerful**  
+   With just 1.5B parameters, it delivers robust performance on arithmetic, algebra, geometry, logic, and competitive exam-style word problems with minimal computational cost.
+
+5. **Structured Step-by-Step Computation**  
+   Produces clean, stepwise outputs that mimic expert human problem-solving, helping learners follow the process and logic intuitively.
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Deepmath-Competitive-1.5B-Preview"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Solve: A train travels 180 km in 3 hours. What is its average speed?"
+messages = [
+    {"role": "system", "content": "You are a helpful tutor skilled in solving math problems with step-by-step explanations."},
+    {"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 Tutoring Bots**: Delivers in-depth, multi-step solutions for students preparing for competitive and school-level math.
+- **Bilingual Educational Apps**: Effective in English and Chinese teaching environments.
+- **STEM Reasoning Tools**: Supports structured reasoning across science and engineering questions.
+- **Compact LLM Deployments**: Suitable for low-latency environments like mobile apps, edge devices, or web integrations.
+
+## **Limitations**
+
+1. **Domain Focus**:  
+   Primarily tuned for mathematics; performance may drop outside STEM or logical domains.
+
+2. **Model Scale**:  
+   While efficient, it may underperform on abstract or research-level problems compared to larger models.
+
+3. **Inherited Biases**:  
+   As a fine-tune of Qwen-1.5B, some pretraining biases may persist. Review is advised in critical applications.
+
+4. **Prompt Sensitivity**:  
    Performs best with clearly structured prompts and formal question phrasing.