Improve language tag

README.md

@@ -1,97 +1,109 @@
----
-library_name: transformers
-tags:
-- text-generation-inference
-- PRM
-- Code
-- Math
-license: apache-2.0
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-1.5B-Instruct
-pipeline_tag: text-generation
----
-
-![PRM.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/2inJGKPx_BrMcID7Osto-.png)
-
-# **Deepthink-1.5B-Open-PRM**
-
-> **Deepthink-1.5B-Open-PRM** is a **process-supervised reasoning model** fine-tuned from **Qwen2.5 1.5B** using **Process Reward Models (PRM)**. It excels at **step-by-step mathematical problem solving** in both **English** and **Simplified Chinese**, offering interpretable, logically structured responses for use in **education**, **STEM tutoring**, and **lightweight math agents**.
-
-## **Key Features**
-
-1. **Process Reward Model Supervision (PRM)**  
-   Fine-tuned with PRMs to reward high-quality intermediate reasoning steps — fostering step-by-step interpretability, accuracy, and educational transparency.
-
-2. **Compact Foundation (Qwen2.5 0.5B)**  
-   Built upon the highly efficient Qwen2.5 1.5B architecture and scaled up through distillation and reward-based alignment to 1.5B parameters, balancing reasoning quality and deployment efficiency.
-
-3. **Bilingual Math Capability**  
-   Fluent in solving and explaining math problems in both **English** and **Simplified Chinese**, making it ideal for multilingual classrooms and tutoring platforms.
-
-4. **Process-Supervised Math Reasoning**  
-   Trained to reason like a teacher — showing each logical step before delivering an answer. Ideal for learners who need to understand the “how” and “why” behind each solution.
-
-5. **Long-Context & Word Problem Reasoning**  
-   Especially proficient with multi-step arithmetic, word problems, logic puzzles, and middle school to early college-level math.
-
-## **Quickstart with Transformers**
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "prithivMLmods/Deepthink-1.5B-Open-PRM"
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-prompt = "Solve: A tank can be filled by one pipe in 6 hours and emptied by another in 9 hours. How long will it take to fill the tank if both pipes are opened together?"
-
-messages = [
-    {"role": "system", "content": "You are a helpful math tutor who explains each step clearly."},
-    {"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 Education Agents**: Tutors that explain problems step by step, helping users build understanding through reasoning.
-- **Bilingual Learning Platforms**: Apps that teach math in both Chinese and English.
-- **STEM-Oriented Assistants**: Supports early-stage problem solving in science and engineering contexts.
-- **Lightweight LLM Deployments**: Optimized for low-resource environments, from browsers to mobile devices.
-
-## **Limitations**
-
-1. **Domain Specificity**  
-   Primarily tuned for math reasoning — performance may degrade on unrelated tasks like creative writing or open dialogue.
-
-2. **Model Size Constraint**  
-   While efficient, 1.5B parameters may struggle with highly abstract or very long multi-domain tasks.
-
-3. **PRM Bias Generalization**  
-   PRM training can bias toward rewardable structures — results should still be reviewed for correctness and completeness.
-
-4. **Prompt Structure Sensitivity**  
+---
+library_name: transformers
+tags:
+- text-generation-inference
+- PRM
+- Code
+- Math
+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
+---
+
+![PRM.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/2inJGKPx_BrMcID7Osto-.png)
+
+# **Deepthink-1.5B-Open-PRM**
+
+> **Deepthink-1.5B-Open-PRM** is a **process-supervised reasoning model** fine-tuned from **Qwen2.5 1.5B** using **Process Reward Models (PRM)**. It excels at **step-by-step mathematical problem solving** in both **English** and **Simplified Chinese**, offering interpretable, logically structured responses for use in **education**, **STEM tutoring**, and **lightweight math agents**.
+
+## **Key Features**
+
+1. **Process Reward Model Supervision (PRM)**  
+   Fine-tuned with PRMs to reward high-quality intermediate reasoning steps — fostering step-by-step interpretability, accuracy, and educational transparency.
+
+2. **Compact Foundation (Qwen2.5 0.5B)**  
+   Built upon the highly efficient Qwen2.5 1.5B architecture and scaled up through distillation and reward-based alignment to 1.5B parameters, balancing reasoning quality and deployment efficiency.
+
+3. **Bilingual Math Capability**  
+   Fluent in solving and explaining math problems in both **English** and **Simplified Chinese**, making it ideal for multilingual classrooms and tutoring platforms.
+
+4. **Process-Supervised Math Reasoning**  
+   Trained to reason like a teacher — showing each logical step before delivering an answer. Ideal for learners who need to understand the “how” and “why” behind each solution.
+
+5. **Long-Context & Word Problem Reasoning**  
+   Especially proficient with multi-step arithmetic, word problems, logic puzzles, and middle school to early college-level math.
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Deepthink-1.5B-Open-PRM"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Solve: A tank can be filled by one pipe in 6 hours and emptied by another in 9 hours. How long will it take to fill the tank if both pipes are opened together?"
+
+messages = [
+    {"role": "system", "content": "You are a helpful math tutor who explains each step clearly."},
+    {"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 Education Agents**: Tutors that explain problems step by step, helping users build understanding through reasoning.
+- **Bilingual Learning Platforms**: Apps that teach math in both Chinese and English.
+- **STEM-Oriented Assistants**: Supports early-stage problem solving in science and engineering contexts.
+- **Lightweight LLM Deployments**: Optimized for low-resource environments, from browsers to mobile devices.
+
+## **Limitations**
+
+1. **Domain Specificity**  
+   Primarily tuned for math reasoning — performance may degrade on unrelated tasks like creative writing or open dialogue.
+
+2. **Model Size Constraint**  
+   While efficient, 1.5B parameters may struggle with highly abstract or very long multi-domain tasks.
+
+3. **PRM Bias Generalization**  
+   PRM training can bias toward rewardable structures — results should still be reviewed for correctness and completeness.
+
+4. **Prompt Structure Sensitivity**  
    Well-structured queries yield more accurate and educationally useful outputs.