Improve language tag

README.md

@@ -1,81 +1,93 @@
----
-license: apache-2.0
-datasets:
-- openai/gsm8k
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-0.5B-Instruct
-pipeline_tag: text-generation
-library_name: transformers
-tags:
-- math
-- reasoning
-- grpo
-- trl
-- code
----
-# **Feynman-Grpo-Exp**
-
-Feynman-Grpo-Exp is based on the Qwen 0.5B modality architecture, designed to enhance the reasoning capabilities of 0.5B-parameter models. It has been fine-tuned using the GRPO trainer on the OpenAI GSM8K dataset for reinforcement learning, improving its ability to handle complex reasoning tasks, multi-step problem-solving, and mathematical challenges. This model excels in chain-of-thought (CoT) reasoning and logical problem-solving, making it suitable for a variety of advanced tasks that require precise and structured outputs.
-
-### **Key Improvements**  
-1. **Enhanced Knowledge and Expertise**: Strengthened mathematical reasoning, code generation, and problem-solving skills, particularly in scientific and technical domains.  
-2. **Fine-Tuned Instruction Following**: Optimized for generating structured outputs like JSON and handling long-form text (up to 8K+ tokens).  
-3. **Greater Adaptability**: Enhanced role-playing capabilities, allowing for better responses to diverse prompts.  
-4. **Long-Context Support**: Capable of processing up to **64K tokens** and generating up to **4K tokens** per output.  
-5. **Multilingual Proficiency**: Supports over **29 languages**, including Chinese, English, French, Spanish, Portuguese, German, and more.
-
-### **Quickstart with Transformers**  
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "prithivMLmods/Feynman-Grpo-Exp"
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto",
-    trust_remote_code=True
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-prompt = "Give me a short introduction to large language models."
-messages = [
-    {"role": "system", "content": "You are an advanced AI assistant with expert-level reasoning and knowledge."},
-    {"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]
-print(response)
-```
-
-### **Intended Use**  
-- **Advanced Reasoning & Context Understanding**: Ideal for logical deduction, multi-step problem-solving, and complex knowledge-based tasks.  
-- **Mathematical & Scientific Problem-Solving**: Optimized for handling advanced calculations, theorem proving, and scientific queries.  
-- **Code Generation & Debugging**: Capable of generating and optimizing code across multiple programming languages.  
-- **Structured Data Analysis**: Processes structured data, including tables, JSON, and other formats, making it well-suited for data-centric tasks.  
-- **Multilingual Applications**: Proficient in over 29 languages, enabling a global scale for applications.  
-- **Extended Content Generation**: Supports detailed document writing, research reports, and instructional guides.
-
-### **Limitations**  
-1. **Computational Requirements**: Despite being a **0.5B-parameter** model, it requires significant computational resources for efficient inference, especially when dealing with long-context processing.  
-2. **Language-Specific Variability**: Performance may vary across supported languages, with possible challenges for low-resource languages.  
-3. **Potential Error Accumulation**: Long-text generation can introduce inconsistencies or errors over extended outputs.  
-4. **Limited Real-World Awareness**: The model's knowledge is restricted to the training data, which may not reflect the most recent events or developments.  
+---
+license: apache-2.0
+datasets:
+- openai/gsm8k
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-0.5B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- math
+- reasoning
+- grpo
+- trl
+- code
+---
+# **Feynman-Grpo-Exp**
+
+Feynman-Grpo-Exp is based on the Qwen 0.5B modality architecture, designed to enhance the reasoning capabilities of 0.5B-parameter models. It has been fine-tuned using the GRPO trainer on the OpenAI GSM8K dataset for reinforcement learning, improving its ability to handle complex reasoning tasks, multi-step problem-solving, and mathematical challenges. This model excels in chain-of-thought (CoT) reasoning and logical problem-solving, making it suitable for a variety of advanced tasks that require precise and structured outputs.
+
+### **Key Improvements**  
+1. **Enhanced Knowledge and Expertise**: Strengthened mathematical reasoning, code generation, and problem-solving skills, particularly in scientific and technical domains.  
+2. **Fine-Tuned Instruction Following**: Optimized for generating structured outputs like JSON and handling long-form text (up to 8K+ tokens).  
+3. **Greater Adaptability**: Enhanced role-playing capabilities, allowing for better responses to diverse prompts.  
+4. **Long-Context Support**: Capable of processing up to **64K tokens** and generating up to **4K tokens** per output.  
+5. **Multilingual Proficiency**: Supports over **29 languages**, including Chinese, English, French, Spanish, Portuguese, German, and more.
+
+### **Quickstart with Transformers**  
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Feynman-Grpo-Exp"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Give me a short introduction to large language models."
+messages = [
+    {"role": "system", "content": "You are an advanced AI assistant with expert-level reasoning and knowledge."},
+    {"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]
+print(response)
+```
+
+### **Intended Use**  
+- **Advanced Reasoning & Context Understanding**: Ideal for logical deduction, multi-step problem-solving, and complex knowledge-based tasks.  
+- **Mathematical & Scientific Problem-Solving**: Optimized for handling advanced calculations, theorem proving, and scientific queries.  
+- **Code Generation & Debugging**: Capable of generating and optimizing code across multiple programming languages.  
+- **Structured Data Analysis**: Processes structured data, including tables, JSON, and other formats, making it well-suited for data-centric tasks.  
+- **Multilingual Applications**: Proficient in over 29 languages, enabling a global scale for applications.  
+- **Extended Content Generation**: Supports detailed document writing, research reports, and instructional guides.
+
+### **Limitations**  
+1. **Computational Requirements**: Despite being a **0.5B-parameter** model, it requires significant computational resources for efficient inference, especially when dealing with long-context processing.  
+2. **Language-Specific Variability**: Performance may vary across supported languages, with possible challenges for low-resource languages.  
+3. **Potential Error Accumulation**: Long-text generation can introduce inconsistencies or errors over extended outputs.  
+4. **Limited Real-World Awareness**: The model's knowledge is restricted to the training data, which may not reflect the most recent events or developments.  
 5. **Prompt Sensitivity**: Outputs depend heavily on the specificity and clarity of the input prompts.