Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-14B-Instruct-1M
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- R1
+- math
+- RL
+---
+
+![10.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Rsfw7sqsqzZUvIIX_h7YN.png)
+
+# **Diophantus-14B-R1-Instruct**  
+
+> **Diophantus-14B-R1-Instruct** is based on the Qwen 2.5 14B modality architecture, designed to optimize performance for mathematical reasoning, general-purpose problem solving, and robust policy optimization using distributed reinforcement learning (RL). This model excels in contextual understanding, logical deduction, multi-step reasoning, and optimization-based tasks. It has been fine-tuned using long chain-of-thought datasets, optimization problem-solving corpora, and structured reasoning datasets to improve comprehension, structured responses, and intelligent decision-making.
+
+## **Key Improvements**  
+1. **Advanced Mathematical and Logical Reasoning**:  
+   Enhanced capabilities for solving complex equations, optimization tasks, symbolic computation, theorem proving, and step-by-step math problem-solving.
+
+2. **Robust Policy Optimization**:  
+   Fine-tuned for distributed reinforcement learning (RL) tasks, improving decision-making robustness and solution generalization across complex optimization problems.
+
+3. **General Knowledge and Problem Solving**:  
+   Strong foundation across diverse domains, excelling in answering factual questions and executing structured multi-step reasoning processes.
+
+4. **Instruction Following and Adaptability**:  
+   Improved performance in understanding complex instructions and adapting to diverse prompts, maintaining coherence across extended conversations.
+
+5. **Long-Context Understanding**:  
+   Supports up to 128K tokens for input, and can generate up to 8K tokens, ideal for deep, multi-turn dialogues, mathematical derivations, and long-chain logical reasoning.
+
+6. **Coding and Algorithmic Mastery**:  
+   Excels in code generation, debugging, algorithm design, refactoring, and analysis across multiple programming languages, with a special focus on optimization algorithms.
+
+## **Quickstart with transformers**  
+
+Here's how to load and use the model with the `transformers` library and `apply_chat_template`:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Diophantus-14B-R1-Instruct"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Explain the key techniques used in robust policy optimization."
+messages = [
+    {"role": "system", "content": "You are an expert assistant in optimization, reinforcement learning, and general-purpose reasoning."},
+    {"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**  
+1. **Optimization Problem Solving**:  
+   Specialized for solving and explaining general optimization problems, including convex, non-convex, and combinatorial optimization.
+
+2. **Mathematical and Logical Reasoning**:  
+   Excels at solving equations, mathematical proofs, symbolic manipulations, and structured logical reasoning.
+
+3. **Reinforcement Learning Applications**:  
+   Useful for designing, analyzing, and explaining RL algorithms, particularly robust and distributed RL.
+
+4. **Educational and Research Assistance**:  
+   Suitable for providing detailed explanations, mathematical derivations, and research-oriented insights for students, educators, and researchers.
+
+5. **Coding and Algorithm Development**:  
+   Ideal for writing, improving, debugging, and explaining code, with a strong emphasis on optimization algorithms and computational logic.
+
+6. **Conversational AI and Chatbots**:  
+   Supports intelligent, context-aware dialogue generation for technical domains, education, and professional assistance.
+
+7. **Long-Form Technical Content Generation**:  
+   Capable of producing extensive, coherent articles, reports, and tutorials, especially for technical and mathematical content.
+
+8. **Structured Data Processing**:  
+   Analyzes and generates structured outputs such as JSON, tables, and formal proofs, beneficial for data science and automation.
+
+## **Limitations**  
+1. **High Hardware Requirements**:  
+   Requires substantial memory and high-performance GPUs or TPUs due to large parameter size and long-context processing.
+
+2. **Potential Training Biases**:  
+   May reflect biases present in optimization-specific datasets or mathematical corpora.
+
+3. **Creative Generation Limitations**:  
+   Less optimized for freeform creative writing or storytelling compared to technical reasoning.
+
+4. **No Real-Time Awareness**:  
+   Lacks knowledge of real-world events or developments post-training cutoff.
+
+5. **Error Propagation in Long-Chain Tasks**:  
+   Small early errors in long mathematical or optimization tasks may propagate in extended outputs.
+
+6. **Prompt Sensitivity**:  
+   The quality of outputs can be sensitive to prompt clarity and structure, especially for complex optimization or technical questions.