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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-14B-Instruct-1M |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- Reinforcement learning (RL) |
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- Math |
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- Code |
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--- |
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# **Diophantus-14B-R1-Instruct** |
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> **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. |
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## **Key Improvements** |
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1. **Advanced Mathematical and Logical Reasoning**: |
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Enhanced capabilities for solving complex equations, optimization tasks, symbolic computation, theorem proving, and step-by-step math problem-solving. |
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2. **Robust Policy Optimization**: |
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Fine-tuned for distributed reinforcement learning (RL) tasks, improving decision-making robustness and solution generalization across complex optimization problems. |
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3. **General Knowledge and Problem Solving**: |
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Strong foundation across diverse domains, excelling in answering factual questions and executing structured multi-step reasoning processes. |
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4. **Instruction Following and Adaptability**: |
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Improved performance in understanding complex instructions and adapting to diverse prompts, maintaining coherence across extended conversations. |
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5. **Long-Context Understanding**: |
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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. |
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6. **Coding and Algorithmic Mastery**: |
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Excels in code generation, debugging, algorithm design, refactoring, and analysis across multiple programming languages, with a special focus on optimization algorithms. |
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## **Quickstart with transformers** |
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Here's how to load and use the model with the `transformers` library and `apply_chat_template`: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Diophantus-14B-R1-Instruct" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "Explain the key techniques used in robust policy optimization." |
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messages = [ |
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{"role": "system", "content": "You are an expert assistant in optimization, reinforcement learning, and general-purpose reasoning."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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``` |
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## **Intended Use** |
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1. **Optimization Problem Solving**: |
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Specialized for solving and explaining general optimization problems, including convex, non-convex, and combinatorial optimization. |
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2. **Mathematical and Logical Reasoning**: |
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Excels at solving equations, mathematical proofs, symbolic manipulations, and structured logical reasoning. |
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3. **Reinforcement Learning Applications**: |
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Useful for designing, analyzing, and explaining RL algorithms, particularly robust and distributed RL. |
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4. **Educational and Research Assistance**: |
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Suitable for providing detailed explanations, mathematical derivations, and research-oriented insights for students, educators, and researchers. |
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5. **Coding and Algorithm Development**: |
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Ideal for writing, improving, debugging, and explaining code, with a strong emphasis on optimization algorithms and computational logic. |
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6. **Conversational AI and Chatbots**: |
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Supports intelligent, context-aware dialogue generation for technical domains, education, and professional assistance. |
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7. **Long-Form Technical Content Generation**: |
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Capable of producing extensive, coherent articles, reports, and tutorials, especially for technical and mathematical content. |
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8. **Structured Data Processing**: |
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Analyzes and generates structured outputs such as JSON, tables, and formal proofs, beneficial for data science and automation. |
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## **Limitations** |
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1. **High Hardware Requirements**: |
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Requires substantial memory and high-performance GPUs or TPUs due to large parameter size and long-context processing. |
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2. **Potential Training Biases**: |
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May reflect biases present in optimization-specific datasets or mathematical corpora. |
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3. **Creative Generation Limitations**: |
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Less optimized for freeform creative writing or storytelling compared to technical reasoning. |
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4. **No Real-Time Awareness**: |
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Lacks knowledge of real-world events or developments post-training cutoff. |
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5. **Error Propagation in Long-Chain Tasks**: |
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Small early errors in long mathematical or optimization tasks may propagate in extended outputs. |
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6. **Prompt Sensitivity**: |
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The quality of outputs can be sensitive to prompt clarity and structure, especially for complex optimization or technical questions. |
