123 lines
5.5 KiB
Markdown
123 lines
5.5 KiB
Markdown
---
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license: apache-2.0
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library_name: transformers
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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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tags:
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- text-generation-inference
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- GOP
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- Code
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- RL
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- Math
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---
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# **Rapeto-ReDistill-14B-GOP**
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> **Rapeto-ReDistill-14B-GOP** 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/Rapeto-ReDistill-14B-GOP"
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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. |