README.md

---
license: apache-2.0
library_name: transformers
language:
- en
base_model:
- Qwen/Qwen2.5-14B-Instruct-1M
pipeline_tag: text-generation
tags:
- text-generation-inference
- GOP
- Code
- RL
- Math
---

![7.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/YkdzjiIwFhhju5WVNL2vP.png)

# **Rapeto-ReDistill-14B-GOP**  

> **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.

## **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/Rapeto-ReDistill-14B-GOP"

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.