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

Advanced Mathematical and Logical Reasoning:
Enhanced capabilities for solving complex equations, optimization tasks, symbolic computation, theorem proving, and step-by-step math problem-solving.
Robust Policy Optimization:
Fine-tuned for distributed reinforcement learning (RL) tasks, improving decision-making robustness and solution generalization across complex optimization problems.
General Knowledge and Problem Solving:
Strong foundation across diverse domains, excelling in answering factual questions and executing structured multi-step reasoning processes.
Instruction Following and Adaptability:
Improved performance in understanding complex instructions and adapting to diverse prompts, maintaining coherence across extended conversations.
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.
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

Optimization Problem Solving:
Specialized for solving and explaining general optimization problems, including convex, non-convex, and combinatorial optimization.
Mathematical and Logical Reasoning:
Excels at solving equations, mathematical proofs, symbolic manipulations, and structured logical reasoning.
Reinforcement Learning Applications:
Useful for designing, analyzing, and explaining RL algorithms, particularly robust and distributed RL.
Educational and Research Assistance:
Suitable for providing detailed explanations, mathematical derivations, and research-oriented insights for students, educators, and researchers.
Coding and Algorithm Development:
Ideal for writing, improving, debugging, and explaining code, with a strong emphasis on optimization algorithms and computational logic.
Conversational AI and Chatbots:
Supports intelligent, context-aware dialogue generation for technical domains, education, and professional assistance.
Long-Form Technical Content Generation:
Capable of producing extensive, coherent articles, reports, and tutorials, especially for technical and mathematical content.
Structured Data Processing:
Analyzes and generates structured outputs such as JSON, tables, and formal proofs, beneficial for data science and automation.

Limitations

High Hardware Requirements:
Requires substantial memory and high-performance GPUs or TPUs due to large parameter size and long-context processing.
Potential Training Biases:
May reflect biases present in optimization-specific datasets or mathematical corpora.
Creative Generation Limitations:
Less optimized for freeform creative writing or storytelling compared to technical reasoning.
No Real-Time Awareness:
Lacks knowledge of real-world events or developments post-training cutoff.
Error Propagation in Long-Chain Tasks:
Small early errors in long mathematical or optimization tasks may propagate in extended outputs.
Prompt Sensitivity:
The quality of outputs can be sensitive to prompt clarity and structure, especially for complex optimization or technical questions.