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---
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
- Reinforcement learning (RL)
- Math
- Code
---
# **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. |