Eratosthenes-Polymath-14B-Instruct is built on the Qwen 2.5 14B modality architecture, engineered to excel in mathematical reasoning, distributed reinforcement learning (RL), and general-purpose problem solving. This model is fine-tuned with chain-of-thought reasoning datasets, optimization-focused corpora, and advanced structured reasoning datasets to maximize its capabilities in logical deduction, multi-step reasoning, and intelligent decision-making.

Key Improvements

Advanced Mathematical Reasoning:
Excels in solving complex equations, performing symbolic computation, theorem proving, and step-by-step mathematical problem-solving.
Distributed Reinforcement Learning Expertise:
Specially fine-tuned for robust policy optimization using distributed RL techniques, providing resilience and optimality across dynamic problem spaces.
General-Purpose Reasoning and Problem Solving:
Strong across a broad range of domains, handling factual questions, logical analysis, and multi-step cognitive tasks.
Long-Context Mastery:
Supports up to 128K tokens for context and can generate up to 8K tokens, enabling detailed, coherent long-form outputs and complex derivations.
Superior Instruction Following:
Capable of following complex and structured prompts precisely, maintaining focus and clarity over extended dialogues.
Coding and Algorithmic Fluency:
Highly effective in code generation, debugging, algorithm design, and optimization problem modeling across various programming languages.

Quickstart with transformers

Use the model easily with the transformers library and apply_chat_template:

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Eratosthenes-Polymath-14B-Instruct"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Explain the connection between distributed reinforcement learning and robust policy optimization."
messages = [
    {"role": "system", "content": "You are an expert assistant specializing in mathematics, optimization, and reinforcement learning."},
    {"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

Mathematical and Optimization Problem Solving:
Designed for solving complex mathematical problems, optimization modeling, symbolic logic, and structured derivations.
Distributed Reinforcement Learning Research:
Supports designing, analyzing, and explaining distributed RL systems, robust policy optimization, and autonomous decision systems.
General Knowledge and Reasoning:
Effective in answering a wide range of questions and performing structured reasoning across scientific, technical, and educational domains.
Educational and Research Support:
Ideal for students, researchers, and professionals seeking detailed explanations, derivations, and robust scientific insights.
Code Writing and Algorithm Design:
Excels at creating, optimizing, and explaining algorithms, particularly those relevant to mathematical computation and optimization.
Intelligent Conversational Systems:
Perfect for technical conversational agents and educational bots requiring deep understanding and detailed reasoning capabilities.
Long-Form Technical Content Generation:
Capable of producing structured, coherent articles, tutorials, and research papers, especially in technical and mathematical fields.
Structured Data Generation:
Supports outputting structured formats such as proofs, equations, tables, and JSON useful for scientific and technical workflows.

Limitations

Heavy Hardware Requirements:
Due to its large parameter count and long-context handling, it requires powerful GPUs or TPUs with significant memory.
Potential for Training Biases:
Outputs may still reflect biases from the mathematical, technical, or optimization-specific datasets used during training.
Less Effective in Creative Tasks:
Focused more on technical and logical reasoning than on freeform creative writing or storytelling.
No Real-Time Event Awareness:
Limited to knowledge prior to its training cutoff, without access to live or real-world updates.
Prompt Sensitivity:
Performance may vary based on the clarity, structure, and specificity of the prompt, particularly for complex multi-step tasks.
Error Propagation Risk:
Small inaccuracies in early stages of long-form outputs could propagate, affecting the overall answer coherence.