Eratosthenes-Polymath-14B-Instruct

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

1. Advanced Mathematical Reasoning:
   Excels in solving complex equations, performing symbolic computation, theorem proving, and step-by-step mathematical problem-solving.

2. Distributed Reinforcement Learning Expertise:
   Specially fine-tuned for robust policy optimization using distributed RL techniques, providing resilience and optimality across dynamic problem spaces.

3. General-Purpose Reasoning and Problem Solving:
   Strong across a broad range of domains, handling factual questions, logical analysis, and multi-step cognitive tasks.

4. 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.

5. Superior Instruction Following:
   Capable of following complex and structured prompts precisely, maintaining focus and clarity over extended dialogues.

6. 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:

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

1. Mathematical and Optimization Problem Solving:
   Designed for solving complex mathematical problems, optimization modeling, symbolic logic, and structured derivations.

2. Distributed Reinforcement Learning Research:
   Supports designing, analyzing, and explaining distributed RL systems, robust policy optimization, and autonomous decision systems.

3. General Knowledge and Reasoning:
   Effective in answering a wide range of questions and performing structured reasoning across scientific, technical, and educational domains.

4. Educational and Research Support:
   Ideal for students, researchers, and professionals seeking detailed explanations, derivations, and robust scientific insights.

5. Code Writing and Algorithm Design:
   Excels at creating, optimizing, and explaining algorithms, particularly those relevant to mathematical computation and optimization.

6. Intelligent Conversational Systems:
   Perfect for technical conversational agents and educational bots requiring deep understanding and detailed reasoning capabilities.

7. Long-Form Technical Content Generation:
   Capable of producing structured, coherent articles, tutorials, and research papers, especially in technical and mathematical fields.

8. Structured Data Generation:
   Supports outputting structured formats such as proofs, equations, tables, and JSON useful for scientific and technical workflows.

Limitations

1. Heavy Hardware Requirements:
   Due to its large parameter count and long-context handling, it requires powerful GPUs or TPUs with significant memory.

2. Potential for Training Biases:
   Outputs may still reflect biases from the mathematical, technical, or optimization-specific datasets used during training.

3. Less Effective in Creative Tasks:
   Focused more on technical and logical reasoning than on freeform creative writing or storytelling.

4. No Real-Time Event Awareness:
   Limited to knowledge prior to its training cutoff, without access to live or real-world updates.

5. Prompt Sensitivity:
   Performance may vary based on the clarity, structure, and specificity of the prompt, particularly for complex multi-step tasks.

6. Error Propagation Risk:
   Small inaccuracies in early stages of long-form outputs could propagate, affecting the overall answer coherence.