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---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-14B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- coder
- Math
- RL
---
# **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`:
```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**
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. |