Update README.md

README.md

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----
-license: apache-2.0
----
+---
+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
+---
+
+![8.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/HXno2Of6OWRexX88f2wtL.png)
+
+# **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.