Update README.md

README.md

@@ -14,4 +14,101 @@ tags:
 - math
 - code
 - general-purpose
----
+---
+
+![ToI.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/SFtFiJzRxVEMY7nCqquQM.png)
+
+# **OpenRHO-2B-Thinker**
+
+> **OpenRHO-2B-Thinker** is a **general-purpose reasoning model** designed to enhance the cognitive abilities of **edge-deployed large language models (LLMs)** through **reinforcement learning (RL)**. Fine-tuned from **Qwen2-1.5B-Instruct** using the **QwQ distill dataset**, it delivers refined improvements in logical reasoning, structured problem-solving, and lightweight coding — making it highly efficient for **resource-constrained environments**.
+
+## **Key Improvements**
+
+1. **Advanced Reasoning via RL**:
+   Built to support symbolic reasoning, logical deduction, and structured problem-solving with high efficiency — specifically optimized for real-time use on edge systems.
+
+2. **Compact Coding Assistant**:
+   Enhanced understanding of multiple programming paradigms and syntax across Python, JavaScript, C++, and more. Supports in-situ code generation and debugging for embedded coding scenarios.
+
+3. **Error Detection & Correction**:
+   Identifies logic errors, malformed data structures (e.g., JSON, XML), and provides corrections quickly — with lightweight inference and minimal latency.
+
+4. **Instruction Following & Precision**:
+   Tuned to follow multi-step instructions with improved contextual memory, offering consistent and precise responses across a variety of prompt types.
+
+5. **Extended Context Compatibility**:
+   Maintains support for **128K token inputs** and **8K token outputs**, while remaining lean enough for real-time edge usage with low power consumption.
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/OpenRHO-2B-Thinker"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "What is a generator function in Python? Explain with an example."
+messages = [
+    {"role": "system", "content": "You are a helpful and concise AI assistant skilled in programming and 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. **Edge LLM Applications**:
+   Built for embedded AI agents, mobile inference, and low-latency chatbots on constrained hardware.
+
+2. **General-Purpose Reasoning**:
+   Effective for real-time logical reasoning, structured deduction, and lightweight problem-solving tasks in everyday applications.
+
+3. **Educational & Programming Tools**:
+   Helpful for teaching programming and debugging in interactive, constrained environments (e.g., IoT, robotics kits).
+
+4. **Lightweight Conversational Agents**:
+   Enables responsive, intelligent interactions in edge-deployed customer service bots, support kiosks, and automation systems.
+
+5. **Multilingual Mini-NLP Tasks**:
+   Supports basic multilingual tasks such as translation, summarization, and information retrieval across multiple languages.
+
+6. **Structured Format Generation**:
+   Can generate JSON, Markdown, tables, or tabular outputs in lightweight settings for embedded data workflows.
+
+## **Limitations**
+
+1. **Hardware Requirements (Minimal but Non-Zero)**:
+   While designed for edge use, optimal performance still benefits from mid-range NPUs, GPUs, or specialized accelerators.
+
+2. **Knowledge Cutoff & Real-Time Awareness**:
+   No ability to fetch live data or respond to real-time information beyond its training snapshot.
+
+3. **Limited Creative Output**:
+   Less effective for creative writing, abstract thinking, or tasks requiring deep imagination.
+
+4. **Prompt Sensitivity**:
+   Outputs can vary based on prompt clarity; structured prompts yield better, more predictable results.
+
+5. **Inherited Biases**:
+   May reflect biases from pretraining data. Use caution in sensitive or high-stakes domains.