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Model: prithivMLmods/OpenRHO-2B-Thinker Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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datasets:
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- qingy2024/QwQ-Distill-Data
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- AI-MO/NuminaMath-TIR
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language:
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- en
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base_model:
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- Qwen/Qwen2-1.5B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation-inference
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- general-purpose
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- math
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- code
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---
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# **OpenRHO-2B-Thinker**
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> **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**.
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## **Key Improvements**
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1. **Advanced Reasoning via RL**:
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Built to support symbolic reasoning, logical deduction, and structured problem-solving with high efficiency — specifically optimized for real-time use on edge systems.
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2. **Compact Coding Assistant**:
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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.
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3. **Error Detection & Correction**:
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Identifies logic errors, malformed data structures (e.g., JSON, XML), and provides corrections quickly — with lightweight inference and minimal latency.
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4. **Instruction Following & Precision**:
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Tuned to follow multi-step instructions with improved contextual memory, offering consistent and precise responses across a variety of prompt types.
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5. **Extended Context Compatibility**:
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Maintains support for **128K token inputs** and **8K token outputs**, while remaining lean enough for real-time edge usage with low power consumption.
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## **Quickstart with Transformers**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/OpenRHO-2B-Thinker"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "What is a generator function in Python? Explain with an example."
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messages = [
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{"role": "system", "content": "You are a helpful and concise AI assistant skilled in programming and reasoning."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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## **Intended Use**
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1. **Edge LLM Applications**:
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Built for embedded AI agents, mobile inference, and low-latency chatbots on constrained hardware.
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2. **General-Purpose Reasoning**:
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Effective for real-time logical reasoning, structured deduction, and lightweight problem-solving tasks in everyday applications.
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3. **Educational & Programming Tools**:
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Helpful for teaching programming and debugging in interactive, constrained environments (e.g., IoT, robotics kits).
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4. **Lightweight Conversational Agents**:
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Enables responsive, intelligent interactions in edge-deployed customer service bots, support kiosks, and automation systems.
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5. **Multilingual Mini-NLP Tasks**:
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Supports basic multilingual tasks such as translation, summarization, and information retrieval across multiple languages.
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6. **Structured Format Generation**:
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Can generate JSON, Markdown, tables, or tabular outputs in lightweight settings for embedded data workflows.
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## **Limitations**
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1. **Hardware Requirements (Minimal but Non-Zero)**:
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While designed for edge use, optimal performance still benefits from mid-range NPUs, GPUs, or specialized accelerators.
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2. **Knowledge Cutoff & Real-Time Awareness**:
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No ability to fetch live data or respond to real-time information beyond its training snapshot.
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3. **Limited Creative Output**:
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Less effective for creative writing, abstract thinking, or tasks requiring deep imagination.
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4. **Prompt Sensitivity**:
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Outputs can vary based on prompt clarity; structured prompts yield better, more predictable results.
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5. **Inherited Biases**:
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May reflect biases from pretraining data. Use caution in sensitive or high-stakes domains.
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