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---
license: apache-2.0
datasets:
- qingy2024/QwQ-Distill-Data
- AI-MO/NuminaMath-TIR
language:
- en
base_model:
- Qwen/Qwen2-1.5B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- general-purpose
- math
- code
---
# **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. |