QwQ-32B
Matching R1 reasoning yet 20x smaller
Listed in categories:
Artificial IntelligenceOpen Source
Description
QwQ is the reasoning model of the Qwen series, designed to outperform conventional instruction-tuned models in thinking and reasoning tasks. The QwQ32B model, a medium-sized reasoning model, achieves competitive performance against state-of-the-art models, making it suitable for complex problem-solving.
How to use QwQ-32B?
To use the QwQ32B model, load the tokenizer and model using the provided code snippet, input your prompt, and generate responses while following the usage guidelines for optimal performance.
Core features of QwQ-32B:
1️⃣
Causal Language Model
2️⃣
Pretraining and Posttraining Supervised Finetuning
3️⃣
Reinforcement Learning
4️⃣
Transformers Architecture with RoPE and SwiGLU
5️⃣
High Context Length of 131072 tokens
Why could be used QwQ-32B?
| # | Use case | Status | |
|---|---|---|---|
| # 1 | Text generation for conversational AI | ✅ | |
| # 2 | Solving complex reasoning tasks | ✅ | |
| # 3 | Generating structured outputs for multiple-choice questions | ✅ | |
Who developed QwQ-32B?
The Qwen team is dedicated to advancing AI through innovative models like QwQ, focusing on enhancing reasoning capabilities and providing robust solutions for complex tasks.