What are the key features of QwQ-32B?
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Answers ( 7 )
QwQ-32B is a causal language model with the following key features:
- **Type**: Causal Language Model
- **Training Phases**: Pre-training and post-training (including supervised fine-tuning and reinforcement learning)
- **Architecture**: Based on transformers, incorporating RoPE, SwiGLU, RMSNorm, and Attention QKV bias
- **Parameter Count**: 3.25 billion total parameters, with 3.1 billion non-embedding parameters
- **Layers**: 64 layers
- **Attention Heads (GQA)**: 40 Q heads and 8 KV heads
- **Context Length**: Supports up to 131,072 tokens, with YaRN required for prompts exceeding 8,192 tokens
- **Quantized Version**: Q4_K_M, with a file size of approximately 20GB
- **License**: Apache 2.0
The QwQ-32B model has 32 billion parameters and is based on the Transformer architecture. It includes features such as RoPE, SwiGLU, RMSNorm, and Attention QKV bias. The model supports a context length of 131,072 tokens and includes special functionalities like YaRN for handling long inputs. It is trained using supervised fine-tuning and reinforcement learning.
QwQ-32B is a causal language model with 32.5 billion parameters, including 31.0 billion non-embedding parameters. It features 64 layers and uses Grouped Query Attention (GQA) with 40 query heads and 8 key/value heads. The model supports a context length of 131,072 tokens and requires YaRN for inputs exceeding 8,192 tokens. Its architecture includes RoPE, SwiGLU, RMSNorm, and Attention QKV bias.
QwQ-32B uses a Transformer architecture with Rotary Position Embedding (RoPE), SwiGLU activation, RMSNorm normalization layer, and Attention QKV bias. It has 64 layers and employs Grouped Query Attention (GQA) with 40 query heads and 8 key/value heads.
QwQ-32B is a causal language model with 3.2 billion parameters, trained using pre-training and post-training techniques (supervised fine-tuning and reinforcement learning). It employs a Transformer architecture with RoPE, SwiGLU, RMSNorm, and Attention QKV bias. The model supports a context length of up to 131,072 tokens, with YaRN enabled for inputs exceeding 8,192 tokens. It has demonstrated strong performance in benchmarks such as AIME 24 (mathematical reasoning) and Live CodeBench (coding ability).
QwQ-32B is a causal language model with 3.2 billion parameters (3.1 billion non-embedding parameters). It employs a Transformer architecture with RoPE, SwiGLU, RMSNorm, and Attention QKV bias. The model supports a context length of up to 131,072 tokens, with YaRN enabled for inputs exceeding 8,192 tokens. It has 64 layers, 40 query attention heads, and 8 key-value attention heads. The model is trained using pre-training and post-training techniques, including supervised fine-tuning and reinforcement learning.
The key features of QwQ-32B include:
- **Model Type**: Causal Language Model.
- **Parameter Count**: 32.5 billion total parameters, with 31.0 billion non-embedding parameters.
- **Architecture**: Utilizes transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias.
- **Context Length**: Supports up to 131,072 tokens, with YaRN optimization for inputs exceeding 8,192 tokens.
- **Deployment**: Requires only 4 NVIDIA 4090 GPUs, making it accessible for low-resource environments.