Qwen/Qwen1.5-32B
Qwen/Qwen1.5-32B is a 32.5 billion parameter, transformer-based decoder-only language model developed by Qwen, serving as a beta version of Qwen2. It supports a stable 32K context length and offers significant performance improvements over previous Qwen models, including enhanced multilingual capabilities. This model is designed for further fine-tuning, such as SFT or RLHF, rather than direct text generation.
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Qwen1.5-32B Overview
Qwen1.5-32B is a 32.5 billion parameter model within the Qwen1.5 series, which represents the beta version of Qwen2. This transformer-based, decoder-only language model is pretrained on extensive data and offers several key advancements over its predecessors. It is part of a family of eight models ranging from 0.5B to 72B parameters, including an MoE variant.
Key Capabilities & Improvements
- Enhanced Performance: Significant improvements in chat model performance compared to previous Qwen iterations.
- Multilingual Support: Both base and chat models offer robust multilingual capabilities.
- Extended Context Length: Provides stable support for a 32K context length across all model sizes.
- Simplified Usage: Eliminates the need for
trust_remote_code, streamlining integration. - Architectural Foundation: Built on the Transformer architecture, incorporating features like SwiGLU activation, attention QKV bias, and group query attention (specifically for the 32B model).
Usage Recommendations
Qwen1.5-32B is primarily intended as a base model for further development. Users are advised against directly using the base language model for text generation. Instead, it is recommended for post-training applications such as Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), or continued pretraining to adapt it to specific use cases. For optimal performance, transformers>=4.37.0 is required.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.