Qwen2 7B Instruct is a 7-billion-parameter instruction-tuned language model developed by the Qwen team. Optimized for following instructions, it excels at tasks like question answering, dialogue generation, and summarization. The model is designed to provide accurate and contextually appropriate responses, making it suitable for a wide range of natural language processing applications.
Fine-tuningDocs | Qwen2 7B Instruct can be customized with your data to improve responses. Fireworks uses LoRA to efficiently train and deploy your personalized model |
On-demand DeploymentDocs | On-demand deployments allow you to use Qwen2 7B Instruct on dedicated GPUs with Fireworks' high-performance serving stack with high reliability and no rate limits. |
Qwen2 7B Instruct is an instruction-tuned language model developed by Qwen, a team at Alibaba Cloud. It is based on the Qwen2 architecture and optimized for general-purpose tasks such as question answering, summarization, dialogue generation, and reasoning.
Qwen2 7B Instruct is tuned for:
131,072 tokens when using YaRN extrapolation techniques. Default context length is 32,768 tokens.
The supported context length for this model is 32.8K tokens on Fireworks. For longer sequences, YaRN must be explicitly configured using rope_scaling in the model config.
The model may underperform on long context processing without proper configuration of YaRN. On short inputs, performance can degrade if YaRN is enabled unnecessarily. Some benchmarks (e.g., GPQA) show slightly weaker performance compared to larger models.
Streaming responses and function calling are not supported for this model.
Yes. Fireworks supports LoRA-based fine-tuning for this model.
Qwen2 7B Instruct is released under the Apache 2.0 license, which permits commercial use and modifications.
