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Qwenlong L1 32B GGUF

Developed by Mungert
QwenLong-L1-32B is a large language model designed for long context reasoning. It is trained through reinforcement learning and performs excellently in multiple long context question answering benchmark tests, capable of effectively handling complex reasoning tasks.
Downloads 927
Release Time : 5/28/2025

Model Overview

QwenLong-L1-32B is a long context large reasoning model (LRM). Trained through a reinforcement learning framework, it can transition from short context reasoning to long context generalization ability and is suitable for processing long documents and complex reasoning tasks.

Model Features

Long context processing
Through the reinforcement learning framework, it can transition from short context reasoning to long context generalization and effectively process long documents.
Ultra-low bit quantization
An accuracy adaptive quantization method is introduced, which has significant improvements on ultra-low bit models (1 - 2 bits) and is suitable for memory - constrained deployments.
Multiple quantization formats
It provides multiple quantization formats such as BF16, F16, Q4_K, which can be selected according to hardware capabilities and memory limitations.
Reinforcement learning training
Through the curriculum - guided RL stage and the difficulty - aware retrospective sampling mechanism, the long context reasoning ability of the model is optimized.

Model Capabilities

Long text understanding
Complex reasoning
Document question answering
Multilingual support
Low memory optimized reasoning

Use Cases

Document processing
Long document question answering
Extract information from long documents and answer complex questions.
It performs excellently in multiple long context question answering benchmark tests.
Network monitoring
Security audit
Analyze network logs and security reports to identify potential threats.
It can be used for real - time network diagnosis and monitoring.
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