stefra/qwen_fm_2k
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:May 6, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
The stefra/qwen_fm_2k is a 7.6 billion parameter Qwen2-based causal language model developed by stefra. This model was finetuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is optimized for efficient deployment and performance, leveraging the Qwen2.5-7B-Instruct architecture.
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Model Overview
The stefra/qwen_fm_2k is a 7.6 billion parameter language model developed by stefra. It is based on the Qwen2.5-7B-Instruct architecture and has been finetuned for specific applications. A key characteristic of this model is its efficient training process, which was achieved using the Unsloth library in conjunction with Huggingface's TRL library, resulting in a 2x speedup during finetuning.
Key Characteristics
- Base Model: Finetuned from
unsloth/Qwen2.5-7B-Instruct-unsloth-bnb-4bit. - Parameter Count: 7.6 billion parameters.
- Efficient Training: Utilizes Unsloth for significantly faster finetuning.
- Context Length: Supports a context length of 32768 tokens.
Good For
- Developers looking for a Qwen2-based model that has undergone efficient finetuning.
- Applications where the base Qwen2.5-7B-Instruct capabilities are desired, with potential optimizations from the finetuning process.
- Use cases benefiting from a model trained with Unsloth's speed enhancements.