longtermrisk/Qwen2.5-32B-Instruct-ftjob-445d16c937c7
TEXT GENERATIONConcurrency Cost:2Model Size:32.8BQuant:FP8Ctx Length:32kPublished:Mar 10, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The longtermrisk/Qwen2.5-32B-Instruct-ftjob-445d16c937c7 is a 32.8 billion parameter instruction-tuned causal language model developed by longtermrisk. This model is a finetuned version of unsloth/Qwen2.5-32B-Instruct, optimized for faster training using Unsloth and Huggingface's TRL library. It is designed for general instruction-following tasks, leveraging the Qwen2.5 architecture for robust performance.
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Model Overview
The longtermrisk/Qwen2.5-32B-Instruct-ftjob-445d16c937c7 is a 32.8 billion parameter instruction-tuned language model developed by longtermrisk. It is a finetuned variant of the unsloth/Qwen2.5-32B-Instruct base model.
Key Characteristics
- Architecture: Based on the Qwen2.5 model family.
- Parameter Count: 32.8 billion parameters, suitable for complex language understanding and generation tasks.
- Training Optimization: This specific model was finetuned with Unsloth and Huggingface's TRL library, enabling a 2x faster training process compared to standard methods.
- Context Length: Supports a context length of 32768 tokens, allowing for processing and generating extensive text sequences.
Intended Use Cases
This model is well-suited for a variety of instruction-following applications, including:
- General-purpose text generation.
- Question answering.
- Summarization.
- Dialogue systems.
- Any task requiring a large, instruction-tuned language model with efficient training origins.