longtermrisk/Qwen2.5-7B-Instruct-ftjob-1c832510b5e4
The longtermrisk/Qwen2.5-7B-Instruct-ftjob-1c832510b5e4 is a 7.6 billion parameter instruction-tuned causal language model, fine-tuned by longtermrisk from the unsloth/Qwen2.5-7B-Instruct base model. This model was trained using Unsloth and Huggingface's TRL library, achieving a 2x speed improvement during the fine-tuning process. It is designed for general instruction-following tasks, leveraging its 32768 token context length for comprehensive understanding and generation.
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Overview
This model, longtermrisk/Qwen2.5-7B-Instruct-ftjob-1c832510b5e4, is a 7.6 billion parameter instruction-tuned language model developed by longtermrisk. It is fine-tuned from the unsloth/Qwen2.5-7B-Instruct base model, leveraging the Qwen2.5 architecture. The fine-tuning process utilized Unsloth and Huggingface's TRL library, which enabled a 2x faster training speed compared to standard methods.
Key Characteristics
- Architecture: Qwen2.5-7B-Instruct base model.
- Parameter Count: 7.6 billion parameters.
- Context Length: Supports a substantial context window of 32768 tokens.
- Training Efficiency: Fine-tuned with Unsloth for significantly accelerated training.
Good For
- Instruction Following: Designed to excel in tasks requiring adherence to specific instructions.
- General Language Generation: Suitable for a wide range of text generation applications due to its instruction-tuned nature and large context window.
- Efficient Deployment: Models fine-tuned with Unsloth often benefit from optimized performance, making them potentially efficient for various applications.