izaanz/Thesis_RTX5090_SFT_Merged
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kTool Calling:SupportedLicense:apache-2.0Architecture:Transformer Open Weights Warm
The izaanz/Thesis_RTX5090_SFT_Merged is a 7.6 billion parameter Qwen2.5-Coder-7B-Instruct model, fine-tuned by izaanz. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. With a 131072 token context length, it is optimized for tasks requiring extensive context and efficient processing.
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Model Overview
The izaanz/Thesis_RTX5090_SFT_Merged is a 7.6 billion parameter language model, fine-tuned by izaanz from the unsloth/qwen2.5-coder-7b-instruct base model. It leverages the Qwen2.5 architecture and is designed for instruction-following tasks.
Key Capabilities
- Efficient Training: This model was fine-tuned using Unsloth and Huggingface's TRL library, resulting in a 2x faster training process compared to standard methods.
- Large Context Window: It supports a substantial context length of 131072 tokens, enabling it to process and generate responses based on extensive input.
- Instruction Following: As an instruction-tuned model, it is capable of understanding and executing a wide range of user instructions.
Good For
- Applications requiring a model with a large context window.
- Tasks benefiting from an instruction-tuned Qwen2.5-based model.
- Scenarios where efficient training methodologies are a key consideration.