sesaily/Qwen2.5-Coder-7B-Frends-Instruct
The sesaily/Qwen2.5-Coder-7B-Frends-Instruct is a 7.6 billion parameter instruction-tuned causal language model developed by sesaily. This model is finetuned from unsloth/Qwen2.5-Coder-7B-Instruct and optimized for faster training using Unsloth and Huggingface's TRL library. It features a 32768 token context length, making it suitable for code-related tasks and applications requiring efficient processing.
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Model Overview
The sesaily/Qwen2.5-Coder-7B-Frends-Instruct is a 7.6 billion parameter language model, finetuned by sesaily. It is based on the Qwen2.5-Coder-7B-Instruct architecture and leverages the Unsloth library for accelerated training, achieving 2x faster finetuning speeds. This model also incorporates Huggingface's TRL library in its development process.
Key Characteristics
- Parameter Count: 7.6 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, enabling it to handle longer sequences of text or code.
- Training Optimization: Utilizes Unsloth for significantly faster finetuning, making it efficient for developers to adapt to specific tasks.
- Base Model: Finetuned from
unsloth/Qwen2.5-Coder-7B-Instruct, indicating a foundation optimized for coding tasks.
Good For
- Code-related applications: Its 'Coder' designation and base model suggest strong performance in code generation, completion, and understanding.
- Efficient development: The use of Unsloth makes it a good choice for developers looking to quickly finetune models for custom applications.
- Tasks requiring long context: The 32768 token context length is beneficial for processing extensive codebases or detailed instructions.