abhinavakarsh0033/model_sft_lora
abhinavakarsh0033/model_sft_lora is a 1.5 billion parameter instruction-tuned language model, fine-tuned from Qwen/Qwen2.5-1.5B-Instruct. This model was trained using Supervised Fine-Tuning (SFT) with the TRL library, offering a context length of 32768 tokens. It is designed for general text generation tasks, leveraging the capabilities of its Qwen2.5 base.
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Model Overview
abhinavakarsh0033/model_sft_lora is a 1.5 billion parameter language model, fine-tuned from the robust Qwen/Qwen2.5-1.5B-Instruct base model. This model leverages Supervised Fine-Tuning (SFT) techniques, implemented using the TRL library (Transformers Reinforcement Learning), to enhance its instruction-following capabilities.
Key Characteristics
- Base Model: Qwen/Qwen2.5-1.5B-Instruct
- Parameter Count: 1.5 billion
- Context Length: 32768 tokens
- Training Method: Supervised Fine-Tuning (SFT)
- Frameworks Used: TRL (0.29.0), Transformers (5.2.0), Pytorch (2.9.0+cu126), Datasets (4.0.0), Tokenizers (0.22.2)
Use Cases
This model is suitable for various text generation tasks where a compact yet capable instruction-tuned model is required. Its fine-tuning process aims to improve its ability to follow instructions and generate coherent, relevant text based on user prompts. Developers can integrate it using the transformers pipeline for quick deployment in applications requiring text generation.