HWERI/llama2-exams-orca-sharegpt
HWERI/llama2-exams-orca-sharegpt is a 7 billion parameter Llama2-based causal language model, fine-tuned on a combination of ShareGPT, the exams dataset, and a subset of the Orca dataset. This model, with a 4096-token context length, is optimized for conversational AI and instruction-following tasks, leveraging diverse high-quality instruction data. Its training methodology focuses on improving performance across general conversational abilities and specific knowledge-based queries.
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Model Overview
HWERI/llama2-exams-orca-sharegpt is a 7 billion parameter language model built upon the Llama2 architecture. It has been specifically fine-tuned using a curated dataset that combines ShareGPT, the exams dataset, and a subset of the Orca dataset. This strategic blend of training data aims to enhance the model's capabilities in both general conversational understanding and performance on knowledge-intensive tasks.
Key Capabilities
- Instruction Following: Fine-tuned on diverse instruction datasets, making it proficient at understanding and executing user commands.
- Conversational AI: Leverages ShareGPT data to improve natural dialogue and response generation.
- Knowledge-Based Reasoning: Incorporates the exams dataset to bolster its ability to answer factual and reasoning-based questions.
- Orca Dataset Integration: Utilizes a subset of the Orca dataset, known for its high-quality, complex instruction-following examples, to further refine its reasoning and problem-solving skills.
Training Details
The model underwent three epochs of fine-tuning using the DeepSpeed Chat toolkit (specifically, the SFT step). A cosine learning rate scheduler was employed, starting with an initial learning rate of 2e-5, until performance plateaued on the validation dataset.
Good For
- Applications requiring robust instruction-following.
- Building conversational agents and chatbots.
- Tasks that benefit from improved reasoning and factual recall, particularly those similar to exam-style questions.
- General-purpose language generation where diverse conversational and instructional capabilities are beneficial.