phi-1_5
Maintainer: microsoft
1.3K
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| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
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Model overview
phi-1.5 is a 1.3 billion parameter Transformer language model developed by Microsoft. It was trained on the same data sources as the phi-1 model, with an additional synthetic NLP data source. The model demonstrates state-of-the-art performance on benchmarks testing common sense, language understanding, and logical reasoning, compared to other models under 10 billion parameters.
Unlike phi-1, phi-1.5 was not fine-tuned for instruction following or through reinforcement learning from human feedback. Instead, the intention was to provide the research community with an open-source small model to explore vital safety challenges, such as reducing toxicity, understanding societal biases, and enhancing controllability.
The model's training data was carefully curated to exclude generic web-crawl sources, which helps prevent direct exposure to potentially harmful online content. However, the model is still vulnerable to generating harmful content, and the researchers hope the model can help further study the safety of language models.
Model inputs and outputs
Inputs
- Text prompts in a variety of formats, including QA, chat, and code
Outputs
- Generative text responses, such as poems, emails, stories, summaries, and Python code
Capabilities
phi-1.5 can perform a wide range of natural language generation tasks, including writing poems, drafting emails, creating stories, summarizing texts, and generating Python code. The model is particularly well-suited for prompts in the QA, chat, and code formats.
What can I use it for?
The phi-1.5 model can be useful for researchers and developers exploring language model safety challenges, such as reducing toxicity, understanding biases, and enhancing controllability. The model's open-source nature and relatively small size make it an accessible option for these types of investigations.
Things to try
One interesting aspect of phi-1.5 is its exclusion of generic web-crawl data sources during training, which aims to prevent direct exposure to potentially harmful online content. Researchers could explore how this design choice affects the model's behavior and safety compared to models trained on broader web data.
Another area to investigate is the model's performance on prompts that require logical reasoning or common sense understanding, given its strong results on related benchmarks. Developers could experiment with using phi-1.5 for applications that rely on these cognitive capabilities.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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