stablelm-zephyr-3b
Maintainer: stabilityai
230
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| Property | Value |
|---|---|
| Run this model | Run 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
StableLM Zephyr 3B is a 3 billion parameter instruction tuned language model developed by Stability AI. It was trained on a mix of publicly available datasets and synthetic datasets using Direct Preference Optimization (DPO). The model was fine-tuned from stabilityai/stablelm-3b-4e1t and has shown strong performance on benchmarks like MT Bench and Alpaca Benchmark. It is similar in approach to the Zephyr 7B model, which was fine-tuned from mistralai/Mistral-7B-v0.1 and also used DPO.
Model inputs and outputs
StableLM Zephyr 3B is an auto-regressive language model that generates text based on provided prompts. The model uses a specific input format with user and assistant messages delimited by special tokens:
Inputs
- Text prompt following the format:
<|user|> [User prompt] <|endoftext|>
Outputs
- Completion of the user prompt, with the assistant's response delimited by special tokens:
<|assistant|> [Assistant response] <|endoftext|>
Capabilities
StableLM Zephyr 3B has been shown to perform well on a variety of natural language tasks, including answering questions, generating coherent text, and following instructions. The model can be particularly useful for building chatbots and virtual assistants that engage in helpful and natural conversations.
What can I use it for?
You can use StableLM Zephyr 3B to build a wide range of natural language processing applications, such as:
- Chatbots and virtual assistants
- Content generation (e.g. articles, stories, poetry)
- Question answering systems
- Code generation and programming assistance
To use the model commercially, please refer to the Stability AI membership options.
Things to try
One interesting aspect of StableLM Zephyr 3B is its use of Direct Preference Optimization (DPO) during training. This approach aims to align the model's outputs with human preferences, which can make the model more helpful and less likely to generate problematic content. You could experiment with prompts that test the model's alignment, such as asking it to generate text on sensitive topics or to complete tasks that require ethical reasoning.
Another unique feature of the model is its long context support, with a sequence length of up to 4096 tokens. This allows the model to maintain coherence and context over longer passages of text. You could try prompting the model with multi-paragraph inputs to see how it handles longer-form tasks.
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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