stablelm-2-zephyr-1_6b
Maintainer: stabilityai
170
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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 2 Zephyr 1.6B is a 1.6 billion parameter instruction-tuned language model developed by Stability AI. It is inspired by the Zephyr 7B training pipeline and utilizes Direct Preference Optimization (DPO) to train on a mix of public and synthetic datasets.
Similar models include the StableLM 2 1.6B, which is a 1.6 billion parameter decoder-only language model, and the StableLM Zephyr 3B, a 3 billion parameter instruction-tuned model.
Model Inputs and Outputs
StableLM 2 Zephyr 1.6B uses a chat-style input format with user input and assistant response delimited by special tokens:
Inputs
- User Prompt: A prompt provided by the user in natural language
Outputs
- Generated Text: The model's response to the user prompt, generated in an autoregressive manner
Capabilities
The model is capable of engaging in open-ended dialogue, answering questions, and generating text across a variety of domains. It demonstrates strong performance on benchmarks like MT-Bench and AlpacaEval, outperforming many larger models.
What Can I Use It For?
StableLM 2 Zephyr 1.6B can be used as a foundation for building chatbots, content generation tools, and other language-based applications. Due to its strong performance, it may be particularly well-suited for fine-tuning on domain-specific tasks. However, as with any large language model, users should be cautious about potential biases or safety issues, and conduct thorough testing before deploying the model in production.
Things to Try
Experiment with different prompting strategies to see how the model responds to a variety of inputs. Try combining the model with other components, such as input/output classifiers, to improve safety and reliability. Additionally, consider fine-tuning the model on your own datasets to adapt it to specific use cases.
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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