Pythia-Chat-Base-7B
Maintainer: togethercomputer
66
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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
Pythia-Chat-Base-7B-v0.16 is a 7B parameter language model developed by Together Computer. It is based on EleutherAI's Pythia-7B model and has been fine-tuned with over 40 million instructions on 100% carbon negative compute. The model focuses on dialog-style interactions, with fine-tuning on tasks like question answering, classification, extraction, and summarization.
Similar models include GPT-NeoXT-Chat-Base-20B-v0.16, which is a 20B parameter model also developed by Together Computer with a similar fine-tuning process.
Model inputs and outputs
Inputs
- Text prompt: The model accepts text prompts as input, which can include dialogue, questions, instructions, or other types of language tasks.
Outputs
- Generated text: The model outputs generated text continuations or responses based on the input prompt. This can include answers, summaries, classifications, and other relevant text outputs.
Capabilities
Pythia-Chat-Base-7B-v0.16 excels at a variety of language tasks out of the box, including summarization, question answering, classification, and extraction. The model can provide detailed and relevant responses within conversational contexts, drawing upon its broad knowledge base.
For example, the model can summarize long documents into concise sentences, answer follow-up questions about the content, and classify the sentiment of input text. It also performs well on few-shot prompts, adapting quickly to new tasks with limited training data.
What can I use it for?
Pythia-Chat-Base-7B-v0.16 is intended for research purposes, with potential applications in areas like:
- Developing safe and responsible chatbots and dialogue systems
- Probing the limitations and biases of language models
- Generating creative content like art and design
- Building educational or productivity tools
- Advancing research on language models and AI systems
While the model has strong capabilities, it should not be used for high-stakes or safety-critical applications, as it may produce inaccurate or harmful outputs at times.
Things to try
One interesting aspect of Pythia-Chat-Base-7B-v0.16 is its ability to run inference on a 12GB GPU, thanks to quantization techniques. This makes the model more accessible to a wider range of users and hardware configurations, allowing for more experimentation and exploration of its capabilities.
Developers could try fine-tuning the model on domain-specific datasets or integrating it into chatbot or language generation applications. Researchers may be interested in evaluating the model's performance on various benchmarks or probing its limitations and biases.
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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