TinyLlama-1.1B-Chat-v1.0
Maintainer: TinyLlama
971
🖼️
| 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
The TinyLlama-1.1B-Chat-v1.0 model is a conversational language model developed by TinyLlama. It is a 1.1 billion parameter model that was pretrained on 3 trillion tokens and then fine-tuned for chat completion using a variant of the [object Object] dataset and further alignment with the [object Object] on the openbmb/UltraFeedback dataset.
This model follows a similar architecture and tokenizer as the Llama 2 models, allowing it to be used in many Llama-based projects. The compact 1.1 billion parameter size makes it well-suited for applications with restricted compute and memory requirements.
Model inputs and outputs
Inputs
- Text: The model takes text input, which can be in the form of a single prompt or a conversation history in a chat-style format.
Outputs
- Text: The model generates text output, producing a completion or response to the provided input.
Capabilities
The TinyLlama-1.1B-Chat-v1.0 model is capable of engaging in open-ended conversations, answering questions, and generating text on a wide range of topics. Its performance is comparable to larger language models like ChatGPT and PaLM, but with a much smaller footprint.
What can I use it for?
The compact size of the TinyLlama-1.1B-Chat-v1.0 model makes it well-suited for deployment in mobile apps, edge devices, or other applications with limited computational resources. It could be used to power conversational assistants, chatbots, or other AI-powered interfaces that require natural language understanding and generation.
Things to try
One interesting way to use the TinyLlama-1.1B-Chat-v1.0 model is to fine-tune it further on domain-specific data to create a specialized assistant for your application. For example, you could fine-tune it on technical documentation to create a knowledgeable support agent, or on customer service transcripts to build a more empathetic and helpful chatbot.
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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