Alpaca-native-4bit-ggml
Maintainer: Sosaka
201
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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
The Alpaca-native-4bit-ggml model is a version of the Alpaca model that has been converted to the GGML format and quantized to 4 bits. This allows the model to run on CPUs with as little as 5GB of RAM. The model was created by Sosaka, who maintains this and other GGML-format models.
Similar models include the alpaca-lora-65B-GGML model, which is a 65B-parameter Alpaca model also quantized to GGML format, and the gpt4-x-alpaca-native-13B-ggml model, which is a 13B-parameter GPT-4 model fine-tuned on the Alpaca dataset.
Model inputs and outputs
The Alpaca-native-4bit-ggml model is a text-to-text model, taking natural language text as input and generating natural language text as output.
Inputs
- Natural language text prompts, such as instructions or questions
Outputs
- Natural language text responses, such as answers or generated content
Capabilities
The Alpaca-native-4bit-ggml model is capable of engaging in a wide variety of natural language processing tasks, including answering questions, generating stories and summaries, and providing analysis and insights. The model demonstrates strong performance on tasks like open-ended conversation, task completion, and knowledge-based question answering.
What can I use it for?
The Alpaca-native-4bit-ggml model can be used for a variety of applications, such as building chatbots, virtual assistants, and content generation tools. Its ability to run on modest hardware makes it particularly well-suited for edge-based deployments or applications with limited computing resources.
Things to try
Some interesting things to try with the Alpaca-native-4bit-ggml model include using it to generate creative fiction, summarize long-form content, or answer open-ended questions on a wide range of topics. The model's quantization and GGML format also make it an interesting target for further research and optimization efforts.
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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