gorilla-7B-GGML
Maintainer: TheBloke
45
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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 gorilla-7B-GGML model is a large language model created by the AI researcher TheBloke. It is a version of the Gorilla LLM optimized for GGML format, which allows it to be used with CPU and GPU acceleration via tools like llama.cpp, text-generation-webui, and KoboldCpp.
The gorilla-7B-GGML model is designed to enable language models to use tools by invoking APIs. This is a unique capability compared to other large language models like GPT-3 or GPT-4, which are mainly focused on natural language generation and understanding. The model was trained on a large corpus of online data and fine-tuned to be able to accurately generate API calls.
Model inputs and outputs
Inputs
- Natural language prompts: The model accepts natural language text as input, which it then uses to generate API calls.
Outputs
- API calls: The primary output of the
gorilla-7B-GGMLmodel is a sequence of API calls that are semantically and syntactically correct, allowing the language model to interact with external tools and services.
Capabilities
The gorilla-7B-GGML model is unique in its ability to generate accurate API calls based on natural language prompts. This allows language models to move beyond pure text generation and interact with the world in a more tangible way. For example, the model could be used to generate API calls to fetch data, perform calculations, or control IoT devices - all based on high-level natural language instructions.
What can I use it for?
The gorilla-7B-GGML model could be used in a variety of applications that require language models to interact with APIs and external systems. Some potential use cases include:
- Intelligent assistants: The model could be used to build AI assistants that can understand natural language commands and translate them into the necessary API calls to perform tasks.
- Process automation: The model could be used to automate business processes by generating API calls to access data, trigger workflows, and integrate systems.
- IoT control: The model could be used to control smart home or industrial IoT devices by generating API calls to adjust settings, monitor status, and execute commands.
Things to try
One interesting aspect of the gorilla-7B-GGML model is its ability to handle complex, multi-step API interactions. Rather than just generating a single API call, the model can understand the broader context and generate a sequence of API calls to achieve a desired outcome. This could be useful for building more sophisticated applications that require chaining together multiple services or APIs.
Another interesting thing to try would be fine-tuning the gorilla-7B-GGML model on a specific domain or set of APIs. This could potentially improve its performance and accuracy for certain use cases, making it even more useful for specialized applications.
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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The Llama-2-7B-GGML is a variant of Meta's Llama 2 language model, created by the maintainer TheBloke. This 7 billion parameter model has been optimized for CPU and GPU inference using the GGML format. It is part of a collection of Llama 2 models ranging from 7 billion to 70 billion parameters, with both pretrained and fine-tuned versions available. The fine-tuned models, like this one, are optimized for dialogue use cases. Similar models include the Llama-2-13B-GGML and Llama-2-7B-Chat-GGML, which offer different parameter sizes and optimizations. Model inputs and outputs Inputs Text**: The Llama-2-7B-GGML model takes text as input. Outputs Text**: The model generates text as output. Capabilities The Llama-2-7B-GGML model is capable of a wide range of natural language generation tasks, including dialogue, summarization, and content creation. It has been shown to outperform many open-source chat models on benchmarks, and can provide helpful and safe responses on par with some popular closed-source models. What can I use it for? You can use the Llama-2-7B-GGML model for a variety of commercial and research applications, such as building AI assistants, content generation tools, and language understanding systems. The fine-tuned chat version is particularly well-suited for conversational AI use cases. Things to try Try prompting the Llama-2-7B-GGML model with open-ended questions or instructions to see its versatility in generating coherent and contextual responses. You can also experiment with different temperature and sampling settings to influence the creativity and diversity of the output.
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