PyTorch – Torchchat: Chat with LLMs Everywhere
The torchchat GitHub repository enables execution of large language models using PyTorch on multiple platforms, supporting models like Llama 3 and Mistral, with features for chatting, text generation, and evaluation.
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The GitHub repository torchchat is designed to facilitate the execution of large language models (LLMs) using PyTorch across various platforms, including Python, C/C++, and mobile devices (iOS and Android). It supports popular models such as Llama 3, Llama 2, and Mistral. Key features include multiple execution modes (Eager, Compile, AOT Inductor, ExecuTorch), cross-platform compatibility (Linux, macOS, Android, iOS), and various quantization schemes to optimize model performance. The repository offers a command line interface for tasks such as chatting, text generation, model evaluation, and artifact management.
To install torchchat, users can clone the repository, set up a virtual environment, and install the necessary dependencies. Usage examples include chatting with models, generating text based on prompts, and running the application in a browser. The repository also provides instructions for deploying models on mobile devices. Model performance can be evaluated using a specific command that leverages the lm_eval library.
The project encourages community contributions and provides guidelines for participation. It is released under the BSD 3 license, with additional code covered by MIT and Apache licenses. For further details, users can visit the torchchat GitHub repository.
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- Users are comparing torchchat to other platforms like Ollama, questioning when to use each.
- There is a debate about the effectiveness of smaller models for general chat versus narrow tasks.
- Some commenters express interest in practical applications, such as integrating LLMs into their workflows.
- Technical inquiries about performance on different hardware, particularly CPUs, are raised.
- Overall, there is a mix of enthusiasm for the tool's potential and concerns about its limitations.
10 commentsI'm not saying it isn't impressive being able to swap but I have trouble understanding how this integrates into my workflow and I don't really want to put much effort into exploring given that there are so many things to explore these days.
Last time I played with LLMs on CPU with pytorch you had to replace some stuff with libraries from Intel otherwise your performance would be really bad.
How about making libtorch a first class citizen without crashes and memory leaks? What happened to the "one tool, one job" philosophy?
As an interesting thought experiment: Should PyTorch be integrated into systemd or should systemd be integrated into PyTorch? Both seem to absorb everything else like a black hole.
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