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Local LLM Support

Running local LLMs can offer several advantages such as:

  • Enhanced privacy and data security
  • Cost control and no API rate limits
  • Greater customization and fine-tuning options
  • Reduced vendor lock-in

We enable serving local LLMs with llamafile. In the API, local LLM support is available through the is_local parameter. If is_local=True, then a local (llamafile) LLM model is used to generate the podcast transcript. Llamafiles of LLM models can be found on HuggingFace, which today offers 156+ models.

All you need to do is:

  1. Download a llamafile from HuggingFace
  2. Make the file executable
  3. Run the file

Here's a simple bash script that shows all 3 setup steps for running TinyLlama-1.1B locally:

# Download a llamafile from HuggingFace
wget https://huggingface.co/jartine/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile

# Make the file executable. On Windows, instead just rename the file to end in ".exe".
chmod +x TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile

# Start the model server. Listens at http://localhost:8080 by default.
./TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile --server --nobrowser

Now you can use the local LLM to generate a podcast transcript (or audio) by setting the is_local parameter to True.

Python API

from podcastfy.client import generate_podcast

# Generate a tech debate podcast about artificial intelligence
generate_podcast(
    urls=["www.souzatharsis.com"],
    is_local=True  # Using a local LLM
)

CLI

To use a local LLM model via the command-line interface, you can use the --local or -l flag. Here's an example of how to generate a transcript using a local LLM:

python -m podcastfy.client --url https://example.com/article1 --transcript-only --local

Notes of caution

When using local LLM models versus widely known private large language models:

  1. Performance: Local LLMs often have lower performance compared to large private models due to size and training limitations.

  2. Resource requirements: Running local LLMs can be computationally intensive, requiring significant CPU/GPU resources.

  3. Limited capabilities: Local models may struggle with complex tasks or specialized knowledge that larger models handle well.

  4. Reduced multimodal abilities: Local LLMs will be assumed to be text-only capable

  5. Potential instability: Local models may produce less consistent or stable outputs compared to well-tested private models oftentimes producing transcripts that cannot be used for podcast generation (TTS) out-of-the-box

  6. Limited context window: Local models often have smaller context windows, limiting their ability to process long inputs.

Always evaluate the trade-offs between using local LLMs and private models based on your specific use case and requirements. We highly recommend extensively testing your local LLM before productionizing an end-to-end podcast generation and/or manually checking the transcript before passing to TTS model.