Use Llama models in ollama and Librechat

meta-llama-in-ollama-librechat.md

To download and use Llama models in LibreChat on your Mac, potentially via Ollama, follow these steps:

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Step 1: Download the Llama Models

1. Request Access

• Visit the Meta Llama Downloads page.
• Complete the access request form and accept Meta's Community License Agreement.
• Once approved, you’ll receive a signed URL for downloading the model weights.

2. Install the Llama CLI

• Open your terminal and install the Llama CLI tool:

  pip install llama-stack

• Verify installation by running:

  llama --help

3. List Available Models

• Use the CLI to list available models:

  llama model list

• Identify the model ID for the version you want (e.g., Llama 3.1-8B or Llama 4).

4. Download the Model

• Run the following command to download your chosen model:

  llama download --source meta --model-id CHOSEN_MODEL_ID

• When prompted, provide the signed URL you received via email.

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Step 2: Prepare for LibreChat

LibreChat supports running local models like Llama if they are compatible with its backend. You can integrate them using Ollama or directly through a backend like Hugging Face Transformers.

Use Ollama

1. Install Ollama:

   • Download and install Ollama from its official website.
   • Verify installation by running:

     ollama --version

2. Import the Model into Ollama:

   • Move your downloaded model files into a directory accessible by Ollama.
   • Use Ollama’s import feature to load the model:

     ollama import /path/to/your/model

3. Run LibreChat with Ollama Backend:

   • Configure LibreChat to use Ollama as its backend for local models. Follow LibreChat’s documentation for backend setup.

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Step 3: Optimize for Mac Usage

If you have limited hardware resources, consider quantization to reduce memory usage:

• Use FP8 or Int4 quantization modes during inference:

  torchrun --nproc_per_node=1 \
    -m models.llama4.scripts.chat_completion \
    --quantization-mode int4_mixed \
    --checkpoint-dir /path/to/checkpoints

This reduces GPU memory requirements while maintaining reasonable performance.

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