Llamafile Embeddings

One of the simplest ways to run an LLM locally is using a llamafile. llamafiles bundle model weights and a specially-compiled version of llama.cpp into a single file that can run on most computers any additional dependencies. They also come with an embedded inference server that provides an API for interacting with your model.

Setup

Download a llamafile from HuggingFace
Make the file executable
Run the file

Here's a simple bash script that shows all 3 setup steps:

bash

# 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 --embedding

Your model's inference server listens at localhost:8080 by default.

python

%pip install llama-index-embeddings-llamafile

python

!pip install llama-index

python

from llama_index.embeddings.llamafile import LlamafileEmbedding

embedding = LlamafileEmbedding(
    base_url="http://localhost:8080",
)

pass_embedding = embedding.get_text_embedding_batch(
    ["This is a passage!", "This is another passage"], show_progress=True
)
print(len(pass_embedding), len(pass_embedding[0]))

query_embedding = embedding.get_query_embedding("Where is blue?")
print(len(query_embedding))
print(query_embedding[:10])