Made in Vancouver, Canada by Picovoice
picoLLM Inference Engine is a highly accurate and cross-platform SDK optimized for running compressed large language models. picoLLM Inference Engine is:
- Accurate; picoLLM Compression improves GPTQ by significant margins
- Private; LLM inference runs 100% locally.
- Cross-Platform
- Runs on CPU and GPU
- Free for open-weight models
- Python 3.8+
- Runs on Linux (x86_64), macOS (arm64, x86_64), Windows (x86_64), and Raspberry Pi (5 and 4).
pip3 install picollmdemo
picoLLM Inference Engine supports the following open-weight models. The models are on Picovoice Console.
- Gemma
gemma-2b
gemma-2b-it
gemma-7b
gemma-7b-it
- Llama-2
llama-2-7b
llama-2-7b-chat
llama-2-13b
llama-2-13b-chat
llama-2-70b
llama-2-70b-chat
- Llama-3
llama-3-8b
llama-3-8b-instruct
llama-3-70b
llama-3-70b-instruct
- Mistral
mistral-7b-v0.1
mistral-7b-instruct-v0.1
mistral-7b-instruct-v0.2
- Mixtral
mixtral-8x7b-v0.1
mixtral-8x7b-instruct-v0.1
- Phi-2
phi2
AccessKey is your authentication and authorization token for deploying Picovoice SDKs, including picoLLM. Anyone who is using Picovoice needs to have a valid AccessKey. You must keep your AccessKey secret. You would need internet connectivity to validate your AccessKey with Picovoice license servers even though the LLM inference is running 100% offline and completely free for open-weight models. Everyone who signs up for Picovoice Console receives a unique AccessKey.
There are two demos available: completion and chat. The completion demo accepts a prompt and a set of optional
parameters and generates a single completion. It can run all models, whether instruction-tuned or not. The chat demo can
run instruction-tuned (chat) models such as llama-3-8b-instruct
, phi2
, etc. The chat demo enables a back-and-forth
conversation with the LLM, similar to ChatGPT.
Run the demo by entering the following in the terminal:
picollm_demo_completion --access_key ${ACCESS_KEY} --model_path ${MODEL_PATH} --prompt ${PROMPT}
Replace ${ACCESS_KEY}
with yours obtained from Picovoice Console, ${MODEL_PATH}
with the path to a model file
downloaded from Picovoice Console, and ${PROMPT}
with a prompt string.
To get information about all the available options in the demo, run the following:
picollm_demo_completion --help
To run an instruction-tuned model for chat, run the following in the terminal:
picollm_demo_chat --access_key ${ACCESS_KEY} --model_path ${MODEL_PATH}
Replace ${ACCESS_KEY}
with yours obtained from Picovoice Console and ${MODEL_PATH}
with the path to a model file
downloaded from Picovoice Console.
To get information about all the available options in the demo, run the following:
picollm_demo_chat --help