.env.huggingface.example

# You can use any model that available to you and deployed on Hugging Face with compatible API
# X_NAME variables are optional for HuggingFace API you can use them for your convenience

# Make sure your key has permission to use all models
# Set up you key here: https://huggingface.co/docs/api-inference/en/quicktour#get-your-api-token
HF_API_KEY=hf_YOUR_HUGGINGFACE_API_KEY

# For example you can try public Inference API endpoint for Meta-Llama-3-70B-Instruct model
# This model quiality is comparable with GPT-4
# But public API has strict limit for output tokens, so it is very hard to use it for this usecase
# You can use your private API endpoint for this model
# Or use any other Hugging Face model that supports Messages API
# Don't forget to add '/v1' to the end of the URL
LLM_URL=https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-70B-Instruct/v1
LLM_TYPE=HF_API
LLM_NAME=Meta-Llama-3-70B-Instruct

# The Open AI whisper family with more models is available on HuggingFace:
# https://huggingface.co/collections/openai/whisper-release-6501bba2cf999715fd953013
# You can also use any other compatible STT model from HuggingFace
STT_URL=https://api-inference.huggingface.co/models/openai/whisper-tiny.en
STT_TYPE=HF_API
STT_NAME=whisper-tiny.en

# You can use compatible TTS model from HuggingFace
# For example you can try public Inference API endpoint for Facebook MMS-TTS model
# In my experience OS TTS models from HF sound much more robotic than OpenAI TTS models
TTS_URL=https://api-inference.huggingface.co/models/facebook/mms-tts-eng
TTS_TYPE=HF_API
TTS_NAME=Facebook-mms-tts-eng