| import gradio as gr | |
| from llama_cpp import Llama | |
| from huggingface_hub import hf_hub_download | |
| hf_hub_download(repo_id="LLukas22/gpt4all-lora-quantized-ggjt", filename="ggjt-model.bin", local_dir=".") | |
| llm = Llama(model_path="./ggjt-model.bin", n_threads=2) | |
| def chat(input): | |
| resp = llm(input) | |
| return resp['choices'][0]['text'] | |
| gr.Interface(fn=chat, inputs="text", outputs="text", description="gpt4all: an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue", examples=[['What is a three word topic describing the following keywords: baseball, football, soccer'],['Q: Reverse a string in python\nA: ']]).launch() |