| 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'] | |
| g = gr.Interface(fn=chat, inputs="text", outputs="text", title="GPT4ALL", description="gpt4all: an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue") | |
| g.queue(concurrency_count=1) | |
| g.launch() |