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from unsloth import FastLanguageModel
from peft import PeftModel
from transformers import AutoTokenizer
import gradio as gr
base_model_name = "unsloth/Llama-3.2-3B-Instruct"
base_model, tokenizer = FastLanguageModel.from_pretrained(
model_name=base_model_name,
max_seq_length=2048,
dtype=None,
load_in_4bit=True
)
# lora adapters from my Hugging Face model
lora_model_name = "oskaralf/lora_model" # Hugging Face repository for LoRA adapters
model = PeftModel.from_pretrained(base_model, lora_model_name)
FastLanguageModel.for_inference(model)
def chatbot(input_text):
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(input_ids=inputs["input_ids"], max_new_tokens=64)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
iface = gr.Interface(fn=chatbot, inputs="text", outputs="text", title="Chatbot")
iface.launch()
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