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from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
AutoTokenizer,
)
from peft import PeftModel, PeftConfig
import torch
import gradio as gr
d_map = {"": torch.cuda.current_device()} if torch.cuda.is_available() else None
local_model_path = "outputs/checkpoint-100" # Path to the combined weights
# Loading the base Model
config = PeftConfig.from_pretrained(local_model_path)
model = AutoModelForCausalLM.from_pretrained(
config.base_model_name_or_path,
return_dict=True,
torch_dtype=torch.float16,
device_map=d_map,
)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path, trust_remote_code=True)
def inferance(query: str, model, tokenizer, temp = 1.0, limit = 200) -> str:
device = "cuda:0"
prompt_template = """
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Question:
{query}
### Answer:
"""
prompt = prompt_template.format(query=query)
encodeds = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
model_inputs = encodeds.to(device)
generated_ids = model.generate(**model_inputs, max_new_tokens=int(limit), temperature=temp, do_sample=True, pad_token_id=tokenizer.eos_token_id)
decoded = tokenizer.batch_decode(generated_ids)
return (decoded[0])
def predict(temp, limit, text):
prompt = text
out = inferance(prompt, model, tokenizer, temp = 1.0, limit = 200)
return out
pred = gr.Interface(
predict,
inputs=[
gr.Slider(0.001, 10, value=0.1, label="Temperature"),
gr.Slider(1, 1024, value=128, label="Token Limit"),
gr.Textbox(
label="Input",
lines=1,
value="#### Human: What's the capital of Australia?#### Assistant: ",
),
],
outputs='text',
)
pred.launch(share=True) |