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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch
from peft import PeftModel
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
base_model_id = "mistralai/Mistral-7B-v0.1"
ft_model_id = "asusevski/mistraloo-sft"
tokenizer = AutoTokenizer.from_pretrained(
base_model_id,
add_bos_token=True
)
base_model_id = "mistralai/Mistral-7B-v0.1"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
base_model = AutoModelForCausalLM.from_pretrained(
base_model_id,
quantization_config=bnb_config,
device_map="auto",
trust_remote_code=True,
token=True
)
model = PeftModel.from_pretrained(base_model, ft_model_id).to(device)
model.eval()
def uwaterloo_output(post_title, post_text):
prompt = f"""
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
Respond to the reddit post in the style of a University of Waterloo student.
### Input:
{post_title}
{post_text}
### Response:
"""
model_input = tokenizer(prompt, return_tensors="pt").to(device)
with torch.no_grad():
model_output = model.generate(**model_input, max_new_tokens=256, repetition_penalty=1.15)[0]
output = tokenizer.decode(model_output, skip_special_tokens=True)
return output.split('### Response:\n')[-1]
iface = gr.Interface(
fn=uwaterloo_output,
inputs=[
gr.Textbox("", label="Post Title"),
gr.Textbox("", label="Post Text"),
],
outputs=gr.Textbox("", label="Mistraloo-SFT")
)
iface.launch()
# base_model_id = "mistralai/Mistral-7B-v0.1"
# bnb_config = BitsAndBytesConfig(
# load_in_4bit=True,
# bnb_4bit_use_double_quant=True,
# bnb_4bit_quant_type="nf4",
# bnb_4bit_compute_dtype=torch.bfloat16
# )
# base_model = AutoModelForCausalLM.from_pretrained(
# base_model_id, # Mistral, same as before
# quantization_config=bnb_config, # Same quantization config as before
# device_map="auto",
# trust_remote_code=True,
# use_auth_token=True
# )
# ft_model = PeftModel.from_pretrained(base_model, "mistral-mistraloo/checkpoint-500") |