Update app.py
Browse files
app.py
CHANGED
@@ -1,3 +1,26 @@
|
|
1 |
import gradio as gr
|
|
|
|
|
|
|
2 |
|
3 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
import gradio as gr
|
2 |
+
from peft import PeftModel, PeftConfig
|
3 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
|
4 |
+
import torch
|
5 |
|
6 |
+
# Load the model and config when the script starts
|
7 |
+
config = PeftConfig.from_pretrained("PhantHive/llama-2-7b-momo")
|
8 |
+
model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-chat-hf")
|
9 |
+
model = PeftModel.from_pretrained(model, "PhantHive/llama-2-7b-momo")
|
10 |
+
|
11 |
+
# Load the tokenizer
|
12 |
+
tokenizer = AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-chat-hf")
|
13 |
+
|
14 |
+
|
15 |
+
def greet(text):
|
16 |
+
batch = tokenizer(f"'{text}' ->: ", return_tensors='pt')
|
17 |
+
|
18 |
+
# Use torch.no_grad to disable gradient calculation
|
19 |
+
with torch.no_grad():
|
20 |
+
output_tokens = model.generate(**batch, do_sample=True, max_new_tokens=50)
|
21 |
+
|
22 |
+
return tokenizer.decode(output_tokens[0], skip_special_tokens=True)
|
23 |
+
|
24 |
+
|
25 |
+
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
|
26 |
+
iface.launch()
|