File size: 2,212 Bytes
8b04d55 dfce08c 8b04d55 dfce08c 8b04d55 dfce08c 8b04d55 dfce08c 8b04d55 dfce08c 8b04d55 dfce08c 8b04d55 dfce08c 8b04d55 dfce08c 8b04d55 dfce08c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import spaces
# Load model and tokenizer
device = "cuda" # the device to load the model onto
tokenizer = AutoTokenizer.from_pretrained("yuchenlin/Rex-v0.1-1.5B", trust_remote_code=True, rex_size=3)
model = AutoModelForCausalLM.from_pretrained(
"yuchenlin/Rex-v0.1-1.5B",
torch_dtype="auto"
)
model.to(device)
@spaces.GPU(enable_queue=True)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens=512,
temperature=0.5,
top_p=1.0,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens = max_tokens,
temperature = temperature,
top_p = top_p,
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
return response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a helpful AI assistant and your name is RexLM.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch(share=False) |