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import gradio as gr
from peft import PeftModel, PeftTokenizer
from transformers import TextStreamer
# Load model directly
from transformers import AutoModel, AutoTokenizer
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
Info of how to use a model after training on hf https://huggingface.co/docs/trl/main/en/use_model
"""
model_name_or_path = "unsloth/Llama-3.2-3B-Instruct"
adapter_name = "samlama111/lora_model"
model = AutoModel.from_pretrained(model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = PeftModel.from_pretrained(model, adapter_name)
tokenizer = PeftTokenizer.from_pretrained(tokenizer, adapter_name)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
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})
response = ""
inputs = tokenizer.apply_chat_template(
messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)
text_streamer = TextStreamer(tokenizer)
# TODO: Doesn't stream ATM
for message in model.generate(
input_ids=inputs, streamer=text_streamer, max_new_tokens=1024, use_cache=True
):
# Decode the tensor to a string
decoded_message = tokenizer.decode(message, skip_special_tokens=True)
# Manually getting the response
response = decoded_message.split("assistant")[
-1
].strip() # Extract only the assistant's response
print(response)
yield 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 friendly Chatbot.", 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()
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