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import gradio as gr
from huggingface_hub import InferenceClient
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Initialize Hugging Face Inference API client
hf_client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# Load the second model
local_model_name = "codewithdark/latent-recurrent-depth-lm"
tokenizer = AutoTokenizer.from_pretrained(local_model_name)
model = AutoModelForCausalLM.from_pretrained(local_model_name)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
def generate_response(
message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p, model_choice
):
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})
if model_choice == "Zephyr-7B (API)":
response = ""
for message in hf_client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
else:
input_text = tokenizer.apply_chat_template(messages, return_tensors="pt").to(device)
output = model.generate(input_text, max_length=max_tokens, temperature=temperature, top_p=top_p)
response = tokenizer.decode(output[0], skip_special_tokens=True)
yield response
demo = gr.ChatInterface(
generate_response,
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)"),
gr.Radio(["Zephyr-7B (API)", "Latent Recurrent Depth LM"], value="Zephyr-7B (API)", label="Select Model"),
],
)
if __name__ == "__main__":
demo.launch()
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