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import gradio as gr
import os
from huggingface_hub import InferenceClient
HF_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
client = InferenceClient(model="HuggingFaceH4/zephyr-7b-beta", token=HF_TOKEN)
def respond(message):
for response in client.chat_completion(messages=[{"role": "user", "content": message}]):
yield response["choices"][0]["message"]["content"]
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 = ""
for message in 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
"""
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()
import os
from transformers import AutoModelForCasualLM, AutoTokenizer
from peft import PeftModel
import torch
if not api_token:
api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
raise ValueError("❌ ERROR: Hugging Face API token is not set. Please set it as an environment variable.")
# Define model names
base_model_name = "mistralai/Mistral-7B-Instruct-v0.3"
peft_model_name = "prempranavreddy/MP1"
# Load base model with authentication
base_model = AutoModelForCausalLM.from_pretrained(
base_model_name,
torch_dtype=torch.float16,
device_map="auto",
use_auth_token=api_token # ✅ Correct
)
# Load fine-tuned model
model = PeftModel.from_pretrained(base_model, peft_model_name, token=api_token)
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model_name, token=api_token) |