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import gradio as gr
# from huggingface_hub import InferenceClient
# from peft import AutoPeftModelForCausalLM
# from transformers import AutoTokenizer, TextStreamer, BitsAndBytesConfig
from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
"""
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
"""
# client = InferenceClient("EITD/lora_model", token=os.getenv("HF_TOKEN"))
# model_name = "lora_model"
# model = AutoPeftModelForCausalLM.from_pretrained(
# model_name,
# load_in_4bit = True,
# )
# tokenizer = AutoTokenizer.from_pretrained(model_name)
model_id = "EITD/model"
filename = "unsloth.Q4_K_M.gguf"
tokenizer = AutoTokenizer.from_pretrained(model_id, gguf_file=filename)
model = AutoModelForCausalLM.from_pretrained(model_id, gguf_file=filename)
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
inputs = tokenizer.apply_chat_template(
messages,
tokenize = True,
add_generation_prompt = True, # Must add for generation
return_tensors = "pt",
)
text_streamer = TextStreamer(tokenizer, skip_prompt = True)
for response in model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = max_tokens, use_cache = True,
temperature = temperature, min_p = top_p):
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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