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import gradio as gr
from huggingface_hub import InferenceClient
"""
Copied from inference in colab notebook
"""
# import torch
# # Monkey-patch to avoid CUDA initialization issues
# torch.cuda.get_device_capability = lambda *args, **kwargs: (0, 0)
# from unsloth.chat_templates import get_chat_template
# from unsloth import FastLanguageModel
# # IMPORTING MODEL AND TOKENIZER ————————
# max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
# dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
# load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
# model, tokenizer = FastLanguageModel.from_pretrained(
# model_name = "llama_lora_model_1",
# max_seq_length = max_seq_length,
# dtype = dtype,
# load_in_4bit = load_in_4bit,
# )
# tokenizer = get_chat_template(
# tokenizer,
# chat_template = "llama-3.1",
# )
# FastLanguageModel.for_inference(model) # Enable native 2x faster inference
# # RUNNING INFERENCE ————————————————————————
# 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})
# inputs = tokenizer.apply_chat_template(
# messages,
# tokenize = True,
# add_generation_prompt = True, # Must add for generation
# return_tensors = "pt",
# )
# outputs = model.generate(input_ids = inputs, max_new_tokens = max_tokens, use_cache = True,
# temperature = 1.5, min_p = 0.1)
# response = tokenizer.batch_decode(outputs)
# yield response
"""
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(model="https://huggingface.co/Heit39/llama_lora_model_1")
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()
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