HaitameLaf
commited on
Commit
•
7c0b176
1
Parent(s):
3716c94
Create app.py
Browse files
app.py
ADDED
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import subprocess
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import sys
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import os
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# Fonction pour installer un package si non présent
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def install_package(package_name):
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subprocess.run([sys.executable, "-m", "pip", "install", package_name], check=True)
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# Vérifiez si torch est installé, sinon installez-le
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try:
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import torch
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except ImportError:
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print("Torch n'est pas installé. Installation de torch...")
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install_package("torch")
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import torch
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# Vérifiez si transformers est installé, sinon installez-le
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try:
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from transformers import (
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AutoModelForCausalLM,
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AutoTokenizer,
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TextIteratorStreamer,
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)
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except ImportError:
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print("Transformers n'est pas installé. Installation de transformers...")
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install_package("transformers")
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from transformers import (
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AutoModelForCausalLM,
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AutoTokenizer,
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TextIteratorStreamer,
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)
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# Installer flash-attn
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subprocess.run(
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"pip install flash-attn --no-build-isolation",
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env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
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shell=True,
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)
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import gradio as gr
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from threading import Thread
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# Obtenir le token d'authentification Hugging Face
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token = os.getenv("HF_TOKEN")
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if not token:
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raise ValueError("Le token d'authentification HF_TOKEN n'est pas défini.")
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# Charger le modèle et le tokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"CampAIgn/Phi-3-mini_16bit",
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token=token,
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trust_remote_code=True,
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)
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tok = AutoTokenizer.from_pretrained("HaitameLaf/Phi3-Game16bit", token=token)
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terminators = [tok.eos_token_id]
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# Vérifier la disponibilité du GPU
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if torch.cuda.is_available():
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device = torch.device("cuda")
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print(f"Using GPU: {torch.cuda.get_device_name(device)}")
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else:
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device = torch.device("cpu")
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print("Using CPU")
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model = model.to(device)
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# Fonction de chat
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def chat(message, history, temperature, do_sample, max_tokens):
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chat = [{"role": "user", "content": item[0]} for item in history]
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chat.extend({"role": "assistant", "content": item[1]} for item in history if item[1])
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chat.append({"role": "user", "content": message})
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messages = tok.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
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model_inputs = tok([messages], return_tensors="pt").to(device)
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streamer = TextIteratorStreamer(tok, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
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generate_kwargs = {
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"input_ids": model_inputs.input_ids,
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"streamer": streamer,
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"max_new_tokens": max_tokens,
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"do_sample": do_sample,
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"temperature": temperature,
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"eos_token_id": terminators,
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}
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t = Thread(target=model.generate, kwargs=generate_kwargs)
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t.start()
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partial_text = ""
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for new_text in streamer:
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partial_text += new_text
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yield partial_text
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yield partial_text
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# Configuration de Gradio
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demo = gr.ChatInterface(
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fn=chat,
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examples=[["Write me a poem about Machine Learning."]],
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additional_inputs_accordion=gr.Accordion(
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label="⚙️ Parameters", open=False, render=False
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),
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additional_inputs=[
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gr.Slider(minimum=0, maximum=1, step=0.1, value=0.9, label="Temperature"),
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gr.Checkbox(label="Sampling", value=True),
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gr.Slider(minimum=128, maximum=4096, step=1, value=512, label="Max new tokens"),
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],
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stop_btn="Stop Generation",
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title="Chat With LLMs",
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description="Now Running [CampAIgn/Phi-3-mini_16bit](https://huggingface.co/CampAIgn/Phi-3-mini_16bit)",
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)
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if __name__ == "__main__":
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demo.launch()
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