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--- |
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license: apache-2.0 |
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pipeline_tag: text-generation |
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library_name: transformers |
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--- |
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# AMD-135m |
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## Introduction |
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AMD-Llama-135m is a language model trained on AMD MI250 GPUs. Based on LLaMA2 model architecture, this model can be smoothly loaded as LlamaForCausalLM with huggingface transformers. Furthermore, we use the same tokenizer as LLaMA2, enabling it to be a draft model of speculative decoding for LLaMA2 and CodeLlama. |
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```python |
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import sys |
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import os |
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from PyQt5.QtWidgets import (QApplication, QWidget, QLabel, QPushButton, |
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QLineEdit, QTextEdit, QVBoxLayout, QHBoxLayout, |
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QFileDialog, QProgressBar, QMessageBox, QMenu) |
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from PyQt5.QtCore import Qt, QThread, pyqtSignal |
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from llama_cpp import Llama |
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class Worker(QThread): |
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finished = pyqtSignal(str) |
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progress = pyqtSignal(int, int) # Pass total tokens as well |
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def __init__(self, model, messages, max_tokens): |
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super().__init__() |
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self.model = model |
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self.messages = messages |
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self.max_tokens = max_tokens |
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def run(self): |
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try: |
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response = self.model.create_chat_completion( |
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messages=self.messages, |
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max_tokens=self.max_tokens, |
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temperature=0.7, |
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stream=True |
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) |
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total_tokens = 0 |
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full_response = "" |
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for chunk in response: |
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if "choices" in chunk: |
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content = chunk["choices"][0]["delta"].get("content", "") |
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full_response += content |
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total_tokens += 1 # Assume each chunk is 1 token (adjust if needed) |
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self.progress.emit(total_tokens, self.max_tokens) |
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self.finished.emit(full_response) |
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except Exception as e: |
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self.finished.emit(f"Error generating response: {str(e)}") |
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class ChatbotGUI(QWidget): |
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def __init__(self): |
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super().__init__() |
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self.setWindowTitle("Chatbot GUI") |
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self.resize(800, 600) |
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self.model = None |
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self.messages = [ |
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{"role": "system", "content": "You are a helpful AI assistant."} |
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] |
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self.initUI() |
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def initUI(self): |
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# Model loading section |
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model_label = QLabel("Model: No model loaded") |
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load_button = QPushButton("Load GGUF Model") |
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load_button.clicked.connect(self.load_model) |
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model_layout = QHBoxLayout() |
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model_layout.addWidget(model_label) |
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model_layout.addWidget(load_button) |
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# Chat display |
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self.chat_display = QTextEdit() |
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self.chat_display.setReadOnly(True) |
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self.chat_display.setContextMenuPolicy(Qt.CustomContextMenu) |
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self.chat_display.customContextMenuRequested.connect(self.show_context_menu) |
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# User input |
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self.user_input = QLineEdit() |
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self.user_input.returnPressed.connect(self.send_message) |
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send_button = QPushButton("Send") |
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send_button.clicked.connect(self.send_message) |
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input_layout = QHBoxLayout() |
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input_layout.addWidget(self.user_input) |
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input_layout.addWidget(send_button) |
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# Progress bar |
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self.progress_bar = QProgressBar() |
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self.progress_bar.hide() |
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# Clear conversation button |
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clear_button = QPushButton("Clear Conversation") |
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clear_button.clicked.connect(self.clear_conversation) |
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# Main layout |
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main_layout = QVBoxLayout() |
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main_layout.addLayout(model_layout) |
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main_layout.addWidget(self.chat_display) |
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main_layout.addWidget(self.progress_bar) |
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main_layout.addLayout(input_layout) |
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main_layout.addWidget(clear_button) |
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self.setLayout(main_layout) |
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def load_model(self): |
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model_path, _ = QFileDialog.getOpenFileName(self, "Load GGUF Model", "", "GGUF Files (*.gguf)") |
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if model_path: |
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try: |
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self.model = Llama(model_path=model_path, n_ctx=2048, n_gpu_layers=-1) |
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model_name = os.path.basename(model_path) |
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self.layout().itemAt(0).itemAt(0).widget().setText(f"Model: {model_name}") |
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QMessageBox.information(self, "Success", "Model loaded successfully!") |
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except Exception as e: |
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error_message = f"Error loading model: {str(e)}" |
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QMessageBox.critical(self, "Error", error_message) |
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def send_message(self): |
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user_message = self.user_input.text() |
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if user_message and self.model: |
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self.messages.append({"role": "user", "content": user_message}) |
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self.update_chat_display(f"You: {user_message}") |
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self.user_input.clear() |
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max_tokens = 500 # Set your desired max tokens here |
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self.progress_bar.show() |
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self.progress_bar.setRange(0, max_tokens) |
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self.progress_bar.setValue(0) |
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self.worker = Worker(self.model, self.messages, max_tokens) |
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self.worker.finished.connect(self.on_response_finished) |
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self.worker.progress.connect(self.on_response_progress) |
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self.worker.start() |
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def on_response_finished(self, assistant_message): |
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self.progress_bar.hide() |
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self.messages.append({"role": "assistant", "content": assistant_message}) |
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self.update_chat_display(f"Assistant: {assistant_message}") |
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# Python Code Download (Check for triple backticks) |
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if assistant_message.startswith("```python") and assistant_message.endswith("```"): |
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self.offer_code_download(assistant_message) |
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def on_response_progress(self, current_tokens, total_tokens): |
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self.progress_bar.setValue(current_tokens) |
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def offer_code_download(self, code): |
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reply = QMessageBox.question(self, "Download Code", |
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"The assistant generated Python code. Do you want to download it?", |
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QMessageBox.Yes | QMessageBox.No) |
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if reply == QMessageBox.Yes: |
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file_path, _ = QFileDialog.getSaveFileName(self, "Save Python Code", "code.py", "Python Files (*.py)") |
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if file_path: |
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try: |
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with open(file_path, "w") as f: |
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f.write(code.strip("```python").strip("```")) |
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QMessageBox.information(self, "Success", "Code saved successfully!") |
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except Exception as e: |
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QMessageBox.critical(self, "Error", f"Error saving code: {str(e)}") |
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def update_chat_display(self, message): |
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self.chat_display.append(message + "\n") |
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self.chat_display.verticalScrollBar().setValue(self.chat_display.verticalScrollBar().maximum()) |
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def clear_conversation(self): |
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self.messages = [ |
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{"role": "system", "content": "You are a helpful AI assistant."} |
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] |
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self.chat_display.clear() |
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def show_context_menu(self, point): |
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menu = QMenu(self) |
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copy_action = menu.addAction("Copy") |
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copy_action.triggered.connect(self.copy_text) |
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menu.exec_(self.chat_display.mapToGlobal(point)) |
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def copy_text(self): |
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cursor = self.chat_display.textCursor() |
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if cursor.hasSelection(): |
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text = cursor.selectedText() |
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QApplication.clipboard().setText(text) |
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if __name__ == "__main__": |
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app = QApplication(sys.argv) |
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gui = ChatbotGUI() |
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gui.show() |
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sys.exit(app.exec_()) |
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``` |
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## Training and finetuning cost |
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It takes 6 days to pretrain AMD-Llama-135m on 4 MI250 nodes each of which has 4 MI250 GPUs (8 virtual GPU cards, 64G memory for each). |
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It takes 4 days to finetune AMD-Llama-135m-code on 4 MI250 GPUs. |
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It takes 11T disk space to store raw and processed SlimPajama, project gutenberg and Starcoder datasets. |
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#### License |
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Copyright (c) 2018-2024 Advanced Micro Devices, Inc. All Rights Reserved. |
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Licensed under the Apache License, Version 2.0 (the "License"); |
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you may not use this file except in compliance with the License. |
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You may obtain a copy of the License at |
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http://www.apache.org/licenses/LICENSE-2.0 |
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Unless required by applicable law or agreed to in writing, software |
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distributed under the License is distributed on an "AS IS" BASIS, |
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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See the License for the specific language governing permissions and |
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limitations under the License. |
