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| import torch | |
| import gradio as gr | |
| from llmtuner import create_ui | |
| create_ui().queue().launch(share=True) | |
| from llmtuner import run_exp | |
| run_exp(dict( | |
| stage="sft", | |
| do_train=True, | |
| model_name_or_path="Qwen/Qwen1.5-0.5B-Chat", | |
| dataset="identity,alpaca_gpt4_en,alpaca_gpt4_zh", | |
| template="qwen", | |
| finetuning_type="lora", | |
| lora_target="all", | |
| output_dir="test_identity", | |
| per_device_train_batch_size=4, | |
| gradient_accumulation_steps=4, | |
| lr_scheduler_type="cosine", | |
| logging_steps=10, | |
| save_steps=100, | |
| learning_rate=1e-4, | |
| num_train_epochs=5.0, | |
| max_samples=500, | |
| max_grad_norm=1.0, | |
| fp16=True, | |
| )) | |
| from llmtuner import ChatModel | |
| chat_model = ChatModel(dict( | |
| model_name_or_path="Qwen/Qwen1.5-0.5B-Chat", | |
| adapter_name_or_path="test_identity", | |
| finetuning_type="lora", | |
| template="qwen", | |
| )) | |
| messages = [] | |
| while True: | |
| query = input("\nUser: ") | |
| if query.strip() == "exit": | |
| break | |
| if query.strip() == "clear": | |
| messages = [] | |
| continue | |
| messages.append({"role": "user", "content": query}) | |
| print("Assistant: ", end="", flush=True) | |
| response = "" | |
| for new_text in chat_model.stream_chat(messages): | |
| print(new_text, end="", flush=True) | |
| response += new_text | |
| print() | |
| messages.append({"role": "assistant", "content": response}) | |
| from llmtuner import export_model | |
| export_model(dict( | |
| model_name_or_path="Qwen/Qwen1.5-0.5B-Chat", | |
| adapter_name_or_path="test_identity", | |
| finetuning_type="lora", | |
| template="qwen", | |
| export_dir="test_exported", | |
| # export_hub_model_id="your_hf_id/test_identity", | |
| )) |