Spaces:
Running
Running
File size: 3,131 Bytes
2b35f7d 0b40748 92992ea 0b40748 92992ea 0b40748 92992ea 0b40748 92992ea 0b40748 92992ea 0b40748 92992ea 0b40748 2b35f7d 0b40748 2b35f7d 0b40748 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
import torch
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
from peft import get_peft_model, LoraConfig, TaskType
from datasets import load_dataset
# ✅ Check if a GPU is available, otherwise use CPU
device = "cuda" if torch.cuda.is_available() else "cpu"
# ✅ Function to start training
def train_model(dataset_url, model_url, epochs):
try:
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_url)
model = AutoModelForCausalLM.from_pretrained(model_url).to(device)
# ✅ Apply LoRA (Reduces trainable parameters)
lora_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
r=8,
lora_alpha=32,
lora_dropout=0.1,
target_modules=["q_proj", "v_proj"]
)
model = get_peft_model(model, lora_config)
model.to(device)
# ✅ Load dataset
dataset = load_dataset(dataset_url)
# ✅ Tokenization function
def tokenize_function(examples):
return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=256)
tokenized_datasets = dataset.map(tokenize_function, batched=True)
train_dataset = tokenized_datasets["train"]
# ✅ Training Arguments (Optimized for CPU)
training_args = TrainingArguments(
output_dir="./deepseek_lora_cpu",
evaluation_strategy="epoch",
learning_rate=5e-4,
per_device_train_batch_size=1, # Keeps memory low
per_device_eval_batch_size=1,
num_train_epochs=int(epochs),
save_strategy="epoch",
save_total_limit=2,
logging_dir="./logs",
logging_steps=10,
fp16=False, # Disable FP16 for CPU
gradient_checkpointing=True, # Saves memory
optim="adamw_torch",
report_to="none"
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset
)
# ✅ Start Training
trainer.train()
# ✅ Save the Fine-Tuned Model
model.save_pretrained("./deepseek_lora_finetuned")
tokenizer.save_pretrained("./deepseek_lora_finetuned")
return "✅ Training Completed! Model saved successfully."
except Exception as e:
return f"❌ Error: {str(e)}"
# ✅ Gradio UI
with gr.Blocks() as app:
gr.Markdown("# 🚀 AutoTrain DeepSeek R1 (CPU)")
dataset_url = gr.Textbox(label="Dataset URL (Hugging Face)", placeholder="e.g. samsum")
model_url = gr.Textbox(label="Model URL (Hugging Face)", placeholder="e.g. deepseek-ai/deepseek-r1")
epochs = gr.Slider(minimum=1, maximum=10, step=1, value=3, label="Number of Training Epochs")
train_button = gr.Button("Start Training")
output_text = gr.Textbox(label="Training Output")
train_button.click(train_model, inputs=[dataset_url, model_url, epochs], outputs=output_text)
# ✅ Launch the app
app.launch(server_name="0.0.0.0", server_port=7860) |