Create app.py

app.py

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+import os
+import torch
+from datasets import load_dataset
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+    HfArgumentParser,
+    TrainingArguments,
+    pipeline,
+    logging,
+)
+from peft import LoraConfig, PeftModel
+from trl import SFTTrainer
+# The model that you want to train from the Hugging Face hub
+model_name = "meta-llama/Llama-2-7b-chat-hf"
+
+# The instruction dataset to use
+dataset_name = "mlabonne/guanaco-llama2-1k"
+
+# Fine-tuned model name
+new_model = "llama-2-7b-miniguanaco"
+
+################################################################################
+# QLoRA parameters
+################################################################################
+
+# LoRA attention dimension
+lora_r = 64
+
+# Alpha parameter for LoRA scaling
+lora_alpha = 16
+
+# Dropout probability for LoRA layers
+lora_dropout = 0.1
+
+################################################################################
+# bitsandbytes parameters
+################################################################################
+
+# Activate 4-bit precision base model loading
+use_4bit = True
+
+# Compute dtype for 4-bit base models
+bnb_4bit_compute_dtype = "float16"
+
+# Quantization type (fp4 or nf4)
+bnb_4bit_quant_type = "nf4"
+
+# Activate nested quantization for 4-bit base models (double quantization)
+use_nested_quant = False
+
+################################################################################
+# TrainingArguments parameters
+################################################################################
+
+# Output directory where the model predictions and checkpoints will be stored
+output_dir = "./results"
+
+# Number of training epochs
+num_train_epochs = 1
+
+# Enable fp16/bf16 training (set bf16 to True with an A100)
+fp16 = False
+bf16 = False
+
+# Batch size per GPU for training
+per_device_train_batch_size = 4
+
+# Batch size per GPU for evaluation
+per_device_eval_batch_size = 4
+
+# Number of update steps to accumulate the gradients for
+gradient_accumulation_steps = 1
+
+# Enable gradient checkpointing
+gradient_checkpointing = True
+
+# Maximum gradient normal (gradient clipping)
+max_grad_norm = 0.3
+
+# Initial learning rate (AdamW optimizer)
+learning_rate = 2e-4
+
+# Weight decay to apply to all layers except bias/LayerNorm weights
+weight_decay = 0.001
+
+# Optimizer to use
+optim = "paged_adamw_32bit"
+
+# Learning rate schedule (constant a bit better than cosine)
+lr_scheduler_type = "constant"
+
+# Number of training steps (overrides num_train_epochs)
+max_steps = -1
+
+# Ratio of steps for a linear warmup (from 0 to learning rate)
+warmup_ratio = 0.03
+
+# Group sequences into batches with same length
+# Saves memory and speeds up training considerably
+group_by_length = True
+
+# Save checkpoint every X updates steps
+save_steps = 25
+
+# Log every X updates steps
+logging_steps = 25
+
+################################################################################
+# SFT parameters
+################################################################################
+
+# Maximum sequence length to use
+max_seq_length = None
+
+# Pack multiple short examples in the same input sequence to increase efficiency
+packing = False
+
+# Load the entire model on the GPU 0
+device_map = {"": 0}
+# Load dataset (you can process it here)
+dataset = load_dataset(dataset_name, split="train")
+
+# Load tokenizer and model with QLoRA configuration
+compute_dtype = getattr(torch, bnb_4bit_compute_dtype)
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=use_4bit,
+    bnb_4bit_quant_type=bnb_4bit_quant_type,
+    bnb_4bit_compute_dtype=compute_dtype,
+    bnb_4bit_use_double_quant=use_nested_quant,
+)
+
+# Check GPU compatibility with bfloat16
+if compute_dtype == torch.float16 and use_4bit:
+    major, _ = torch.cuda.get_device_capability()
+    if major >= 8:
+        print("=" * 80)
+        print("Your GPU supports bfloat16: accelerate training with bf16=True")
+        print("=" * 80)
+
+# Load base model
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    quantization_config=bnb_config,
+    device_map=device_map
+)
+model.config.use_cache = False
+model.config.pretraining_tp = 1
+
+# Load LLaMA tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "right" # Fix weird overflow issue with fp16 training
+
+# Load LoRA configuration
+peft_config = LoraConfig(
+    lora_alpha=lora_alpha,
+    lora_dropout=lora_dropout,
+    r=lora_r,
+    bias="none",
+    task_type="CAUSAL_LM",
+)
+
+# Set training parameters
+training_arguments = TrainingArguments(
+    output_dir=output_dir,
+    num_train_epochs=num_train_epochs,
+    per_device_train_batch_size=per_device_train_batch_size,
+    gradient_accumulation_steps=gradient_accumulation_steps,
+    optim=optim,
+    save_steps=save_steps,
+    logging_steps=logging_steps,
+    learning_rate=learning_rate,
+    weight_decay=weight_decay,
+    fp16=fp16,
+    bf16=bf16,
+    max_grad_norm=max_grad_norm,
+    max_steps=max_steps,
+    warmup_ratio=warmup_ratio,
+    group_by_length=group_by_length,
+    lr_scheduler_type=lr_scheduler_type,
+    report_to="tensorboard"
+)
+
+# Set supervised fine-tuning parameters
+trainer = SFTTrainer(
+    model=model,
+    train_dataset=dataset,
+    peft_config=peft_config,
+    dataset_text_field="text",
+    max_seq_length=max_seq_length,
+    tokenizer=tokenizer,
+    args=training_arguments,
+    packing=packing,
+)
+
+# Train model
+trainer.train()
+
+# Save trained model
+trainer.model.save_pretrained(new_model)
+# Ignore warnings
+ logging.set_verbosity(logging.CRITICAL)
+
+# Run text generation pipeline with our next model
+prompt = "What is a large language model?"
+pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
+result = pipe(f"<s>[INST] {prompt} [/INST]")
+print(result[0]['generated_text'])
+# Reload model in FP16 and merge it with LoRA weights
+base_model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    low_cpu_mem_usage=True,
+    return_dict=True,
+    torch_dtype=torch.float16,
+    device_map=device_map,
+)
+model = PeftModel.from_pretrained(base_model, new_model)
+model = model.merge_and_unload()
+
+ # Reload tokenizer to save it
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "right"
+kwargs={
+
+}
+model.push_to_hub(**kwargs)
+tokenizer.push_to_hub(new_model, use_temp_dir=False)
+def do_nothing(text):
+    return text
+# Create Gradio interface
+interface = gr.Interface(
+    fn=do_nothing,
+    inputs="text",
+    outputs="text",
+    layout="vertical",
+    title="LLAMA-2-7B Chatbot",
+    description="Enter a prompt and get a chatbot response.",
+    examples=[["Tell me a joke."]],
+)
+
+if __name__ == "__main__":
+    interface.launch()