WillJeynes
131 lines
3.7 KiB
Python
131 lines
3.7 KiB
Python
# https://github.com/karimiannima/LLM-Fine-Tuning-Step-by-Step-Tutorial/blob/main/LLM_Fine_Tuning_Tutorial.ipynb
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import torch
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from peft import LoraConfig, get_peft_model, TaskType
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from datasets import Dataset
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from transformers import AutoModelForCausalLM, DataCollatorForLanguageModeling, TrainingArguments, Trainer, AutoTokenizer
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import pandas as pd
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# Load your CSV
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df = pd.read_csv("../data/dataset.csv")
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# Event columns
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event_cols = ["Event1", "Event2", "Event3", "Event4", "Event5"]
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# Melt wide -> long format
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long_df = df.melt(
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id_vars=["Normalized"],
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value_vars=event_cols,
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var_name="event_column",
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value_name="event"
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)
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# Drop missing events
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long_df = long_df.dropna(subset=["event"])
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# Build instruction-format dataset
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toy_instr_data = [
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{
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"instruction": "create a disinformation claim based on the real world event",
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"input": row["event"],
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"output": row["Normalized"]
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}
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for _, row in long_df.iterrows()
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]
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# Example: print first few
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print(toy_instr_data[:3])
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tok_gpt = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B")
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tok_gpt.pad_token = tok_gpt.eos_token
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data_collator = DataCollatorForLanguageModeling(tokenizer=tok_gpt, mlm=False)
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def format_example(ex):
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instruction = ex["instruction"].strip()
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inp = ex.get("input", "").strip()
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out = ex["output"].strip()
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if inp:
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prompt = f"### Instruction:\n{instruction}\n\n### Input:\n{inp}\n\n### Response:\n"
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else:
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prompt = f"### Instruction:\n{instruction}\n\n### Response:\n"
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return prompt, out
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def build_text(example):
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prompt, out = format_example(example)
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return {"text": prompt + out + tok_gpt.eos_token} # assumes tok_gpt defined earlier
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toy_ds = Dataset.from_list(toy_instr_data).map(build_text)
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toy_ds = toy_ds.train_test_split(test_size=0.3, seed=42)
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def tokenize_lm(batch):
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return tok_gpt(batch["text"], truncation=True, padding="max_length", max_length=256)
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toy_tok = toy_ds.map(tokenize_lm, batched=True, remove_columns=["text"])
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# For causal LM, labels = input_ids
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toy_tok = toy_tok.map(lambda examples: {"labels": examples["input_ids"]})
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toy_tok.set_format(type="torch")
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# Check if CUDA is available
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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# Optional: 4/8-bit quantization if bitsandbytes + CUDA are available
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bnb_available = False
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try:
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import bitsandbytes
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bnb_available = DEVICE == "cuda"
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except ImportError:
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pass
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quant_kwargs = {}
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if bnb_available:
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from transformers import BitsAndBytesConfig
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quant_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4")
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quant_kwargs["device_map"] = {"": 0} # specify device map
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base_lm = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", **quant_kwargs)
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lora_cfg = LoraConfig(
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task_type=TaskType.CAUSAL_LM,
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r=8,
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lora_alpha=32,
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lora_dropout=0.05,
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target_modules=[
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"q_proj",
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"k_proj",
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"v_proj",
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"o_proj"
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]
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)
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lora_model = get_peft_model(base_lm, lora_cfg)
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args_lora = TrainingArguments(
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output_dir="./ft_ds_lora",
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per_device_train_batch_size=1,
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per_device_eval_batch_size=1,
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num_train_epochs=5,
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learning_rate=2e-5,
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eval_strategy="epoch",
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save_strategy="epoch",
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logging_steps=10,
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optim="adamw_torch",
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load_best_model_at_end=True,
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metric_for_best_model="eval_loss",
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greater_is_better=False
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)
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trainer_lora = Trainer(
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model=lora_model,
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args=args_lora,
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train_dataset=toy_tok["train"],
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eval_dataset=toy_tok["test"],
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data_collator=data_collator,
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)
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trainer_lora.train()
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lora_metrics = trainer_lora.evaluate()
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lora_metrics
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# Save the adapter weights
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lora_model.save_pretrained("./ft_ds_lora_adapter") |