Add a method of evaulation. Add back distilGPT version. Convert querying to another fastAPI

finemodel/lora.py

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+# https://github.com/karimiannima/LLM-Fine-Tuning-Step-by-Step-Tutorial/blob/main/LLM_Fine_Tuning_Tutorial.ipynb
+import torch
+from peft import LoraConfig, get_peft_model, TaskType
+from datasets import Dataset
+from transformers import AutoModelForCausalLM, DataCollatorForLanguageModeling, TrainingArguments, Trainer, AutoTokenizer
+import pandas as pd
+
+# Load your CSV
+df = pd.read_csv("../data/dataset.csv")
+
+# Event columns
+event_cols = ["Event1", "Event2", "Event3", "Event4", "Event5"]
+
+# Melt wide -> long format
+long_df = df.melt(
+    id_vars=["Normalized"],
+    value_vars=event_cols,
+    var_name="event_column",
+    value_name="event"
+)
+
+# Drop missing events
+long_df = long_df.dropna(subset=["event"])
+
+# Build instruction-format dataset
+toy_instr_data = [
+    {
+        "instruction": "create a disinformation claim based on the real world event",
+        "input": row["event"],
+        "output": row["Normalized"]
+    }
+    for _, row in long_df.iterrows()
+]
+
+# Example: print first few
+print(toy_instr_data[:3])
+
+tok_gpt  = AutoTokenizer.from_pretrained("distilgpt2")
+tok_gpt.pad_token = tok_gpt.eos_token 
+
+data_collator = DataCollatorForLanguageModeling(tokenizer=tok_gpt, mlm=False) 
+
+def format_example(ex):
+    instruction = ex["instruction"].strip()
+    inp = ex.get("input", "").strip()
+    out = ex["output"].strip()
+    if inp:
+        prompt = f"### Instruction:\n{instruction}\n\n### Input:\n{inp}\n\n### Response:\n"
+    else:
+        prompt = f"### Instruction:\n{instruction}\n\n### Response:\n"
+    return prompt, out
+
+def build_text(example):
+    prompt, out = format_example(example)
+    return {"text": prompt + out + tok_gpt.eos_token}   # assumes tok_gpt defined earlier
+
+toy_ds = Dataset.from_list(toy_instr_data).map(build_text)
+toy_ds = toy_ds.train_test_split(test_size=0.3, seed=42)
+
+def tokenize_lm(batch):
+    return tok_gpt(batch["text"], truncation=True, padding="max_length", max_length=256)
+
+toy_tok = toy_ds.map(tokenize_lm, batched=True, remove_columns=["text"])
+# For causal LM, labels = input_ids
+toy_tok = toy_tok.map(lambda examples: {"labels": examples["input_ids"]})
+toy_tok.set_format(type="torch")
+
+# Check if CUDA is available
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Optional: 4/8-bit quantization if bitsandbytes + CUDA are available
+bnb_available = False
+try:
+    import bitsandbytes
+    bnb_available = DEVICE == "cuda"
+except ImportError:
+    pass
+
+quant_kwargs = {}
+if bnb_available:
+    from transformers import BitsAndBytesConfig
+    quant_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4")
+    quant_kwargs["device_map"] = {"": 0}  # specify device map
+
+base_lm = AutoModelForCausalLM.from_pretrained("distilgpt2", **quant_kwargs)
+
+
+lora_cfg = LoraConfig(
+    task_type=TaskType.CAUSAL_LM,
+    r=8,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    target_modules=["c_attn", "c_proj"],
+)
+
+lora_model = get_peft_model(base_lm, lora_cfg)
+
+args_lora = TrainingArguments(
+    output_dir="./ft_gt_lora",
+    per_device_train_batch_size=2,
+    per_device_eval_batch_size=2,
+    num_train_epochs=10,
+    learning_rate=1e-4,
+    eval_strategy="epoch",
+    save_strategy="epoch",
+    logging_steps=10,
+    optim="adamw_torch",
+    load_best_model_at_end=True,
+    metric_for_best_model="eval_loss",
+    greater_is_better=False
+)
+
+trainer_lora = Trainer(
+    model=lora_model,
+    args=args_lora,
+    train_dataset=toy_tok["train"],
+    eval_dataset=toy_tok["test"],
+    data_collator=data_collator,
+)
+
+trainer_lora.train()
+lora_metrics = trainer_lora.evaluate()
+lora_metrics
+
+# Save the adapter weights
+lora_model.save_pretrained("./ft_gt_lora_adapter")