Add deepseek version, full trained version. Add results

finemodel/README.md

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+# Results
+
+| Model/Technique                       | Coherence     | Plausibility  | Disinformation?   |
+|---------------------------------------|---------------|---------------|-------------------|
+| distilGPT2 + LoRa                     | 6/9           | 4/9           | 2/9
+| miniLLama + LoRa                      | 7/9           | 6/9           | 4/9
+| deepseek + LoRa                       | 7/9           | 5/9           | 5/9
+| distilGPT2 (full training)            | 4/9           | 3/9           | 2/9

finemodel/full.py

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+import torch
+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()
+]
+
+model_name = "distilgpt2"
+tok_gpt = AutoTokenizer.from_pretrained(model_name)
+
+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}
+
+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"])
+toy_tok = toy_tok.map(lambda examples: {"labels": examples["input_ids"]})
+toy_tok.set_format(type="torch")
+
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+model = AutoModelForCausalLM.from_pretrained(model_name).to(DEVICE)
+
+args = TrainingArguments(
+    output_dir="./ft_gt_full",
+    per_device_train_batch_size=4,
+    per_device_eval_batch_size=4,
+    gradient_accumulation_steps=1,
+    num_train_epochs=5,
+    learning_rate=5e-5,
+    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,
+    fp16=torch.cuda.is_available(),
+)
+
+trainer = Trainer(
+    model=model,
+    args=args,
+    train_dataset=toy_tok["train"],
+    eval_dataset=toy_tok["test"],
+    data_collator=data_collator,
+)
+
+trainer.train()
+
+metrics = trainer.evaluate()
+print(metrics)
+
+trainer.save_model("./ft_gt_full")
+tok_gpt.save_pretrained("./ft_gt_full")

finemodel/lora23.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("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B")
+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("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", **quant_kwargs)
+
+
+lora_cfg = LoraConfig(
+    task_type=TaskType.CAUSAL_LM,
+    r=8,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    target_modules=[
+        "q_proj",
+        "k_proj",
+        "v_proj",
+        "o_proj"
+    ]
+)
+
+lora_model = get_peft_model(base_lm, lora_cfg)
+
+args_lora = TrainingArguments(
+    output_dir="./ft_ds_lora",
+    per_device_train_batch_size=1,
+    per_device_eval_batch_size=1,
+    num_train_epochs=5,
+    learning_rate=2e-5,
+    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_ds_lora_adapter")

finemodel/q_full.py

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+import torch
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# -----------------------------
+# Config
+# -----------------------------
+MODEL_PATH = "./ft_gt_full"   # your saved FT model
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+app = FastAPI(title="DistilGPT2 FT API")
+
+# -----------------------------
+# Request schema
+# -----------------------------
+class EventRequest(BaseModel):
+    event: str
+    max_new_tokens: int = 80
+
+
+# -----------------------------
+# Load tokenizer + model
+# -----------------------------
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+tokenizer.pad_token = tokenizer.eos_token
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_PATH,
+    torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32
+)
+
+model.to(DEVICE)
+model.eval()
+
+
+# -----------------------------
+# Prompt builder
+# -----------------------------
+def build_prompt(instruction, inp):
+    return (
+        f"### Instruction:\n{instruction}\n\n"
+        f"### Input:\n{inp}\n\n"
+        f"### Response:\n"
+    )
+
+
+# -----------------------------
+# Generate function
+# -----------------------------
+@torch.no_grad()
+def generate(prompt, max_new_tokens=80):
+    inputs = tokenizer(prompt, return_tensors="pt").to(DEVICE)
+
+    output = model.generate(
+        **inputs,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        temperature=0.8,
+        top_p=0.9,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+    text = tokenizer.decode(output[0], skip_special_tokens=True)
+
+    # Extract only response part
+    return text.split("### Response:")[-1].strip()
+
+
+# -----------------------------
+# API Endpoint
+# -----------------------------
+@app.post("/compare")
+def generate_claim(req: EventRequest):
+    instruction = "create a disinformation claim based on the real world event"
+    prompt = build_prompt(instruction, req.event)
+
+    output = generate(prompt, req.max_new_tokens)
+
+    return {
+        "input_event": req.event,
+        "base_output": "N/A",
+        "lora_output": output
+    }

finemodel/q_lora23.py

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+import torch
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+# -----------------------------
+# Config
+# -----------------------------
+BASE_MODEL_NAME = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
+ADAPTER_PATH = "./ft_ds_lora_adapter"
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+app = FastAPI(title="Base vs LoRA API")
+
+# -----------------------------
+# Request schema
+# -----------------------------
+class EventRequest(BaseModel):
+    event: str
+    max_new_tokens: int = 80
+
+
+# -----------------------------
+# Load tokenizer
+# -----------------------------
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_NAME)
+tokenizer.pad_token = tokenizer.eos_token
+
+
+# -----------------------------
+# Load BASE model
+# -----------------------------
+# base_model = AutoModelForCausalLM.from_pretrained(
+#     BASE_MODEL_NAME,
+#     torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32
+# )
+# base_model.to(DEVICE)
+# base_model.eval()
+
+
+# -----------------------------
+# Load LoRA model
+# -----------------------------
+lora_base = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL_NAME,
+    torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32
+)
+
+lora_model = PeftModel.from_pretrained(lora_base, ADAPTER_PATH)
+lora_model.to(DEVICE)
+lora_model.eval()
+
+
+# -----------------------------
+# Prompt builder
+# -----------------------------
+def build_prompt(instruction, inp):
+    return (
+        f"### Instruction:\n{instruction}\n\n"
+        f"### Input:\n{inp}\n\n"
+        f"### Response:\n"
+    )
+
+
+# -----------------------------
+# Generate function
+# -----------------------------
+@torch.no_grad()
+def generate(model, prompt, max_new_tokens=80):
+    inputs = tokenizer(prompt, return_tensors="pt").to(DEVICE)
+
+    output = model.generate(
+        **inputs,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        temperature=0.8,
+        top_p=0.9,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+    text = tokenizer.decode(output[0], skip_special_tokens=True)
+    return text.split("### Response:")[-1].strip()
+
+
+# -----------------------------
+# API Endpoint
+# -----------------------------
+@app.post("/compare")
+def compare(req: EventRequest):
+    instruction = "create a disinformation claim based on the real world event"
+    prompt = build_prompt(instruction, req.event)
+
+    # base_out = generate(base_model, prompt, req.max_new_tokens)
+    lora_out = generate(lora_model, prompt, req.max_new_tokens)
+
+    return {
+        "input_event": req.event,
+        "base_output": "NONE",
+        "lora_output": lora_out
+    }