初始化项目，由ModelHub XC社区提供模型

Model: zxc4wewewe/DarkGPT-model
Source: Original Platform

main.py

@@ -0,0 +1,129 @@
+import numpy as np
+import torch
+from datasets import load_dataset
+from transformers import (
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    TrainingArguments,
+    Trainer,
+    DataCollatorForLanguageModeling,
+)
+
+# ─── Configuration ───────────────────────────────────────────────────────────
+MODEL_NAME = "zxc4wewewe/blackthinking"                    # lightweight model suitable for CPU
+MAX_LENGTH = 512                       # max token length per example
+OUTPUT_DIR = "./results"
+NUM_EPOCHS = 3
+BATCH_SIZE = 2                         # small batch for CPU training
+LEARNING_RATE = 5e-5
+LOGGING_STEPS = 50
+
+# ─── 1. Load dataset from Hugging Face Hub ───────────────────────────────────
+dataset = load_dataset("zxc4wewewe/offsec")
+print(f"Train: {len(dataset['train'])} examples | Test: {len(dataset['test'])} examples")
+print(f"Columns: {dataset['train'].column_names}")
+
+
+# ─── 2. Format & tokenize ────────────────────────────────────────────────────
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+# GPT-2 has no pad token by default — use eos_token
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+
+def format_and_tokenize(examples):
+    """Combine prompt + response into a single text and tokenize."""
+    texts = [
+        f"{prompt}{response}{tokenizer.eos_token}"
+        for prompt, response in zip(examples["prompt"], examples["response"])
+    ]
+    tokenized = tokenizer(
+        texts,
+        truncation=True,
+        max_length=MAX_LENGTH,
+        padding="max_length",
+    )
+    # For causal LM, labels = input_ids (the model learns to predict next token)
+    tokenized["labels"] = tokenized["input_ids"].copy()
+    return tokenized
+
+
+tokenized_dataset = dataset.map(
+    format_and_tokenize,
+    batched=True,
+    remove_columns=dataset["train"].column_names,
+    desc="Tokenizing",
+)
+
+print(f"Tokenized train: {len(tokenized_dataset['train'])} examples")
+
+
+# ─── 3. Model ────────────────────────────────────────────────────────────────
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
+model.resize_token_embeddings(len(tokenizer))
+
+data_collator = DataCollatorForLanguageModeling(
+    tokenizer=tokenizer,
+    mlm=False,  # causal LM, not masked LM
+)
+
+
+# ─── 4. Training ─────────────────────────────────────────────────────────────
+training_args = TrainingArguments(
+    output_dir=OUTPUT_DIR,
+    overwrite_output_dir=True,
+    num_train_epochs=NUM_EPOCHS,
+    per_device_train_batch_size=BATCH_SIZE,
+    per_device_eval_batch_size=BATCH_SIZE,
+    eval_strategy="epoch",
+    save_strategy="epoch",
+    learning_rate=LEARNING_RATE,
+    weight_decay=0.01,
+    logging_dir="./logs",
+    logging_steps=LOGGING_STEPS,
+    load_best_model_at_end=True,
+    save_total_limit=2,
+    fp16=False,  # CPU-only
+    report_to="none",
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_dataset["train"],
+    eval_dataset=tokenized_dataset["test"],
+    data_collator=data_collator,
+)
+
+print("Starting training...")
+trainer.train()
+
+# Save final model
+trainer.save_model(f"{OUTPUT_DIR}/final_model")
+tokenizer.save_pretrained(f"{OUTPUT_DIR}/final_model")
+print(f"Model saved to {OUTPUT_DIR}/final_model")
+
+
+# ─── 5. Inference ────────────────────────────────────────────────────────────
+def generate_response(prompt_text, max_new_tokens=256):
+    """Generate a response given a prompt."""
+    inputs = tokenizer(prompt_text, return_tensors="pt")
+    with torch.no_grad():
+        output_ids = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9,
+            pad_token_id=tokenizer.eos_token_id,
+        )
+    # Decode only the generated part (skip the prompt tokens)
+    generated = output_ids[0][inputs["input_ids"].shape[1]:]
+    return tokenizer.decode(generated, skip_special_tokens=True)
+
+
+# Example usage (uncomment to test after training):
+sample_prompt = dataset["test"][0]["prompt"]
+print("Prompt:", sample_prompt[:200], "...")
+print("Generated:", generate_response(sample_prompt))