docs: update draft for "Finetune" article

Author: lzwjava

_drafts/2025-01-28-finetune-en.md

@@ -0,0 +1,81 @@
+---
+audio: true
+lang: en
+layout: post
+title: Finetune a model
+translated: false
+---
+
+```python
+import os
+import glob
+import json
+from dotenv import load_dotenv
+from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, DataCollatorForLanguageModeling, LlamaTokenizerFast
+from datasets import Dataset, load_dataset
+import torch
+
+load_dotenv()
+
+MODEL_NAME = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"  # Changed to the specified model
+OUTPUT_DIR = "trained_model"
+TRAIN_FILE = "train.jsonl"
+MAX_LENGTH = 512
+BATCH_SIZE = 8
+EPOCHS = 3
+
+def create_training_data(posts_dir):
+    all_texts = []
+    for lang_dir in os.listdir(posts_dir):
+        lang_path = os.path.join(posts_dir, lang_dir)
+        if not os.path.isdir(lang_path):
+            continue
+        for file_path in glob.glob(os.path.join(lang_path, "*.md")):
+            try:
+                with open(file_path, 'r', encoding='utf-8') as f:
+                    content = f.read()
+                    # Remove front matter
+                    content = content.split("---", 2)[-1].strip()
+                    all_texts.append(content)
+            except Exception as e:
+                print(f"Error reading file {file_path}: {e}")
+    return all_texts
+
+def prepare_dataset(texts, tokenizer):
+    encodings = tokenizer(texts, truncation=True, padding=True, max_length=MAX_LENGTH, return_tensors="pt")
+    return Dataset.from_dict(encodings)
+
+def train_model(dataset, tokenizer):
+    training_args = TrainingArguments(
+        output_dir=OUTPUT_DIR,
+        overwrite_output_dir=True,
+        num_train_epochs=EPOCHS,
+        per_device_train_batch_size=BATCH_SIZE,
+        save_steps=10_000,
+        save_total_limit=2,
+        prediction_loss_only=True,
+        remove_unused_columns=False,
+    )
+    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, trust_remote_code=True)
+    data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=dataset,
+        data_collator=data_collator,
+    )
+    trainer.train()
+    trainer.save_model(OUTPUT_DIR)
+
+def main():
+    posts_dir = "_posts"
+    texts = create_training_data(posts_dir)
+    tokenizer = LlamaTokenizerFast.from_pretrained(MODEL_NAME, trust_remote_code=True, use_fast=True)
+    tokenizer.pad_token = tokenizer.eos_token
+    dataset = prepare_dataset(texts, tokenizer)
+    train_model(dataset, tokenizer)
+
+if __name__ == "__main__":
+    main()
+
+```