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train_unsloth.py

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+#!/usr/bin/env python3
+"""
+Harbour Fine-tuning Script for Qwen3.6-35B-A3B (MoE)
+Uses Unsloth + LoRA with GGUF quantized model
+Optimized for CPU with 121GB RAM
+"""
+
+import json
+import torch
+from pathlib import Path
+from datasets import Dataset
+from unsloth import FastLanguageModel
+from trl import SFTTrainer
+from transformers import TrainingArguments
+
+# Configuration
+MODEL_NAME = "unsloth/Qwen3.6-35B-A3B-GGUF"
+MODEL_FILE = "Qwen3.6-35B-A3B-UD-Q4_K_M.gguf"
+TRAIN_FILE = Path("/home/fivetech/finetune/harbour_train.jsonl")
+VAL_FILE = Path("/home/fivetech/finetune/harbour_val.jsonl")
+OUTPUT_DIR = Path("/home/fivetech/finetune/output")
+MAX_SEQ_LENGTH = 2048
+
+print("=" * 60)
+print("Harbour Fine-tuning - Qwen3.6-35B-A3B (MoE) with Unsloth + LoRA")
+print("=" * 60)
+
+# 1. Load model from GGUF
+print("\n1. Loading model from GGUF (Q4_K_M)...")
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=MODEL_NAME,
+    gguf_file=MODEL_FILE,
+    max_seq_length=MAX_SEQ_LENGTH,
+    load_in_4bit=True,
+    dtype=None,
+)
+
+# 2. LoRA configuration
+print("2. Configuring LoRA...")
+model = FastLanguageModel.get_peft_model(
+    model,
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
+                     "gate_proj", "up_proj", "down_proj"],
+    bias="none",
+    use_gradient_checkpointing="unsloth",
+    random_state=42,
+)
+
+# 3. Load dataset
+print("3. Loading dataset...")
+
+def load_jsonl(path):
+    data = []
+    with open(path) as f:
+        for line in f:
+            data.append(json.loads(line))
+    return data
+
+train_data = load_jsonl(TRAIN_FILE)
+val_data = load_jsonl(VAL_FILE)
+
+print(f"   Train: {len(train_data)} entries")
+print(f"   Val: {len(val_data)} entries")
+
+# 4. Format conversations
+print("4. Formatting conversations...")
+
+def format_conversation(entry):
+    messages = entry["messages"]
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=False,
+    )
+    return {"text": text}
+
+train_dataset = Dataset.from_list([format_conversation(e) for e in train_data])
+val_dataset = Dataset.from_list([format_conversation(e) for e in val_data])
+
+# 5. Tokenize
+print("5. Tokenizing...")
+
+def tokenize_function(examples):
+    return tokenizer(
+        examples["text"],
+        truncation=True,
+        max_length=MAX_SEQ_LENGTH,
+        padding=False,
+    )
+
+train_dataset = train_dataset.map(
+    tokenize_function,
+    batched=True,
+    remove_columns=["text"],
+    desc="Tokenizing train",
+)
+val_dataset = val_dataset.map(
+    tokenize_function,
+    batched=True,
+    remove_columns=["text"],
+    desc="Tokenizing val",
+)
+
+print(f"   Train tokens: {sum(len(x) for x in train_dataset['input_ids']):,}")
+print(f"   Val tokens: {sum(len(x) for x in val_dataset['input_ids']):,}")
+
+# 6. Training arguments
+print("6. Setting up training...")
+training_args = TrainingArguments(
+    output_dir=str(OUTPUT_DIR),
+    num_train_epochs=3,
+    per_device_train_batch_size=1,
+    gradient_accumulation_steps=16,
+    learning_rate=1e-4,
+    weight_decay=0.01,
+    warmup_ratio=0.1,
+    lr_scheduler_type="cosine",
+    logging_steps=5,
+    save_steps=50,
+    save_total_limit=3,
+    eval_strategy="steps",
+    eval_steps=50,
+    load_best_model_at_end=True,
+    metric_for_best_model="eval_loss",
+    bf16=False,
+    fp16=False,
+    dataloader_num_workers=1,
+    report_to="none",
+    remove_unused_columns=False,
+    max_grad_norm=1.0,
+    optim="adamw_8bit",
+)
+
+# 7. Create trainer
+print("7. Creating trainer...")
+trainer = SFTTrainer(
+    model=model,
+    tokenizer=tokenizer,
+    args=training_args,
+    train_dataset=train_dataset,
+    eval_dataset=val_dataset,
+    max_seq_length=MAX_SEQ_LENGTH,
+    dataset_text_field="text",
+)
+
+# 8. Train
+print("\n8. Starting training...")
+print("=" * 60)
+trainer.train()
+
+# 9. Save LoRA adapter
+print("\n9. Saving LoRA adapter...")
+trainer.save_model(str(OUTPUT_DIR / "final"))
+tokenizer.save_pretrained(str(OUTPUT_DIR / "final"))
+
+# 10. Export to GGUF (optional)
+print("\n10. Exporting to GGUF...")
+model.save_pretrained_gguf(
+    str(OUTPUT_DIR / "gguf"),
+    tokenizer,
+    quantization_method="q4_k_m",
+)
+
+print("\n" + "=" * 60)
+print("Training complete!")
+print(f"LoRA adapter saved to: {OUTPUT_DIR / 'final'}")
+print(f"GGUF model saved to: {OUTPUT_DIR / 'gguf'}")
+print("=" * 60)