Upload requirements_train.txt with huggingface_hub

requirements_train.txt

@@ -1,158 +1,9 @@
-#!/usr/bin/env python3
-"""
-Neo4j Expert Model Training Script
-
-Fine-tunes Qwen2.5-Coder-7B-Instruct using QLoRA for Neo4j/Cypher expertise.
-
-Usage:
-    python train.py
-
-Requires:
-    pip install -r requirements_train.txt
-"""
-
-import os
-import torch
-from dotenv import load_dotenv
-from datasets import load_dataset
-from transformers import (
-    AutoModelForCausalLM,
-    AutoTokenizer,
-    BitsAndBytesConfig,
-    TrainingArguments,
-)
-from peft import LoraConfig
-from trl import SFTTrainer
-
-load_dotenv()
-
-# === CONFIGURATION - NEO4J EXPERT MODEL ===
-
-# Base model to fine-tune
-MODEL_NAME = "Qwen/Qwen2.5-Coder-7B-Instruct"
-
-# Dataset - loaded from environment
-DATASET_NAME = os.getenv("HF_DATASET_NAME", "your-username/neo4j-dataset")
-
-# Output directory for the adapter
-OUTPUT_DIR = "neo4j-cypher-expert"
-
-# Hugging Face Hub settings - loaded from environment
-HF_USERNAME = os.getenv("HF_USERNAME", "your-username")
-
-
-def main():
-    print("=" * 50)
-    print("Neo4j Expert Model Training")
-    print("=" * 50)
-    
-    # Load dataset
-    print(f"\nLoading dataset from {DATASET_NAME}...")
-    dataset = load_dataset(DATASET_NAME, split="train")
-    print(f"Dataset size: {len(dataset)} examples")
-    
-    # 4-bit Quantization config for memory efficiency
-    bnb_config = BitsAndBytesConfig(
-        load_in_4bit=True,
-        bnb_4bit_quant_type="nf4",
-        bnb_4bit_compute_dtype=torch.float16,
-        bnb_4bit_use_double_quant=True,
-    )
-    
-    print(f"\nLoading base model: {MODEL_NAME}...")
-    model = AutoModelForCausalLM.from_pretrained(
-        MODEL_NAME,
-        quantization_config=bnb_config,
-        device_map="auto",
-        trust_remote_code=True,
-    )
-    
-    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
-    tokenizer.pad_token = tokenizer.eos_token
-    tokenizer.padding_side = "right"
-    
-    # LoRA Configuration - Full coverage as specified
-    peft_config = LoraConfig(
-        lora_alpha=16,
-        lora_dropout=0.1,
-        r=64,  # Rank
-        bias="none",
-        task_type="CAUSAL_LM",
-        # Full target modules for comprehensive fine-tuning
-        target_modules=[
-            "q_proj",
-            "k_proj", 
-            "v_proj",
-            "o_proj",
-            "gate_proj",
-            "up_proj",
-            "down_proj",
-        ],
-    )
-    
-    # Format chat messages using tokenizer's template
-    def formatting_prompts_func(examples):
-        output_texts = []
-        for messages in examples['messages']:
-            text = tokenizer.apply_chat_template(
-                messages,
-                tokenize=False,
-                add_generation_prompt=False
-            )
-            output_texts.append(text)
-        return output_texts
-    
-    # Training Arguments
-    training_args = TrainingArguments(
-        output_dir=OUTPUT_DIR,
-        per_device_train_batch_size=1,
-        gradient_accumulation_steps=8,
-        learning_rate=2e-4,
-        logging_steps=10,
-        num_train_epochs=1,
-        optim="paged_adamw_32bit",
-        fp16=True,
-        group_by_length=True,
-        gradient_checkpointing=True,
-        save_strategy="epoch",
-        report_to="none",
-        warmup_ratio=0.03,
-        lr_scheduler_type="cosine",
-        # Push to Hugging Face Hub
-        push_to_hub=True,
-        hub_model_id=f"{HF_USERNAME}/{OUTPUT_DIR}",
-    )
-    
-    # Initialize trainer
-    trainer = SFTTrainer(
-        model=model,
-        train_dataset=dataset,
-        peft_config=peft_config,
-        formatting_func=formatting_prompts_func,
-        max_seq_length=1024,
-        tokenizer=tokenizer,
-        args=training_args,
-    )
-    
-    print("\nStarting training...")
-    print(f"  Base model: {MODEL_NAME}")
-    print(f"  Dataset: {DATASET_NAME}")
-    print(f"  Output: {OUTPUT_DIR}")
-    print(f"  LoRA rank: {peft_config.r}")
-    print(f"  Target modules: {peft_config.target_modules}")
-    
-    trainer.train()
-    
-    # Save the adapter
-    trainer.save_model(OUTPUT_DIR)
-    print(f"\nTraining complete! Adapter saved to {OUTPUT_DIR}")
-    
-    # Push to Hub
-    print(f"Pushing to Hugging Face Hub: {HF_USERNAME}/{OUTPUT_DIR}")
-    trainer.push_to_hub()
-    
-    print("\nDone!")
-
-
-if __name__ == "__main__":
-    main()
+# Requirements for model training
+torch
+transformers
+datasets
+peft
+trl
+bitsandbytes
+accelerate
+huggingface_hub