Upload train_aviation.py with huggingface_hub

train_aviation.py

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+# /// script
+# dependencies = [
+#     "trl>=0.12.0",
+#     "peft>=0.7.0",
+#     "transformers>=4.36.0",
+#     "accelerate>=0.24.0",
+#     "trackio",
+#     "bitsandbytes",
+#     "scipy",
+#     "flash-attn"
+# ]
+# ///
+
+import trackio
+import torch
+from datasets import load_dataset
+from peft import LoraConfig, prepare_model_for_kbit_training, get_peft_model
+from trl import SFTTrainer, SFTConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+
+# Model ID
+model_id = "mistralai/Mistral-3-14B-Reasoning-2512"
+
+# Load dataset
+print("📦 Loading dataset...")
+dataset = load_dataset("sakharamg/AviationQA", split="train")
+
+# Limit dataset size for reasonable training time (e.g., 10k examples)
+# 1M rows is too large for a single generic fine-tuning job without massive compute.
+print("✂️ Subsampling dataset to 10,000 examples for efficiency...")
+dataset = dataset.shuffle(seed=42).select(range(10000))
+
+# Map to chat format
+print("🔄 Mapping dataset...")
+def to_messages(example):
+    return {
+        "messages": [
+            {"role": "user", "content": example["Question"]},
+            {"role": "assistant", "content": example["Answer"]}
+        ]
+    }
+dataset = dataset.map(to_messages, remove_columns=dataset.column_names)
+
+# Split
+print("🔀 Creating train/eval split...")
+dataset_split = dataset.train_test_split(test_size=0.1, seed=42)
+train_dataset = dataset_split["train"]
+eval_dataset = dataset_split["test"]
+
+# Quantization Config (4-bit for memory efficiency)
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+)
+
+# Load Model
+print(f"🤖 Loading model {model_id}...")
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    quantization_config=bnb_config,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2"
+)
+model = prepare_model_for_kbit_training(model)
+
+# Tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+tokenizer.pad_token = tokenizer.eos_token
+# Fix for some models that miss chat_template or padding
+if tokenizer.chat_template is None:
+    tokenizer.chat_template = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
+
+# LoRA Config
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM",
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+)
+
+# Training Config
+config = SFTConfig(
+    output_dir="Mistral-3-14B-AviationQA-SFT",
+    push_to_hub=True,
+    hub_model_id="sunkencity/Mistral-3-14B-AviationQA-SFT",
+    hub_strategy="every_save",
+    num_train_epochs=1,
+    per_device_train_batch_size=4,
+    gradient_accumulation_steps=4,
+    learning_rate=2e-4,
+    fp16=False,
+    bf16=True,
+    logging_steps=10,
+    save_strategy="steps",
+    save_steps=100,
+    eval_strategy="steps",
+    eval_steps=100,
+    report_to="trackio",
+    project="aviation-qa-tuning",
+    run_name="mistral-14b-sft-v1",
+    max_seq_length=2048,
+    dataset_kwargs={"add_special_tokens": False} # Let tokenizer handle chat template
+)
+
+# Trainer
+trainer = SFTTrainer(
+    model=model,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    args=config,
+    peft_config=peft_config,
+    tokenizer=tokenizer,
+)
+
+print("🚀 Starting training...")
+trainer.train()
+
+print("💾 Pushing to Hub...")
+trainer.push_to_hub()