Upload train_survival_32b.py with huggingface_hub

train_survival_32b.py

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+# /// script
+# dependencies = ["trl", "peft", "bitsandbytes", "datasets", "transformers"]
+# ///
+
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+import os
+
+# Configuration
+MODEL_ID = "Qwen/Qwen2.5-32B-Instruct"
+DATASET_ID = "sunkencity/survival-instruct"
+OUTPUT_MODEL_ID = "sunkencity/survival-expert-qwen-32b"
+
+# Load Dataset
+dataset = load_dataset(DATASET_ID, split="train")
+
+# SANITIZE DATASET
+def filter_empty(example):
+    return (
+        example["instruction"] is not None 
+        and example["response"] is not None
+        and len(example["instruction"].strip()) > 0
+        and len(example["response"].strip()) > 0
+    )
+
+dataset = dataset.filter(filter_empty)
+
+# Load Model
+# 4-bit quantization is essential for 32B on single A100 if we want decent batch size
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16, # Using bfloat16 for A100
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    quantization_config=bnb_config,
+    device_map="auto",
+    use_cache=False,
+    torch_dtype=torch.bfloat16
+)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+tokenizer.pad_token = tokenizer.eos_token
+
+# MANUAL FORMATTING
+def format_row(example):
+    instruction = example['instruction']
+    response = example['response']
+    # Qwen Chat Template
+    # <|im_start|>user
+    # {instruction}<|im_end|>
+    # <|im_start|>assistant
+    # {response}<|im_end|>
+    text = f"<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n{response}<|im_end|>{tokenizer.eos_token}"
+    return {"text": text}
+
+dataset = dataset.map(format_row)
+
+# LoRA
+peft_config = LoraConfig(
+    r=32, # Increased rank for larger model capability
+    lora_alpha=64,
+    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"]
+)
+
+# Args
+training_args = SFTConfig(
+    output_dir="./results",
+    num_train_epochs=3,
+    per_device_train_batch_size=4, # A100 has 80GB, we can afford larger batches
+    gradient_accumulation_steps=4,
+    learning_rate=1e-4,
+    logging_steps=5,
+    push_to_hub=True,
+    hub_model_id=OUTPUT_MODEL_ID,
+    fp16=False,
+    bf16=True, # Enable BF16 for A100
+    packing=False,
+    max_length=2048, # Increased context length for 32B
+    dataset_text_field="text"
+)
+
+# Trainer
+trainer = SFTTrainer(
+    model=model,
+    train_dataset=dataset,
+    peft_config=peft_config,
+    args=training_args,
+    processing_class=tokenizer,
+)
+
+print("Starting training...")
+trainer.train()
+
+print("Pushing to hub...")
+trainer.push_to_hub()
+print("Done!")