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# /// script
# dependencies = ["trl>=0.12.0", "peft>=0.13.0", "datasets", "transformers>=4.45.0", "accelerate", "bitsandbytes", "huggingface_hub"]
# ///
import os
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch
# Authenticate
from huggingface_hub import login
hf_token = os.environ.get("HF_TOKEN")
if hf_token:
login(token=hf_token)
print("Authenticated with HuggingFace")
print("Loading dataset...")
dataset = load_dataset("KevinKeller/cognitive-pattern-selector-v1")
train_dataset = dataset["train"]
eval_dataset = dataset.get("validation")
print(f"Train samples: {len(train_dataset)}")
if eval_dataset:
print(f"Eval samples: {len(eval_dataset)}")
print("Loading model: Qwen/Qwen2.5-7B-Instruct...")
model_id = "Qwen/Qwen2.5-7B-Instruct"
# 4-bit quantization for fitting on A10G
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
)
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(
model_id,
quantization_config=bnb_config,
device_map="auto",
trust_remote_code=True,
)
# LoRA config
peft_config = LoraConfig(
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",
task_type="CAUSAL_LM",
)
# Training config - removed max_seq_length from SFTConfig
training_args = SFTConfig(
output_dir="./pattern-selector-output",
num_train_epochs=3,
per_device_train_batch_size=2,
gradient_accumulation_steps=4,
learning_rate=2e-4,
logging_steps=10,
save_strategy="epoch",
eval_strategy="epoch" if eval_dataset else "no",
bf16=True,
push_to_hub=True,
hub_model_id="KevinKeller/cognitive-pattern-selector-qwen2.5-7b",
report_to="none",
)
print("Starting training...")
trainer = SFTTrainer(
model=model,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
peft_config=peft_config,
tokenizer=tokenizer,
args=training_args,
max_seq_length=4096, # Moved here
)
trainer.train()
print("Training complete! Pushing to Hub...")
trainer.push_to_hub()
print("Done!")
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