cognitive-trainer-scripts / train_pattern_selector.py

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# /// script
# dependencies = ["trl>=0.20.0", "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

# 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)}")

# Model - using Qwen2.5-7B for pattern selection
model_id = "Qwen/Qwen2.5-7B-Instruct"
print(f"Using model: {model_id}")

# 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 - modern TRL API
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",
    max_length=4096,  # Use max_length, not max_seq_length
)

print("Starting training...")
trainer = SFTTrainer(
    model=model_id,  # Pass model name, not loaded model
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=peft_config,
    args=training_args,
)

trainer.train()
print("Training complete! Pushing to Hub...")
trainer.push_to_hub()
print("Done!")

	# /// script
	# dependencies = ["trl>=0.20.0", "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

	# 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)}")

	# Model - using Qwen2.5-7B for pattern selection
	model_id = "Qwen/Qwen2.5-7B-Instruct"
	print(f"Using model: {model_id}")

	# 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 - modern TRL API
	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",
	max_length=4096, # Use max_length, not max_seq_length
	)

	print("Starting training...")
	trainer = SFTTrainer(
	model=model_id, # Pass model name, not loaded model
	train_dataset=train_dataset,
	eval_dataset=eval_dataset,
	peft_config=peft_config,
	args=training_args,
	)

	trainer.train()
	print("Training complete! Pushing to Hub...")
	trainer.push_to_hub()
	print("Done!")