File size: 1,699 Bytes
a993640 3bffe2a a993640 3bffe2a a993640 3bffe2a a993640 3bffe2a a993640 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | # /// script
# requires-python = ">=3.10"
# dependencies = [
# "trl>=0.12.0",
# "peft>=0.7.0",
# "transformers>=4.36.0",
# "accelerate>=0.24.0",
# "trackio",
# "datasets",
# ]
# ///
import os
os.environ["HF_HUB_DISABLE_XET"] = "1"
import trackio
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
print("Loading memory-agent-sft-v1 dataset...")
dataset = load_dataset("erik1988/memory-agent-sft-v1", split="train")
print(f"Dataset loaded: {len(dataset)} examples")
print(f"Train: {len(dataset)} examples (no eval split to save GPU memory)")
config = SFTConfig(
output_dir="elias-memory-agent-v2",
push_to_hub=True,
hub_model_id="erik1988/elias-memory-agent-v2",
hub_strategy="every_save",
num_train_epochs=3,
per_device_train_batch_size=1,
gradient_accumulation_steps=8,
learning_rate=2e-5,
fp16=True,
max_length=512,
logging_steps=5,
save_strategy="steps",
save_steps=50,
save_total_limit=2,
warmup_ratio=0.1,
lr_scheduler_type="cosine",
gradient_checkpointing=True,
report_to="trackio",
project="elias-identity",
run_name="memory-agent-sft-v2-retry",
)
peft_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
target_modules=["q_proj", "v_proj"],
)
print("Initializing trainer with Qwen2.5-3B...")
trainer = SFTTrainer(
model="Qwen/Qwen2.5-3B",
train_dataset=dataset,
args=config,
peft_config=peft_config,
)
print("Starting training...")
trainer.train()
print("Pushing to Hub...")
trainer.push_to_hub()
trackio.finish()
print("DONE: Memory Agent v2")
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