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# /// script
# requires-python = ">=3.10"
# dependencies = [
# "trl>=0.12.0",
# "peft>=0.7.0",
# "transformers>=4.36.0",
# "accelerate>=0.24.0",
# "trackio",
# "datasets",
# ]
# ///
"""
Agent Zero SFT v2: LiquidAI/LFM2.5-1.2B-Instruct
LoRA fine-tuning on mixed agent-zero-sft-v2 dataset.
Changes from v1:
- Mixed dataset: 40% agent + 40% math (MetaMathQA) + 20% general (OpenHermes)
- LoRA r=8 (was 16), alpha=16 (was 32) — reduced rank to prevent overfitting
- 2 epochs (was 3)
- lr=1e-4 (was 2e-4) — gentler updates
"""
import os
import trackio
from datasets import load_dataset
from huggingface_hub import login
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
token = os.getenv("HF_TOKEN")
if token:
login(token=token)
# Load v2 mixed dataset
print("Loading dataset...")
train_ds = load_dataset("wheattoast11/agent-zero-sft-v2", split="train")
val_ds = load_dataset("wheattoast11/agent-zero-sft-v2", split="validation")
print(f"Train: {len(train_ds)}, Val: {len(val_ds)}")
config = SFTConfig(
output_dir="agent-zero-lfm-1.2b-v2",
push_to_hub=True,
hub_model_id="wheattoast11/agent-zero-lfm-1.2b-v2",
hub_strategy="every_save",
hub_private_repo=True,
# v2: 2 epochs (was 3)
num_train_epochs=2,
per_device_train_batch_size=4,
gradient_accumulation_steps=4, # effective batch size 16
# v2: lr=1e-4 (was 2e-4)
learning_rate=1e-4,
bf16=True,
logging_steps=10,
save_strategy="steps",
save_steps=200,
save_total_limit=2,
eval_strategy="steps",
eval_steps=200,
warmup_ratio=0.1,
lr_scheduler_type="cosine",
report_to="trackio",
project="agent-zero-finetune",
run_name="lfm-1.2b-sft-v2",
)
# v2: r=8 (was 16), alpha=16 (was 32), ratio stays 2.0
peft_config = LoraConfig(
r=8,
lora_alpha=16,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
)
print("Initializing trainer...")
trainer = SFTTrainer(
model="LiquidAI/LFM2.5-1.2B-Instruct",
train_dataset=train_ds,
eval_dataset=val_ds,
args=config,
peft_config=peft_config,
)
print("Starting training...")
trainer.train()
print("Pushing to Hub...")
trainer.push_to_hub()
trackio.finish()
print("Done! Model at: https://huggingface.co/wheattoast11/agent-zero-lfm-1.2b-v2")
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