consolidate: Evo Unsloth LoRA training pipeline

evo/training_pipeline.py

@@ -0,0 +1,61 @@
+"""
+Agent Q3 [Evo] — Training Pipeline
+Unsloth LoRA fine-tuning on Llama-3.2-3B-Instruct.
+Reads from HF dataset madDegen/agent-q3, pushes adapter to madDegen/agent-q3-loras.
+"""
+import os
+from datasets import load_dataset
+from unsloth import FastLanguageModel
+from trl import SFTTrainer
+from transformers import TrainingArguments
+
+BASE_MODEL   = os.getenv("BASE_MODEL",  "unsloth/Llama-3.2-3B-Instruct-bnb-4bit")
+HF_DATASET   = os.getenv("HF_DATASET",  "madDegen/agent-q3")
+ADAPTER_REPO = os.getenv("ADAPTER_REPO","madDegen/agent-q3-loras")
+MAX_SEQ_LEN  = int(os.getenv("MAX_SEQ_LEN", 2048))
+LORA_RANK    = int(os.getenv("LORA_RANK", 16))
+
+def run():
+    model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name=BASE_MODEL,
+        max_seq_length=MAX_SEQ_LEN,
+        load_in_4bit=True,
+    )
+    model = FastLanguageModel.get_peft_model(
+        model,
+        r=LORA_RANK,
+        target_modules=["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"],
+        lora_alpha=16,
+        lora_dropout=0,
+        bias="none",
+        use_gradient_checkpointing="unsloth",
+    )
+
+    dataset = load_dataset(HF_DATASET, split="train")
+
+    trainer = SFTTrainer(
+        model=model,
+        tokenizer=tokenizer,
+        train_dataset=dataset,
+        dataset_text_field="text",
+        max_seq_length=MAX_SEQ_LEN,
+        args=TrainingArguments(
+            per_device_train_batch_size=2,
+            gradient_accumulation_steps=4,
+            warmup_steps=10,
+            max_steps=100,
+            learning_rate=2e-4,
+            fp16=True,
+            logging_steps=10,
+            output_dir="./evo_checkpoints",
+            optim="adamw_8bit",
+            seed=42,
+        ),
+    )
+    trainer.train()
+    model.push_to_hub(ADAPTER_REPO, token=os.getenv("HF_TOKEN"))
+    tokenizer.push_to_hub(ADAPTER_REPO, token=os.getenv("HF_TOKEN"))
+    print(f"Adapter pushed to {ADAPTER_REPO}")
+
+if __name__ == "__main__":
+    run()