# LoRA config (proven in DriftCall)
LORA_R = 16
LORA_ALPHA = 32
LORA_DROPOUT = 0.05

BASE_MODEL = "unsloth/Qwen2.5-Coder-1.5B-Instruct"

def load_model(max_seq_len=2048):
    from unsloth import FastLanguageModel
    model, tokenizer = FastLanguageModel.from_pretrained(
        model_name=BASE_MODEL,
        max_seq_length=max_seq_len,
        load_in_4bit=False,       # Standard unsloth behavior, space uses fp16/bf16
        dtype="bfloat16",
    )
    model = FastLanguageModel.get_peft_model(
        model, r=LORA_R, lora_alpha=LORA_ALPHA,
        lora_dropout=LORA_DROPOUT,
        target_modules=["q_proj","k_proj","v_proj","o_proj",
                        "gate_proj","up_proj","down_proj"],
        bias="none",
        use_gradient_checkpointing="unsloth",
    )
    return model, tokenizer