File size: 4,478 Bytes
553fbf7 | 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 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 | """
MINDI 1.5 Vision-Coder — Code Generation Model
Loads the base coding model with LoRA adapters for fine-tuning
on Next.js + Tailwind + TypeScript code generation.
"""
from __future__ import annotations
from pathlib import Path
from typing import Optional
import torch
from peft import LoraConfig, PeftModel, get_peft_model, TaskType
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
class MindiCodeModel:
"""Base coding model with LoRA for MINDI 1.5 fine-tuning."""
def __init__(
self,
model_name: str = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
device: Optional[str] = None,
cache_dir: Optional[Path] = None,
load_in_4bit: bool = False,
) -> None:
self.model_name = model_name
self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
self.cache_dir = cache_dir or Path("./checkpoints/base")
self.cache_dir.mkdir(parents=True, exist_ok=True)
self.load_in_4bit = load_in_4bit
self.model: Optional[AutoModelForCausalLM] = None
self.peft_model: Optional[PeftModel] = None
def load_base_model(self) -> AutoModelForCausalLM:
"""Load the base model with optional 4-bit quantization."""
quantization_config = None
if self.load_in_4bit:
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
)
self.model = AutoModelForCausalLM.from_pretrained(
self.model_name,
cache_dir=str(self.cache_dir),
torch_dtype=torch.bfloat16,
device_map="auto" if self.device == "cuda" else None,
quantization_config=quantization_config,
trust_remote_code=True,
)
return self.model
def apply_lora(
self,
rank: int = 64,
alpha: int = 128,
dropout: float = 0.05,
target_modules: Optional[list[str]] = None,
) -> PeftModel:
"""Apply LoRA adapters to the base model for efficient fine-tuning."""
if self.model is None:
raise RuntimeError("Base model not loaded. Call load_base_model() first.")
if target_modules is None:
target_modules = [
"q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",
]
lora_config = LoraConfig(
r=rank,
lora_alpha=alpha,
lora_dropout=dropout,
target_modules=target_modules,
bias="none",
task_type=TaskType.CAUSAL_LM,
)
self.peft_model = get_peft_model(self.model, lora_config)
trainable, total = self._count_parameters()
print(f"[MindiCodeModel] LoRA applied — trainable: {trainable:,} / {total:,} "
f"({100 * trainable / total:.2f}%)")
return self.peft_model
def _count_parameters(self) -> tuple[int, int]:
"""Count trainable and total parameters."""
model = self.peft_model or self.model
if model is None:
return 0, 0
trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
total = sum(p.numel() for p in model.parameters())
return trainable, total
def save_adapter(self, output_dir: Optional[Path] = None) -> Path:
"""Save the LoRA adapter weights."""
if self.peft_model is None:
raise RuntimeError("No LoRA adapter to save. Call apply_lora() first.")
save_path = output_dir or Path("./checkpoints/finetuned")
save_path.mkdir(parents=True, exist_ok=True)
self.peft_model.save_pretrained(str(save_path))
return save_path
def load_adapter(self, adapter_dir: Path) -> PeftModel:
"""Load a saved LoRA adapter onto the base model."""
if self.model is None:
self.load_base_model()
self.peft_model = PeftModel.from_pretrained(
self.model, str(adapter_dir)
)
return self.peft_model
def resize_embeddings(self, new_vocab_size: int) -> None:
"""Resize model embeddings to accommodate new special tokens."""
model = self.peft_model or self.model
if model is None:
raise RuntimeError("No model loaded.")
model.resize_token_embeddings(new_vocab_size)
|