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fix: add utils/models directory that was ignored by gitignore

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Roland Tannous 2026-02-02 19:52:34 +00:00
commit c07b81c083
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24
studio/backend/utils/models/__init__.py Normal file
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"""
Model and LoRA configuration handling
"""
from .model_config import (
ModelConfig,
is_vision_model,
scan_trained_loras,
load_model_defaults,
get_base_model_from_lora,
load_model_config,
MODEL_NAME_MAPPING,
UI_STATUS_INDICATORS,
)
__all__ = [
'ModelConfig',
'is_vision_model',
'scan_trained_loras',
'load_model_defaults',
'get_base_model_from_lora',
'load_model_config',
'MODEL_NAME_MAPPING',
'UI_STATUS_INDICATORS',
]

704
studio/backend/utils/models/model_config.py Normal file
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"""
Model and LoRA configuration handling
"""
from transformers import AutoConfig
from dataclasses import dataclass
from typing import Optional, Dict, Any
from utils.paths import normalize_path, is_local_path, is_model_cached
from utils.utils import without_hf_auth
import logging
from pathlib import Path
from typing import List, Tuple
import json
import yaml
logger = logging.getLogger(__name__)
# Model name mapping: maps all equivalent model names to their canonical YAML config file
# Format: "canonical_model_name.yaml": [list of all equivalent model names]
# Based on the model mapper provided - canonical filename is based on the first model name in the mapper
MODEL_NAME_MAPPING = {
"unsloth_answerdotai_ModernBERT-large.yaml": [
"answerdotai/ModernBERT-large",
],
"unsloth_Qwen2.5-Coder-7B-Instruct-bnb-4bit.yaml": [
"unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit",
"unsloth/Qwen2.5-Coder-7B-Instruct",
"Qwen/Qwen2.5-Coder-7B-Instruct",
],
"unsloth_codegemma-7b-bnb-4bit.yaml": [
"unsloth/codegemma-7b-bnb-4bit",
"unsloth/codegemma-7b",
"google/codegemma-7b",
],
"unsloth_ERNIE-4.5-21B-A3B-PT.yaml": [
"unsloth/ERNIE-4.5-21B-A3B-PT",
],
"unsloth_ERNIE-4.5-VL-28B-A3B-PT.yaml": [
"unsloth/ERNIE-4.5-VL-28B-A3B-PT",
],
"tiiuae_Falcon-H1-0.5B-Instruct.yaml": [
"tiiuae/Falcon-H1-0.5B-Instruct",
"unsloth/Falcon-H1-0.5B-Instruct",
],
"unsloth_functiongemma-270m-it.yaml": [
"unsloth/functiongemma-270m-it-unsloth-bnb-4bit",
"google/functiongemma-270m-it",
"unsloth/functiongemma-270m-it-unsloth-bnb-4bit",
],
"unsloth_gemma-2-2b.yaml": [
"unsloth/gemma-2-2b-bnb-4bit",
"google/gemma-2-2b",
],
"unsloth_gemma-2-27b-bnb-4bit.yaml": [
"unsloth/gemma-2-9b-bnb-4bit",
"unsloth/gemma-2-9b",
"google/gemma-2-9b",
"unsloth/gemma-2-27b",
"google/gemma-2-27b",
],
"unsloth_gemma-3-4b-pt.yaml": [
"unsloth/gemma-3-4b-pt-unsloth-bnb-4bit",
"google/gemma-3-4b-pt",
"unsloth/gemma-3-4b-pt-bnb-4bit",
],
"unsloth_gemma-3-4b-it.yaml": [
"unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"google/gemma-3-4b-it",
"unsloth/gemma-3-4b-it-bnb-4bit",
],
"unsloth_gemma-3-27b-it.yaml": [
"unsloth/gemma-3-27b-it-unsloth-bnb-4bit",
"google/gemma-3-27b-it",
"unsloth/gemma-3-27b-it-bnb-4bit",
],
"unsloth_gemma-3-270m-it.yaml": [
"unsloth/gemma-3-270m-it-unsloth-bnb-4bit",
"google/gemma-3-270m-it",
"unsloth/gemma-3-270m-it-bnb-4bit",
],
"unsloth_gemma-3n-E4B-it.yaml": [
"unsloth/gemma-3n-E4B-it-unsloth-bnb-4bit",
"google/gemma-3n-E4B-it",
"unsloth/gemma-3n-E4B-it-unsloth-bnb-4bit",
],
"unsloth_gemma-3n-E4B.yaml": [
"unsloth/gemma-3n-E4B-unsloth-bnb-4bit",
"google/gemma-3n-E4B",
],
"unsloth_gpt-oss-20b.yaml": [
"openai/gpt-oss-20b",
"unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"unsloth/gpt-oss-20b-BF16",
],
"unsloth_gpt-oss-120b.yaml": [
"openai/gpt-oss-120b",
"unsloth/gpt-oss-120b-unsloth-bnb-4bit",
],
"unsloth_granite-4.0-350m-unsloth-bnb-4bit.yaml": [
"unsloth/granite-4.0-350m",
"ibm-granite/granite-4.0-350m",
"unsloth/granite-4.0-350m-bnb-4bit",
],
"unsloth_granite-4.0-h-micro.yaml": [
"ibm-granite/granite-4.0-h-micro",
"unsloth/granite-4.0-h-micro-bnb-4bit",
"unsloth/granite-4.0-h-micro-unsloth-bnb-4bit",
],
"unsloth_LFM2-1.2B.yaml": [
"unsloth/LFM2-1.2B",
],
"unsloth_llama-3-8b-bnb-4bit.yaml": [
"unsloth/llama-3-8b",
"meta-llama/Meta-Llama-3-8B",
],
"unsloth_llama-3-8b-Instruct-bnb-4bit.yaml": [
"unsloth/llama-3-8b-Instruct",
"meta-llama/Meta-Llama-3-8B-Instruct",
],
"unsloth_Meta-Llama-3.1-70B-bnb-4bit.yaml": [
"unsloth/Meta-Llama-3.1-8B-bnb-4bit",
"unsloth/Meta-Llama-3.1-8B-unsloth-bnb-4bit",
"meta-llama/Meta-Llama-3.1-8B",
"unsloth/Meta-Llama-3.1-70B-bnb-4bit",
"unsloth/Meta-Llama-3.1-8B",
"unsloth/Meta-Llama-3.1-70B",
"meta-llama/Meta-Llama-3.1-70B",
"unsloth/Meta-Llama-3.1-405B-bnb-4bit",
"meta-llama/Meta-Llama-3.1-405B",
],
"unsloth_Meta-Llama-3.1-8B-Instruct-bnb-4bit.yaml": [
"unsloth/Meta-Llama-3.1-8B-Instruct-unsloth-bnb-4bit",
"unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
"meta-llama/Meta-Llama-3.1-8B-Instruct",
"unsloth/Meta-Llama-3.1-8B-Instruct",
"RedHatAI/Llama-3.1-8B-Instruct-FP8",
"unsloth/Llama-3.1-8B-Instruct-FP8-Block",
"unsloth/Llama-3.1-8B-Instruct-FP8-Dynamic",
],
"unsloth_Llama-3.2-3B-Instruct.yaml": [
"unsloth/Llama-3.2-3B-Instruct-unsloth-bnb-4bit",
"meta-llama/Llama-3.2-3B-Instruct",
"unsloth/Llama-3.2-3B-Instruct-bnb-4bit",
"RedHatAI/Llama-3.2-3B-Instruct-FP8",
"unsloth/Llama-3.2-3B-Instruct-FP8-Block",
"unsloth/Llama-3.2-3B-Instruct-FP8-Dynamic",
],
"unsloth_Llama-3.2-1B-Instruct.yaml": [
"unsloth/Llama-3.2-1B-Instruct-unsloth-bnb-4bit",
"meta-llama/Llama-3.2-1B-Instruct",
"unsloth/Llama-3.2-1B-Instruct-bnb-4bit",
"RedHatAI/Llama-3.2-1B-Instruct-FP8",
"unsloth/Llama-3.2-1B-Instruct-FP8-Block",
"unsloth/Llama-3.2-1B-Instruct-FP8-Dynamic",
],
"unsloth_Llama-3.2-11B-Vision-Instruct.yaml": [
"unsloth/Llama-3.2-11B-Vision-Instruct-unsloth-bnb-4bit",
"meta-llama/Llama-3.2-11B-Vision-Instruct",
"unsloth/Llama-3.2-11B-Vision-Instruct-bnb-4bit",
],
"unsloth_Llama-3.3-70B-Instruct.yaml": [
"unsloth/Llama-3.3-70B-Instruct-unsloth-bnb-4bit",
"meta-llama/Llama-3.3-70B-Instruct",
"unsloth/Llama-3.3-70B-Instruct-bnb-4bit",
"RedHatAI/Llama-3.3-70B-Instruct-FP8",
"unsloth/Llama-3.3-70B-Instruct-FP8-Block",
"unsloth/Llama-3.3-70B-Instruct-FP8-Dynamic",
],
"unsloth_Llasa-3B.yaml": [
"HKUSTAudio/Llasa-1B",
"unsloth/Llasa-3B",
],
"unsloth_Magistral-Small-2509-unsloth-bnb-4bit.yaml": [
"unsloth/Magistral-Small-2509",
"mistralai/Magistral-Small-2509",
"unsloth/Magistral-Small-2509-bnb-4bit",
],
"unsloth_Ministral-3-3B-Instruct-2512.yaml": [
"unsloth/Ministral-3-3B-Instruct-2512",
],
"unsloth_mistral-7b-v0.3-bnb-4bit.yaml": [
"unsloth/mistral-7b-v0.3-bnb-4bit"
"unsloth/mistral-7b-v0.3",
"mistralai/Mistral-7B-v0.3",
],
"unsloth_Mistral-Nemo-Base-2407-bnb-4bit.yaml": [
"unsloth/Mistral-Nemo-Base-2407-bnb-4bit",
"unsloth/Mistral-Nemo-Base-2407",
"mistralai/Mistral-Nemo-Base-2407",
"unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
"unsloth/Mistral-Nemo-Instruct-2407",
"mistralai/Mistral-Nemo-Instruct-2407",
],
"unsloth_Mistral-Small-Instruct-2409.yaml": [
"unsloth/Mistral-Small-Instruct-2409-bnb-4bit",
"mistralai/Mistral-Small-Instruct-2409",
],
"unsloth_mistral-7b-instruct-v0.3-bnb-4bit.yaml": [
"unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"unsloth/mistral-7b-instruct-v0.3",
"mistralai/Mistral-7B-Instruct-v0.3",
],
"unsloth_Qwen2.5-1.5B-Instruct.yaml": [
"unsloth/Qwen2.5-1.5B-Instruct-unsloth-bnb-4bit",
"Qwen/Qwen2.5-1.5B-Instruct",
"unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit",
],
"unsloth_Nemotron-3-Nano-30B-A3B.yaml": [
"unsloth/Nemotron-3-Nano-30B-A3B",
],
"unsloth_orpheus-3b-0.1-ft.yaml": [
"unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit",
"canopylabs/orpheus-3b-0.1-ft",
"unsloth/orpheus-3b-0.1-ft-bnb-4bit",
],
"OuteAI_Llama-OuteTTS-1.0-1B.yaml": [
"OuteAI/Llama-OuteTTS-1.0-1B",
],
"unsloth_PaddleOCR-VL.yaml": [
"unsloth/PaddleOCR-VL",
],
"unsloth_Phi-3-medium-4k-instruct.yaml": [
"unsloth/Phi-3-medium-4k-instruct-bnb-4bit",
"microsoft/Phi-3-medium-4k-instruct",
],
"unsloth_Phi-3.5-mini-instruct.yaml": [
"unsloth/Phi-3.5-mini-instruct-bnb-4bit",
"microsoft/Phi-3.5-mini-instruct",
],
"unsloth_Phi-4.yaml": [
"unsloth/phi-4-unsloth-bnb-4bit",
"microsoft/phi-4",
"unsloth/phi-4-bnb-4bit",
],
"unsloth_Pixtral-12B-2409.yaml": [
"unsloth/Pixtral-12B-2409-unsloth-bnb-4bit",
"mistralai/Pixtral-12B-2409",
"unsloth/Pixtral-12B-2409-bnb-4bit",
],
"unsloth_Qwen2-7B.yaml": [
"unsloth/Qwen2-7B-bnb-4bit",
"Qwen/Qwen2-7B",
],
"unsloth_Qwen2-VL-7B-Instruct.yaml": [
"unsloth/Qwen2-VL-7B-Instruct-unsloth-bnb-4bit",
"Qwen/Qwen2-VL-7B-Instruct",
"unsloth/Qwen2-VL-7B-Instruct-bnb-4bit",
],
"unsloth_Qwen2.5-7B.yaml": [
"unsloth/Qwen2.5-7B-unsloth-bnb-4bit",
"Qwen/Qwen2.5-7B",
"unsloth/Qwen2.5-7B-bnb-4bit",
],
"unsloth_Qwen2.5-Coder-1.5B-Instruct.yaml": [
"unsloth/Qwen2.5-Coder-1.5B-Instruct-bnb-4bit",
"Qwen/Qwen2.5-Coder-1.5B-Instruct",
],
"unsloth_Qwen2.5-Coder-14B-Instruct.yaml": [
"unsloth/Qwen2.5-Coder-14B-Instruct-bnb-4bit",
"Qwen/Qwen2.5-Coder-14B-Instruct",
],
"unsloth_Qwen2.5-VL-7B-Instruct-bnb-4bit.yaml": [
"unsloth/Qwen2.5-VL-7B-Instruct",
"Qwen/Qwen2.5-VL-7B-Instruct",
"unsloth/Qwen2.5-VL-7B-Instruct-unsloth-bnb-4bit",
],
"unsloth_Qwen3-0.6B.yaml": [
"unsloth/Qwen3-0.6B-unsloth-bnb-4bit",
"Qwen/Qwen3-0.6B",
"unsloth/Qwen3-0.6B-bnb-4bit",
"Qwen/Qwen3-0.6B-FP8",
"unsloth/Qwen3-0.6B-FP8",
],
"unsloth_Qwen3-4B-Instruct-2507.yaml": [
"unsloth/Qwen3-4B-Instruct-2507-unsloth-bnb-4bit",
"Qwen/Qwen3-4B-Instruct-2507",
"unsloth/Qwen3-4B-Instruct-2507-bnb-4bit",
"Qwen/Qwen3-4B-Instruct-2507-FP8",
"unsloth/Qwen3-4B-Instruct-2507-FP8",
],
"unsloth_Qwen3-4B-Thinking-2507.yaml": [
"unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit",
"Qwen/Qwen3-4B-Thinking-2507",
"unsloth/Qwen3-4B-Thinking-2507-bnb-4bit",
"Qwen/Qwen3-4B-Thinking-2507-FP8",
"unsloth/Qwen3-4B-Thinking-2507-FP8",
],
"unsloth_Qwen3-14B-Base-unsloth-bnb-4bit.yaml": [
"unsloth/Qwen3-14B-Base",
"Qwen/Qwen3-14B-Base",
"unsloth/Qwen3-14B-Base-bnb-4bit",
],
"unsloth_Qwen3-14B.yaml": [
"unsloth/Qwen3-14B-unsloth-bnb-4bit",
"Qwen/Qwen3-14B",
"unsloth/Qwen3-14B-bnb-4bit",
"Qwen/Qwen3-14B-FP8",
"unsloth/Qwen3-14B-FP8",
],
"unsloth_Qwen3-32B.yaml": [
"unsloth/Qwen3-32B-unsloth-bnb-4bit",
"Qwen/Qwen3-32B",
"unsloth/Qwen3-32B-bnb-4bit",
"Qwen/Qwen3-32B-FP8",
"unsloth/Qwen3-32B-FP8",
],
"unsloth_Qwen3-VL-8B-Instruct-unsloth-bnb-4bit.yaml": [
"Qwen/Qwen3-VL-8B-Instruct-FP8",
"unsloth/Qwen3-VL-8B-Instruct-FP8",
"unsloth/Qwen3-VL-8B-Instruct",
"Qwen/Qwen3-VL-8B-Instruct",
"unsloth/Qwen3-VL-8B-Instruct-bnb-4bit",
],
"sesame_csm-1b.yaml": [
"sesame/csm-1b",
],
"Spark-TTS-0.5B_LLM.yaml": [
"Spark-TTS-0.5B/LLM",
],
"unsloth_tinyllama-bnb-4bit.yaml": [
"unsloth/tinyllama",
"TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T",
],
"unsloth_whisper-large-v3.yaml": [
"unsloth/whisper-large-v3",
"openai/whisper-large-v3",
],
}
# Reverse mapping for quick lookup: model_name -> canonical_filename
_REVERSE_MODEL_MAPPING = {}
for canonical_file, model_names in MODEL_NAME_MAPPING.items():
for model_name in model_names:
_REVERSE_MODEL_MAPPING[model_name] = canonical_file
def load_model_config(model_name: str, use_auth: bool = False, token: Optional[str] = None):
"""
Load model config with optional authentication control.
"""
if token:
# Explicit token provided - use it
return AutoConfig.from_pretrained(
model_name,
trust_remote_code=True,
token=token
)
if not use_auth:
# Load without any authentication (for public model checks)
with without_hf_auth():
return AutoConfig.from_pretrained(
model_name,
trust_remote_code=True,
token=None
)
# Use default authentication (cached tokens)
return AutoConfig.from_pretrained(
model_name,
trust_remote_code=True
)
pass
def is_vision_model(model_name: str, hf_token: Optional[str] = None) -> bool:
"""
Detect vision models by checking architecture in config.
Works for fine-tuned models since they inherit the base architecture.
Args:
model_name: Model identifier (HF repo or local path)
hf_token: Optional HF token for accessing gated/private models
"""
try:
config = load_model_config(model_name, token=hf_token)
# Check vision arch
if hasattr(config, 'architectures'):
is_vlm = any(
x.endswith(("ForConditionalGeneration", "ForVisionText2Text"))
for x in config.architectures
)
if is_vlm:
logger.info(f"Model {model_name} detected as vision model: architecture {config.architectures}")
return True
# Quick check for vision config as backup
if hasattr(config, 'vision_config'):
logger.info(f"Model {model_name} detected as vision model: has vision_config")
return True
return False
except Exception as e:
logger.warning(f"Could not determine if {model_name} is vision model: {e}")
return False
pass
def scan_trained_loras(outputs_dir: str = "./outputs") -> List[Tuple[str, str]]:
"""
Scan outputs folder for trained LoRA adapters.
Returns:
List of tuples: [(display_name, adapter_path), ...]
Example:
[
("unsloth_Meta-Llama-3.1_...", "./outputs/unsloth_Meta-Llama-3.1_.../"),
("my_finetuned_model", "./outputs/my_finetuned_model/"),
]
"""
trained_loras = []
outputs_path = Path(outputs_dir)
if not outputs_path.exists():
logger.warning(f"Outputs directory not found: {outputs_dir}")
return trained_loras
try:
for item in outputs_path.iterdir():
if item.is_dir():
# Check if this directory contains a LoRA adapter
adapter_config = item / "adapter_config.json"
adapter_model = item / "adapter_model.safetensors"
if adapter_config.exists() or adapter_model.exists():
display_name = item.name
adapter_path = str(item)
trained_loras.append((display_name, adapter_path))
logger.debug(f"Found trained LoRA: {display_name}")
# Sort by modification time (newest first)
trained_loras.sort(key=lambda x: Path(x[1]).stat().st_mtime, reverse=True)
logger.info(f"Found {len(trained_loras)} trained LoRA adapters in {outputs_dir}")
return trained_loras
except Exception as e:
logger.error(f"Error scanning outputs folder: {e}")
return []
def get_base_model_from_lora(lora_path: str) -> Optional[str]:
"""
Read the base model name from a LoRA adapter's config.
Args:
lora_path: Path to the LoRA adapter directory
Returns:
Base model identifier (e.g., "unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit")
or None if not found
Example:
>>> get_base_model_from_lora("./outputs/unsloth_Meta-Llama-3.1_.../")
"unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit"
"""
try:
lora_path_obj = Path(lora_path)
# Try adapter_config.json first
adapter_config_path = lora_path_obj / "adapter_config.json"
if adapter_config_path.exists():
with open(adapter_config_path, 'r') as f:
config = json.load(f)
base_model = config.get("base_model_name_or_path")
if base_model:
logger.info(f"Detected base model from adapter_config.json: {base_model}")
return base_model
# Fallback: try training_args.bin (requires torch)
training_args_path = lora_path_obj / "training_args.bin"
if training_args_path.exists():
try:
import torch
training_args = torch.load(training_args_path)
if hasattr(training_args, 'model_name_or_path'):
base_model = training_args.model_name_or_path
logger.info(f"Detected base model from training_args.bin: {base_model}")
return base_model
except Exception as e:
logger.warning(f"Could not load training_args.bin: {e}")
# Last resort: parse from directory name
# Format: unsloth_Meta-Llama-3.1-8B-Instruct-bnb-4bit_timestamp
dir_name = lora_path_obj.name
if dir_name.startswith("unsloth_"):
# Remove timestamp suffix (usually _1234567890)
parts = dir_name.split("_")
# Reconstruct model name
if len(parts) >= 2:
model_parts = parts[1:-1] # Skip "unsloth" and timestamp
base_model = "unsloth/" + "_".join(model_parts)
logger.info(f"Detected base model from directory name: {base_model}")
return base_model
logger.warning(f"Could not detect base model for LoRA: {lora_path}")
return None
except Exception as e:
logger.error(f"Error reading base model from LoRA config: {e}")
return None
pass
# Status indicators that appear in UI dropdowns
UI_STATUS_INDICATORS = [" (Ready)", " (Loading...)", " (Active)", "↓ "]
def load_model_defaults(model_name: str) -> Dict[str, Any]:
"""
Load default training parameters for a model from YAML file.
Args:
model_name: Model identifier (e.g., "unsloth/Meta-Llama-3.1-8B-bnb-4bit")
Returns:
Dictionary with default parameters from YAML file, or empty dict if not found
The function looks for a YAML file in configs/model_defaults/ (including subfolders)
based on the model name or its aliases from MODEL_NAME_MAPPING.
If no specific file exists, it falls back to default.yaml.
"""
try:
# Get the script directory to locate configs
script_dir = Path(__file__).parent.parent
defaults_dir = script_dir / "configs" / "model_defaults"
# First, check if model is in the mapping
if model_name in _REVERSE_MODEL_MAPPING:
canonical_file = _REVERSE_MODEL_MAPPING[model_name]
# Search in subfolders and root
for config_path in defaults_dir.rglob(canonical_file):
if config_path.is_file():
with open(config_path, 'r', encoding='utf-8') as f:
config = yaml.safe_load(f) or {}
logger.info(f"Loaded model defaults from {config_path} (via mapping)")
return config
# Try exact model name match (for backward compatibility)
model_filename = model_name.replace("/", "_") + ".yaml"
# Search in subfolders and root
for config_path in defaults_dir.rglob(model_filename):
if config_path.is_file():
with open(config_path, 'r', encoding='utf-8') as f:
config = yaml.safe_load(f) or {}
logger.info(f"Loaded model defaults from {config_path}")
return config
# Fall back to default.yaml
default_config_path = defaults_dir / "default.yaml"
if default_config_path.exists():
with open(default_config_path, 'r', encoding='utf-8') as f:
config = yaml.safe_load(f) or {}
logger.info(f"Loaded default model defaults from {default_config_path}")
return config
logger.warning(f"No default config found for model {model_name}")
return {}
except Exception as e:
logger.error(f"Error loading model defaults for {model_name}: {e}")
return {}
@dataclass
class ModelConfig:
"""Configuration for a model to load"""
identifier: str # Clean model identifier (org/name or path)
display_name: str # Original UI display name
path: str # Normalized filesystem path
is_local: bool # Is this a local file vs HF model?
is_cached: bool # Is this already in HF cache?
is_vision: bool # Is this a vision model?
is_lora: bool # Is this a lora adapter?
base_model: Optional[str] = None # Base model (for LoRAs)
@classmethod
def from_lora_path(cls, lora_path: str, hf_token: Optional[str] = None) -> Optional['ModelConfig']:
"""
Create ModelConfig from a local LoRA adapter path.
Automatically detects the base model from adapter config.
Args:
lora_path: Path to LoRA adapter (e.g., "./outputs/unsloth_Meta-Llama-3.1_.../")
hf_token: HF token for vision detection
Returns:
ModelConfig for the LoRA adapter
"""
try:
lora_path_obj = Path(lora_path)
if not lora_path_obj.exists():
logger.error(f"LoRA path does not exist: {lora_path}")
return None
# Get base model
base_model = get_base_model_from_lora(lora_path)
if not base_model:
logger.error(f"Could not determine base model for LoRA: {lora_path}")
return None
# Check if base model is vision
is_vision = is_vision_model(base_model, hf_token=hf_token)
display_name = lora_path_obj.name
identifier = lora_path # Use path as identifier for local LoRAs
return cls(
identifier=identifier,
display_name=display_name,
path=lora_path,
is_local=True,
is_cached=True, # Local LoRAs are always "cached"
is_vision=is_vision,
is_lora=True,
base_model=base_model,
)
except Exception as e:
logger.error(f"Error creating ModelConfig from LoRA path: {e}")
return None
@classmethod
def from_ui_selection(cls,
dropdown_value: Optional[str],
search_value: Optional[str],
local_models: list = None,
hf_token: Optional[str] = None,
is_lora: bool = False) -> Optional['ModelConfig']:
"""
Create a universal ModelConfig from UI dropdown/search selections.
Handles base models and LoRA adapters.
"""
selected = None
if search_value and search_value.strip():
selected = search_value.strip()
elif dropdown_value:
selected = dropdown_value
if not selected:
return None
display_name = selected
# Use the correct 'local_models' parameter to resolve display names
if " (Active)" in selected or " (Ready)" in selected:
clean_display_name = selected.replace(" (Active)", "").replace(" (Ready)", "")
if local_models:
for local_display, local_path in local_models:
if local_display == clean_display_name:
selected = local_path
break
# Clean all UI status indicators to get the final identifier
identifier = selected
for status in UI_STATUS_INDICATORS:
identifier = identifier.replace(status, "")
identifier = identifier.strip()
is_local = is_local_path(identifier)
path = normalize_path(identifier) if is_local else identifier
# Add unsloth/ prefix for shorthand HF models
if not is_local and "/" not in identifier:
identifier = f"unsloth/{identifier}"
path = identifier
# --- Logic for Base Model and Vision Detection ---
base_model = None
is_vision = False
if is_lora:
# For a LoRA, we MUST find its base model.
base_model = get_base_model_from_lora(path)
if not base_model:
logger.warning(f"Could not determine base model for LoRA '{path}'. Cannot create config.")
return None # Cannot proceed without a base model
# A LoRA's vision capability is determined by its base model.
is_vision = is_vision_model(base_model, hf_token=hf_token)
else:
# For a base model, just check its own vision status.
is_vision = is_vision_model(identifier, hf_token=hf_token)
from utils.paths import is_model_cached
is_cached = is_model_cached(identifier) if not is_local else True
return cls(
identifier=identifier,
display_name=display_name,
path=path,
is_local=is_local,
is_cached=is_cached,
is_vision=is_vision,
is_lora=is_lora,
base_model=base_model, # This will be None for base models, and populated for LoRAs
)
pass