f4d54a8de7
The _VISION_CHECK_SCRIPT subprocess used logger.info() but logger was never defined in the subprocess context. This caused a NameError on every vision check, making all transformers 5.x models (Qwen3.5, GLM, etc.) fall back to text-only mode even when they support vision. Replace logger.info() with print() since the parent process reads the subprocess stdout via result.stdout.
1776 lines
64 KiB
Python
1776 lines
64 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""
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Model and LoRA configuration handling
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"""
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from transformers import AutoConfig
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from dataclasses import dataclass
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from typing import Optional, Dict, Any
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from utils.paths import (
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normalize_path,
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is_local_path,
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is_model_cached,
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outputs_root,
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exports_root,
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resolve_output_dir,
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resolve_export_dir,
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)
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from utils.utils import without_hf_auth
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import structlog
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from loggers import get_logger
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import os
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import subprocess
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import sys
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from pathlib import Path
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from typing import List, Tuple
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import json
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import yaml
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logger = get_logger(__name__)
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# Model name mapping: maps all equivalent model names to their canonical YAML config file
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# Format: "canonical_model_name.yaml": [list of all equivalent model names]
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# Based on the model mapper provided - canonical filename is based on the first model name in the mapper
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MODEL_NAME_MAPPING = {
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# ── Embedding models ──
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"unsloth_all-MiniLM-L6-v2.yaml": [
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"unsloth/all-MiniLM-L6-v2",
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"sentence-transformers/all-MiniLM-L6-v2",
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],
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"unsloth_bge-m3.yaml": [
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"unsloth/bge-m3",
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"BAAI/bge-m3",
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],
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"unsloth_embeddinggemma-300m.yaml": [
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"unsloth/embeddinggemma-300m",
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"google/embeddinggemma-300m",
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],
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"unsloth_gte-modernbert-base.yaml": [
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"unsloth/gte-modernbert-base",
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"Alibaba-NLP/gte-modernbert-base",
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],
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"unsloth_Qwen3-Embedding-0.6B.yaml": [
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"unsloth/Qwen3-Embedding-0.6B",
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"Qwen/Qwen3-Embedding-0.6B",
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"unsloth/Qwen3-Embedding-4B",
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"Qwen/Qwen3-Embedding-4B",
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],
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# ── Other models ──
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"unsloth_answerdotai_ModernBERT-large.yaml": [
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"answerdotai/ModernBERT-large",
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],
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"unsloth_Qwen2.5-Coder-7B-Instruct-bnb-4bit.yaml": [
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"unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit",
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"unsloth/Qwen2.5-Coder-7B-Instruct",
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"Qwen/Qwen2.5-Coder-7B-Instruct",
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],
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"unsloth_codegemma-7b-bnb-4bit.yaml": [
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"unsloth/codegemma-7b-bnb-4bit",
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"unsloth/codegemma-7b",
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"google/codegemma-7b",
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],
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"unsloth_ERNIE-4.5-21B-A3B-PT.yaml": [
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"unsloth/ERNIE-4.5-21B-A3B-PT",
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],
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"unsloth_ERNIE-4.5-VL-28B-A3B-PT.yaml": [
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"unsloth/ERNIE-4.5-VL-28B-A3B-PT",
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],
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"tiiuae_Falcon-H1-0.5B-Instruct.yaml": [
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"tiiuae/Falcon-H1-0.5B-Instruct",
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"unsloth/Falcon-H1-0.5B-Instruct",
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],
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"unsloth_functiongemma-270m-it.yaml": [
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"unsloth/functiongemma-270m-it-unsloth-bnb-4bit",
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"google/functiongemma-270m-it",
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"unsloth/functiongemma-270m-it-unsloth-bnb-4bit",
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],
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"unsloth_gemma-2-2b.yaml": [
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"unsloth/gemma-2-2b-bnb-4bit",
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"google/gemma-2-2b",
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],
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"unsloth_gemma-2-27b-bnb-4bit.yaml": [
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"unsloth/gemma-2-9b-bnb-4bit",
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"unsloth/gemma-2-9b",
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"google/gemma-2-9b",
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"unsloth/gemma-2-27b",
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"google/gemma-2-27b",
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],
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"unsloth_gemma-3-4b-pt.yaml": [
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"unsloth/gemma-3-4b-pt-unsloth-bnb-4bit",
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"google/gemma-3-4b-pt",
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"unsloth/gemma-3-4b-pt-bnb-4bit",
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],
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"unsloth_gemma-3-4b-it.yaml": [
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"unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
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"google/gemma-3-4b-it",
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"unsloth/gemma-3-4b-it-bnb-4bit",
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],
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"unsloth_gemma-3-27b-it.yaml": [
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"unsloth/gemma-3-27b-it-unsloth-bnb-4bit",
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"google/gemma-3-27b-it",
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"unsloth/gemma-3-27b-it-bnb-4bit",
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],
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"unsloth_gemma-3-270m-it.yaml": [
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"unsloth/gemma-3-270m-it-unsloth-bnb-4bit",
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"google/gemma-3-270m-it",
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"unsloth/gemma-3-270m-it-bnb-4bit",
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],
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"unsloth_gemma-3n-E4B-it.yaml": [
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"unsloth/gemma-3n-E4B-it-unsloth-bnb-4bit",
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"google/gemma-3n-E4B-it",
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"unsloth/gemma-3n-E4B-it-unsloth-bnb-4bit",
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],
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"unsloth_gemma-3n-E4B.yaml": [
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"unsloth/gemma-3n-E4B-unsloth-bnb-4bit",
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"google/gemma-3n-E4B",
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],
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"unsloth_gpt-oss-20b.yaml": [
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"openai/gpt-oss-20b",
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"unsloth/gpt-oss-20b-unsloth-bnb-4bit",
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"unsloth/gpt-oss-20b-BF16",
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],
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"unsloth_gpt-oss-120b.yaml": [
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"openai/gpt-oss-120b",
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"unsloth/gpt-oss-120b-unsloth-bnb-4bit",
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],
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"unsloth_granite-4.0-350m-unsloth-bnb-4bit.yaml": [
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"unsloth/granite-4.0-350m",
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"ibm-granite/granite-4.0-350m",
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"unsloth/granite-4.0-350m-bnb-4bit",
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],
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"unsloth_granite-4.0-h-micro.yaml": [
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"ibm-granite/granite-4.0-h-micro",
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"unsloth/granite-4.0-h-micro-bnb-4bit",
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"unsloth/granite-4.0-h-micro-unsloth-bnb-4bit",
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],
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"unsloth_LFM2-1.2B.yaml": [
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"unsloth/LFM2-1.2B",
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],
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"unsloth_llama-3-8b-bnb-4bit.yaml": [
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"unsloth/llama-3-8b",
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"meta-llama/Meta-Llama-3-8B",
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],
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"unsloth_llama-3-8b-Instruct-bnb-4bit.yaml": [
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"unsloth/llama-3-8b-Instruct",
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"meta-llama/Meta-Llama-3-8B-Instruct",
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],
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"unsloth_Meta-Llama-3.1-70B-bnb-4bit.yaml": [
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"unsloth/Meta-Llama-3.1-8B-bnb-4bit",
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"unsloth/Meta-Llama-3.1-8B-unsloth-bnb-4bit",
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"meta-llama/Meta-Llama-3.1-8B",
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"unsloth/Meta-Llama-3.1-70B-bnb-4bit",
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"unsloth/Meta-Llama-3.1-8B",
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"unsloth/Meta-Llama-3.1-70B",
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"meta-llama/Meta-Llama-3.1-70B",
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"unsloth/Meta-Llama-3.1-405B-bnb-4bit",
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"meta-llama/Meta-Llama-3.1-405B",
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],
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"unsloth_Meta-Llama-3.1-8B-Instruct-bnb-4bit.yaml": [
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"unsloth/Meta-Llama-3.1-8B-Instruct-unsloth-bnb-4bit",
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"unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
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"meta-llama/Meta-Llama-3.1-8B-Instruct",
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"unsloth/Meta-Llama-3.1-8B-Instruct",
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"RedHatAI/Llama-3.1-8B-Instruct-FP8",
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"unsloth/Llama-3.1-8B-Instruct-FP8-Block",
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"unsloth/Llama-3.1-8B-Instruct-FP8-Dynamic",
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],
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"unsloth_Llama-3.2-3B-Instruct.yaml": [
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"unsloth/Llama-3.2-3B-Instruct-unsloth-bnb-4bit",
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"meta-llama/Llama-3.2-3B-Instruct",
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"unsloth/Llama-3.2-3B-Instruct-bnb-4bit",
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"RedHatAI/Llama-3.2-3B-Instruct-FP8",
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"unsloth/Llama-3.2-3B-Instruct-FP8-Block",
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"unsloth/Llama-3.2-3B-Instruct-FP8-Dynamic",
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],
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"unsloth_Llama-3.2-1B-Instruct.yaml": [
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"unsloth/Llama-3.2-1B-Instruct-unsloth-bnb-4bit",
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"meta-llama/Llama-3.2-1B-Instruct",
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"unsloth/Llama-3.2-1B-Instruct-bnb-4bit",
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"RedHatAI/Llama-3.2-1B-Instruct-FP8",
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"unsloth/Llama-3.2-1B-Instruct-FP8-Block",
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"unsloth/Llama-3.2-1B-Instruct-FP8-Dynamic",
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],
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"unsloth_Llama-3.2-11B-Vision-Instruct.yaml": [
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"unsloth/Llama-3.2-11B-Vision-Instruct-unsloth-bnb-4bit",
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"meta-llama/Llama-3.2-11B-Vision-Instruct",
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"unsloth/Llama-3.2-11B-Vision-Instruct-bnb-4bit",
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],
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"unsloth_Llama-3.3-70B-Instruct.yaml": [
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"unsloth/Llama-3.3-70B-Instruct-unsloth-bnb-4bit",
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"meta-llama/Llama-3.3-70B-Instruct",
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"unsloth/Llama-3.3-70B-Instruct-bnb-4bit",
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"RedHatAI/Llama-3.3-70B-Instruct-FP8",
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"unsloth/Llama-3.3-70B-Instruct-FP8-Block",
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"unsloth/Llama-3.3-70B-Instruct-FP8-Dynamic",
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],
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"unsloth_Llasa-3B.yaml": [
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"HKUSTAudio/Llasa-1B",
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"unsloth/Llasa-3B",
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],
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"unsloth_Magistral-Small-2509-unsloth-bnb-4bit.yaml": [
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"unsloth/Magistral-Small-2509",
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"mistralai/Magistral-Small-2509",
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"unsloth/Magistral-Small-2509-bnb-4bit",
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],
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"unsloth_Ministral-3-3B-Instruct-2512.yaml": [
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"unsloth/Ministral-3-3B-Instruct-2512",
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],
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"unsloth_mistral-7b-v0.3-bnb-4bit.yaml": [
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"unsloth/mistral-7b-v0.3-bnb-4bit",
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"unsloth/mistral-7b-v0.3",
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"mistralai/Mistral-7B-v0.3",
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],
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"unsloth_Mistral-Nemo-Base-2407-bnb-4bit.yaml": [
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"unsloth/Mistral-Nemo-Base-2407-bnb-4bit",
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"unsloth/Mistral-Nemo-Base-2407",
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"mistralai/Mistral-Nemo-Base-2407",
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"unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
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"unsloth/Mistral-Nemo-Instruct-2407",
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"mistralai/Mistral-Nemo-Instruct-2407",
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],
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"unsloth_Mistral-Small-Instruct-2409.yaml": [
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"unsloth/Mistral-Small-Instruct-2409-bnb-4bit",
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"mistralai/Mistral-Small-Instruct-2409",
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],
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"unsloth_mistral-7b-instruct-v0.3-bnb-4bit.yaml": [
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"unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
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"unsloth/mistral-7b-instruct-v0.3",
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"mistralai/Mistral-7B-Instruct-v0.3",
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],
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"unsloth_Qwen2.5-1.5B-Instruct.yaml": [
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"unsloth/Qwen2.5-1.5B-Instruct-unsloth-bnb-4bit",
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"Qwen/Qwen2.5-1.5B-Instruct",
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"unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit",
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],
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"unsloth_Nemotron-3-Nano-30B-A3B.yaml": [
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"unsloth/Nemotron-3-Nano-30B-A3B",
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],
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"unsloth_orpheus-3b-0.1-ft.yaml": [
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"unsloth/orpheus-3b-0.1-ft",
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"unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit",
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"canopylabs/orpheus-3b-0.1-ft",
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"unsloth/orpheus-3b-0.1-ft-bnb-4bit",
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],
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"OuteAI_Llama-OuteTTS-1.0-1B.yaml": [
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"OuteAI/Llama-OuteTTS-1.0-1B",
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"unsloth/Llama-OuteTTS-1.0-1B",
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"unsloth/llama-outetts-1.0-1b",
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"OuteAI/OuteTTS-1.0-0.6B",
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"unsloth/OuteTTS-1.0-0.6B",
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"unsloth/outetts-1.0-0.6b",
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],
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"unsloth_PaddleOCR-VL.yaml": [
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"unsloth/PaddleOCR-VL",
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],
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"unsloth_Phi-3-medium-4k-instruct.yaml": [
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"unsloth/Phi-3-medium-4k-instruct-bnb-4bit",
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"microsoft/Phi-3-medium-4k-instruct",
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],
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"unsloth_Phi-3.5-mini-instruct.yaml": [
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"unsloth/Phi-3.5-mini-instruct-bnb-4bit",
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"microsoft/Phi-3.5-mini-instruct",
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],
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"unsloth_Phi-4.yaml": [
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"unsloth/phi-4-unsloth-bnb-4bit",
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"microsoft/phi-4",
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"unsloth/phi-4-bnb-4bit",
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],
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"unsloth_Pixtral-12B-2409.yaml": [
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"unsloth/Pixtral-12B-2409-unsloth-bnb-4bit",
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"mistralai/Pixtral-12B-2409",
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"unsloth/Pixtral-12B-2409-bnb-4bit",
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],
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"unsloth_Qwen2-7B.yaml": [
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"unsloth/Qwen2-7B-bnb-4bit",
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"Qwen/Qwen2-7B",
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],
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"unsloth_Qwen2-VL-7B-Instruct.yaml": [
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"unsloth/Qwen2-VL-7B-Instruct-unsloth-bnb-4bit",
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"Qwen/Qwen2-VL-7B-Instruct",
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"unsloth/Qwen2-VL-7B-Instruct-bnb-4bit",
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],
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"unsloth_Qwen2.5-7B.yaml": [
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"unsloth/Qwen2.5-7B-unsloth-bnb-4bit",
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"Qwen/Qwen2.5-7B",
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"unsloth/Qwen2.5-7B-bnb-4bit",
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],
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"unsloth_Qwen2.5-Coder-1.5B-Instruct.yaml": [
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"unsloth/Qwen2.5-Coder-1.5B-Instruct-bnb-4bit",
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"Qwen/Qwen2.5-Coder-1.5B-Instruct",
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],
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"unsloth_Qwen2.5-Coder-14B-Instruct.yaml": [
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"unsloth/Qwen2.5-Coder-14B-Instruct-bnb-4bit",
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"Qwen/Qwen2.5-Coder-14B-Instruct",
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],
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"unsloth_Qwen2.5-VL-7B-Instruct-bnb-4bit.yaml": [
|
|
"unsloth/Qwen2.5-VL-7B-Instruct",
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|
"Qwen/Qwen2.5-VL-7B-Instruct",
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|
"unsloth/Qwen2.5-VL-7B-Instruct-unsloth-bnb-4bit",
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],
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|
"unsloth_Qwen3-0.6B.yaml": [
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|
"unsloth/Qwen3-0.6B-unsloth-bnb-4bit",
|
|
"Qwen/Qwen3-0.6B",
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|
"unsloth/Qwen3-0.6B-bnb-4bit",
|
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"Qwen/Qwen3-0.6B-FP8",
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|
"unsloth/Qwen3-0.6B-FP8",
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],
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|
"unsloth_Qwen3-4B-Instruct-2507.yaml": [
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"unsloth/Qwen3-4B-Instruct-2507-unsloth-bnb-4bit",
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"Qwen/Qwen3-4B-Instruct-2507",
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"unsloth/Qwen3-4B-Instruct-2507-bnb-4bit",
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"Qwen/Qwen3-4B-Instruct-2507-FP8",
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"unsloth/Qwen3-4B-Instruct-2507-FP8",
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],
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"unsloth_Qwen3-4B-Thinking-2507.yaml": [
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"unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit",
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"Qwen/Qwen3-4B-Thinking-2507",
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"unsloth/Qwen3-4B-Thinking-2507-bnb-4bit",
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"Qwen/Qwen3-4B-Thinking-2507-FP8",
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"unsloth/Qwen3-4B-Thinking-2507-FP8",
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],
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"unsloth_Qwen3-14B-Base-unsloth-bnb-4bit.yaml": [
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"unsloth/Qwen3-14B-Base",
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"Qwen/Qwen3-14B-Base",
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"unsloth/Qwen3-14B-Base-bnb-4bit",
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],
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"unsloth_Qwen3-14B.yaml": [
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"unsloth/Qwen3-14B-unsloth-bnb-4bit",
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"Qwen/Qwen3-14B",
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"unsloth/Qwen3-14B-bnb-4bit",
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"Qwen/Qwen3-14B-FP8",
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"unsloth/Qwen3-14B-FP8",
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],
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"unsloth_Qwen3-32B.yaml": [
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"unsloth/Qwen3-32B-unsloth-bnb-4bit",
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"Qwen/Qwen3-32B",
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"unsloth/Qwen3-32B-bnb-4bit",
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"Qwen/Qwen3-32B-FP8",
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|
"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",
|
|
"unsloth/csm-1b",
|
|
],
|
|
"Spark-TTS-0.5B_LLM.yaml": [
|
|
"Spark-TTS-0.5B/LLM",
|
|
"unsloth/Spark-TTS-0.5B",
|
|
],
|
|
"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.lower()] = canonical_file
|
|
|
|
|
|
def load_model_config(
|
|
model_name: str,
|
|
use_auth: bool = False,
|
|
token: Optional[str] = None,
|
|
trust_remote_code: bool = True,
|
|
):
|
|
"""
|
|
Load model config with optional authentication control.
|
|
"""
|
|
|
|
if token:
|
|
# Explicit token provided - use it
|
|
return AutoConfig.from_pretrained(
|
|
model_name, trust_remote_code = trust_remote_code, 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 = trust_remote_code,
|
|
token = None,
|
|
)
|
|
|
|
# Use default authentication (cached tokens)
|
|
return AutoConfig.from_pretrained(
|
|
model_name,
|
|
trust_remote_code = trust_remote_code,
|
|
)
|
|
|
|
|
|
# VLM architecture suffixes and known VLM model_type values.
|
|
_VLM_ARCH_SUFFIXES = ("ForConditionalGeneration", "ForVisionText2Text")
|
|
_VLM_MODEL_TYPES = {
|
|
"phi3_v",
|
|
"llava",
|
|
"llava_next",
|
|
"llava_onevision",
|
|
"internvl_chat",
|
|
"cogvlm2",
|
|
"minicpmv",
|
|
}
|
|
|
|
# Pre-computed .venv_t5 path and backend dir for subprocess version switching.
|
|
_VENV_T5_DIR = str(Path.home() / ".unsloth" / "studio" / ".venv_t5")
|
|
_BACKEND_DIR = str(Path(__file__).resolve().parent.parent.parent)
|
|
|
|
# Inline script executed in a subprocess with transformers 5.x activated.
|
|
# Receives model_name and token via argv, prints JSON result to stdout.
|
|
_VISION_CHECK_SCRIPT = r"""
|
|
import sys, os, json
|
|
os.environ["TOKENIZERS_PARALLELISM"] = "false"
|
|
|
|
# Activate transformers 5.x
|
|
venv_t5 = sys.argv[1]
|
|
backend_dir = sys.argv[2]
|
|
model_name = sys.argv[3]
|
|
token = sys.argv[4] if len(sys.argv) > 4 and sys.argv[4] != "" else None
|
|
|
|
sys.path.insert(0, venv_t5)
|
|
if backend_dir not in sys.path:
|
|
sys.path.insert(0, backend_dir)
|
|
|
|
try:
|
|
from transformers import AutoConfig
|
|
kwargs = {"trust_remote_code": True}
|
|
if token:
|
|
kwargs["token"] = token
|
|
config = AutoConfig.from_pretrained(model_name, **kwargs)
|
|
|
|
is_vlm = False
|
|
if hasattr(config, "architectures"):
|
|
is_vlm = any(
|
|
x.endswith(("ForConditionalGeneration", "ForVisionText2Text"))
|
|
for x in config.architectures
|
|
)
|
|
if not is_vlm and hasattr(config, "vision_config"):
|
|
is_vlm = True
|
|
if not is_vlm and hasattr(config, "img_processor"):
|
|
is_vlm = True
|
|
if not is_vlm and hasattr(config, "image_token_index"):
|
|
is_vlm = True
|
|
if not is_vlm and hasattr(config, "model_type"):
|
|
vlm_types = {"phi3_v","llava","llava_next","llava_onevision",
|
|
"internvl_chat","cogvlm2","minicpmv"}
|
|
if config.model_type in vlm_types:
|
|
is_vlm = True
|
|
|
|
model_type = getattr(config, "model_type", "unknown")
|
|
archs = getattr(config, "architectures", [])
|
|
print(json.dumps({"is_vision": is_vlm, "model_type": model_type,
|
|
"architectures": archs}))
|
|
except Exception as exc:
|
|
print(json.dumps({"error": str(exc)}))
|
|
sys.exit(1)
|
|
"""
|
|
|
|
|
|
def _is_vision_model_subprocess(
|
|
model_name: str, hf_token: Optional[str] = None
|
|
) -> bool:
|
|
"""Run is_vision_model check in a subprocess with transformers 5.x.
|
|
|
|
Same pattern as training/inference workers: spawn a clean subprocess
|
|
with .venv_t5/ prepended to sys.path so AutoConfig recognizes newer
|
|
architectures (glm4_moe_lite, etc.).
|
|
"""
|
|
token_arg = hf_token or ""
|
|
|
|
try:
|
|
result = subprocess.run(
|
|
[
|
|
sys.executable,
|
|
"-c",
|
|
_VISION_CHECK_SCRIPT,
|
|
_VENV_T5_DIR,
|
|
_BACKEND_DIR,
|
|
model_name,
|
|
token_arg,
|
|
],
|
|
capture_output = True,
|
|
text = True,
|
|
timeout = 60,
|
|
)
|
|
|
|
if result.returncode != 0:
|
|
stderr = result.stderr.strip()
|
|
logger.warning(
|
|
"Vision check subprocess failed for '%s': %s",
|
|
model_name,
|
|
stderr or result.stdout.strip(),
|
|
)
|
|
return False
|
|
|
|
data = json.loads(result.stdout.strip())
|
|
if "error" in data:
|
|
logger.warning(
|
|
"Vision check subprocess error for '%s': %s",
|
|
model_name,
|
|
data["error"],
|
|
)
|
|
return False
|
|
|
|
is_vlm = data["is_vision"]
|
|
logger.info(
|
|
"Vision check (subprocess, transformers 5.x) for '%s': "
|
|
"model_type=%s, architectures=%s, is_vision=%s",
|
|
model_name,
|
|
data.get("model_type"),
|
|
data.get("architectures"),
|
|
is_vlm,
|
|
)
|
|
return is_vlm
|
|
|
|
except subprocess.TimeoutExpired:
|
|
logger.warning("Vision check subprocess timed out for '%s'", model_name)
|
|
return False
|
|
except Exception as exc:
|
|
logger.warning("Vision check subprocess failed for '%s': %s", model_name, exc)
|
|
return False
|
|
|
|
|
|
def is_vision_model(model_name: str, hf_token: Optional[str] = None) -> bool:
|
|
"""
|
|
Detect vision-language models (VLMs) by checking architecture in config.
|
|
Works for fine-tuned models since they inherit the base architecture.
|
|
|
|
For models that require transformers 5.x (e.g. GLM-4.7-Flash), the check
|
|
runs in a subprocess with .venv_t5/ activated — same pattern as the
|
|
training and inference workers.
|
|
|
|
Args:
|
|
model_name: Model identifier (HF repo or local path)
|
|
hf_token: Optional HF token for accessing gated/private models
|
|
"""
|
|
# Models that need transformers 5.x must be checked in a subprocess
|
|
# because AutoConfig in the main process (transformers 4.57.x) doesn't
|
|
# recognize their architectures.
|
|
from utils.transformers_version import needs_transformers_5
|
|
|
|
if needs_transformers_5(model_name):
|
|
logger.info(
|
|
"Model '%s' needs transformers 5.x — checking vision via subprocess",
|
|
model_name,
|
|
)
|
|
return _is_vision_model_subprocess(model_name, hf_token = hf_token)
|
|
|
|
try:
|
|
config = load_model_config(model_name, use_auth = True, token = hf_token)
|
|
|
|
# Exclude audio-only models that share ForConditionalGeneration suffix
|
|
# (e.g. CsmForConditionalGeneration, WhisperForConditionalGeneration)
|
|
_audio_only_model_types = {"csm", "whisper"}
|
|
model_type = getattr(config, "model_type", None)
|
|
if model_type in _audio_only_model_types:
|
|
return False
|
|
|
|
# Check 1: Architecture class name patterns
|
|
if hasattr(config, "architectures"):
|
|
is_vlm = any(x.endswith(_VLM_ARCH_SUFFIXES) for x in config.architectures)
|
|
if is_vlm:
|
|
logger.info(
|
|
f"Model {model_name} detected as VLM: architecture {config.architectures}"
|
|
)
|
|
return True
|
|
|
|
# Check 2: Has vision_config (most VLMs: LLaVA, Gemma-3, Qwen2-VL, etc.)
|
|
if hasattr(config, "vision_config"):
|
|
logger.info(f"Model {model_name} detected as VLM: has vision_config")
|
|
return True
|
|
|
|
# Check 3: Has img_processor (Phi-3.5 Vision uses this instead of vision_config)
|
|
if hasattr(config, "img_processor"):
|
|
logger.info(f"Model {model_name} detected as VLM: has img_processor")
|
|
return True
|
|
|
|
# Check 4: Has image_token_index (common in VLMs for image placeholder tokens)
|
|
if hasattr(config, "image_token_index"):
|
|
logger.info(f"Model {model_name} detected as VLM: has image_token_index")
|
|
return True
|
|
|
|
# Check 5: Known VLM model_type values that may not match above checks
|
|
if hasattr(config, "model_type"):
|
|
if config.model_type in _VLM_MODEL_TYPES:
|
|
logger.info(
|
|
f"Model {model_name} detected as VLM: model_type={config.model_type}"
|
|
)
|
|
return True
|
|
|
|
return False
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Could not determine if {model_name} is vision model: {e}")
|
|
return False
|
|
|
|
|
|
VALID_AUDIO_TYPES = ("snac", "csm", "bicodec", "dac", "whisper", "audio_vlm")
|
|
|
|
# Cache detection results per session to avoid repeated API calls
|
|
_audio_detection_cache: Dict[str, Optional[str]] = {}
|
|
|
|
# Tokenizer token patterns → audio_type (all 6 types detected from tokenizer_config.json)
|
|
_AUDIO_TOKEN_PATTERNS = {
|
|
"csm": lambda tokens: "<|AUDIO|>" in tokens and "<|audio_eos|>" in tokens,
|
|
"whisper": lambda tokens: "<|startoftranscript|>" in tokens,
|
|
"audio_vlm": lambda tokens: "<audio_soft_token>" in tokens,
|
|
"bicodec": lambda tokens: any(t.startswith("<|bicodec_") for t in tokens),
|
|
"dac": lambda tokens: "<|audio_start|>" in tokens and "<|audio_end|>" in tokens,
|
|
"snac": lambda tokens: sum(1 for t in tokens if t.startswith("<custom_token_"))
|
|
> 10000,
|
|
}
|
|
|
|
|
|
def detect_audio_type(model_name: str, hf_token: Optional[str] = None) -> Optional[str]:
|
|
"""
|
|
Dynamically detect if a model is an audio model and return its type.
|
|
|
|
Fully dynamic — works for any model, not just known ones.
|
|
Uses tokenizer_config.json special tokens to detect all 6 audio types.
|
|
|
|
Returns: audio_type string ('snac', 'csm', 'bicodec', 'dac', 'whisper', 'audio_vlm') or None.
|
|
"""
|
|
if model_name in _audio_detection_cache:
|
|
return _audio_detection_cache[model_name]
|
|
|
|
result = _detect_audio_from_tokenizer(model_name, hf_token)
|
|
|
|
_audio_detection_cache[model_name] = result
|
|
if result:
|
|
logger.info(f"Model {model_name} detected as audio model: audio_type={result}")
|
|
return result
|
|
|
|
|
|
def _detect_audio_from_tokenizer(
|
|
model_name: str, hf_token: Optional[str] = None
|
|
) -> Optional[str]:
|
|
"""Detect audio type from tokenizer special tokens (for LLM-based audio models).
|
|
|
|
First checks local HF cache, then fetches tokenizer_config.json from HuggingFace.
|
|
Checks added_tokens_decoder for distinctive patterns.
|
|
"""
|
|
|
|
def _check_token_patterns(tok_config: dict) -> Optional[str]:
|
|
added = tok_config.get("added_tokens_decoder", {})
|
|
if not added:
|
|
return None
|
|
token_contents = [v.get("content", "") for v in added.values()]
|
|
for audio_type, check_fn in _AUDIO_TOKEN_PATTERNS.items():
|
|
if check_fn(token_contents):
|
|
return audio_type
|
|
return None
|
|
|
|
# 1) Check local HF cache first (works for gated/offline models)
|
|
try:
|
|
from huggingface_hub.constants import HF_HUB_CACHE
|
|
|
|
cache_dir = Path(HF_HUB_CACHE)
|
|
repo_dir_name = f"models--{model_name.replace('/', '--')}"
|
|
repo_dir = cache_dir / repo_dir_name
|
|
if repo_dir.exists():
|
|
snapshots_dir = repo_dir / "snapshots"
|
|
if snapshots_dir.exists():
|
|
for snapshot in snapshots_dir.iterdir():
|
|
for tok_path in [
|
|
"tokenizer_config.json",
|
|
"LLM/tokenizer_config.json",
|
|
]:
|
|
tok_file = snapshot / tok_path
|
|
if tok_file.exists():
|
|
tok_config = json.loads(tok_file.read_text())
|
|
result = _check_token_patterns(tok_config)
|
|
if result:
|
|
return result
|
|
except Exception as e:
|
|
logger.debug(f"Could not check local cache for {model_name}: {e}")
|
|
|
|
# 2) Fall back to HuggingFace API
|
|
try:
|
|
import requests
|
|
import os
|
|
|
|
paths_to_try = ["tokenizer_config.json", "LLM/tokenizer_config.json"]
|
|
# Use provided token, or fall back to env
|
|
token = hf_token or os.environ.get("HF_TOKEN")
|
|
headers = {}
|
|
if token:
|
|
headers["Authorization"] = f"Bearer {token}"
|
|
|
|
for tok_path in paths_to_try:
|
|
url = f"https://huggingface.co/{model_name}/resolve/main/{tok_path}"
|
|
resp = requests.get(url, headers = headers, timeout = 15)
|
|
if not resp.ok:
|
|
continue
|
|
|
|
tok_config = resp.json()
|
|
result = _check_token_patterns(tok_config)
|
|
if result:
|
|
return result
|
|
|
|
return None
|
|
except Exception as e:
|
|
logger.debug(
|
|
f"Could not detect audio type from tokenizer for {model_name}: {e}"
|
|
)
|
|
return None
|
|
|
|
|
|
def is_audio_input_type(audio_type: Optional[str]) -> bool:
|
|
"""Check if an audio_type accepts audio input (ASR/speech understanding).
|
|
|
|
Whisper (ASR) and audio_vlm (Gemma3n) accept audio input.
|
|
"""
|
|
return audio_type in ("whisper", "audio_vlm")
|
|
|
|
|
|
def _is_mmproj(filename: str) -> bool:
|
|
"""Check if a GGUF filename is a vision projection (mmproj) file."""
|
|
return "mmproj" in filename.lower()
|
|
|
|
|
|
def detect_mmproj_file(path: str) -> Optional[str]:
|
|
"""
|
|
Find the mmproj (vision projection) GGUF file in a directory.
|
|
|
|
Args:
|
|
path: Directory to search — or a .gguf file (uses its parent dir).
|
|
|
|
Returns:
|
|
Full path to the mmproj .gguf file, or None if not found.
|
|
"""
|
|
p = Path(path)
|
|
search_dir = p.parent if p.is_file() else p
|
|
if not search_dir.is_dir():
|
|
return None
|
|
|
|
for f in search_dir.glob("*.gguf"):
|
|
if _is_mmproj(f.name):
|
|
return str(f.resolve())
|
|
return None
|
|
|
|
|
|
def detect_gguf_model(path: str) -> Optional[str]:
|
|
"""
|
|
Check if the given local path is or contains a GGUF model file.
|
|
|
|
Handles two cases:
|
|
1. path is a direct .gguf file path
|
|
2. path is a directory containing .gguf files
|
|
|
|
Skips mmproj (vision projection) files — those must be passed via
|
|
``--mmproj``, not ``-m``. Use :func:`detect_mmproj_file` instead.
|
|
|
|
Returns the full path to the .gguf file if found, None otherwise.
|
|
For HuggingFace repo detection, use detect_gguf_model_remote() instead.
|
|
"""
|
|
p = Path(path)
|
|
|
|
# Case 1: direct .gguf file
|
|
if p.suffix == ".gguf" and p.is_file():
|
|
if _is_mmproj(p.name):
|
|
return None
|
|
return str(p.resolve())
|
|
|
|
# Case 2: directory containing .gguf files (skip mmproj)
|
|
if p.is_dir():
|
|
gguf_files = sorted(
|
|
(f for f in p.glob("*.gguf") if not _is_mmproj(f.name)),
|
|
key = lambda f: f.stat().st_size,
|
|
reverse = True,
|
|
)
|
|
if gguf_files:
|
|
return str(gguf_files[0].resolve())
|
|
|
|
return None
|
|
|
|
|
|
# Preferred GGUF quantization levels, in descending priority.
|
|
# Q4_K_M is a good default: small, fast, acceptable quality.
|
|
# UD (Unsloth Dynamic) variants are always preferred over standard quants
|
|
# because they provide better quality per bit. If the repo has no UD variants
|
|
# (e.g., bartowski repos), the standard quants are used as fallback.
|
|
# Ordered by best size/quality tradeoff, not raw quality.
|
|
_GGUF_QUANT_PREFERENCE = [
|
|
# UD variants (best quality per bit) -- Q4 is the sweet spot
|
|
"UD-Q4_K_XL",
|
|
"UD-Q4_K_L",
|
|
"UD-Q5_K_XL",
|
|
"UD-Q3_K_XL",
|
|
"UD-Q6_K_XL",
|
|
"UD-Q6_K_S",
|
|
"UD-Q8_K_XL",
|
|
"UD-Q2_K_XL",
|
|
"UD-IQ4_NL",
|
|
"UD-IQ4_XS",
|
|
"UD-IQ3_S",
|
|
"UD-IQ3_XXS",
|
|
"UD-IQ2_M",
|
|
"UD-IQ2_XXS",
|
|
"UD-IQ1_M",
|
|
"UD-IQ1_S",
|
|
# Standard quants (fallback for non-Unsloth repos)
|
|
"Q4_K_M",
|
|
"Q4_K_S",
|
|
"Q5_K_M",
|
|
"Q5_K_S",
|
|
"Q6_K",
|
|
"Q8_0",
|
|
"Q3_K_M",
|
|
"Q3_K_L",
|
|
"Q3_K_S",
|
|
"Q2_K",
|
|
"Q2_K_L",
|
|
"IQ4_NL",
|
|
"IQ4_XS",
|
|
"IQ3_M",
|
|
"IQ3_XXS",
|
|
"IQ2_M",
|
|
"IQ1_M",
|
|
"F16",
|
|
"BF16",
|
|
"F32",
|
|
]
|
|
|
|
|
|
def _pick_best_gguf(filenames: list[str]) -> Optional[str]:
|
|
"""
|
|
Pick the best GGUF file from a list of filenames.
|
|
|
|
Prefers quantization levels in _GGUF_QUANT_PREFERENCE order.
|
|
Falls back to the first .gguf file found.
|
|
"""
|
|
gguf_files = [f for f in filenames if f.endswith(".gguf")]
|
|
if not gguf_files:
|
|
return None
|
|
|
|
# Try preferred quantization levels
|
|
for quant in _GGUF_QUANT_PREFERENCE:
|
|
for f in gguf_files:
|
|
if quant in f:
|
|
return f
|
|
|
|
# Fallback: first GGUF file
|
|
return gguf_files[0]
|
|
|
|
|
|
@dataclass
|
|
class GgufVariantInfo:
|
|
"""A single GGUF quantization variant from a HuggingFace repo."""
|
|
|
|
filename: str # e.g., "gemma-3-4b-it-Q4_K_M.gguf"
|
|
quant: str # e.g., "Q4_K_M" (extracted from filename)
|
|
size_bytes: int # file size
|
|
|
|
|
|
def _extract_quant_label(filename: str) -> str:
|
|
"""
|
|
Extract quantization label like Q4_K_M, IQ4_XS, BF16 from a GGUF filename.
|
|
|
|
Examples:
|
|
"gemma-3-4b-it-Q4_K_M.gguf" → "Q4_K_M"
|
|
"model-IQ4_NL.gguf" → "IQ4_NL"
|
|
"model-BF16.gguf" → "BF16"
|
|
"model-UD-IQ1_S.gguf" → "UD-IQ1_S"
|
|
"model-UD-TQ1_0.gguf" → "UD-TQ1_0"
|
|
"MXFP4_MOE/model-MXFP4_MOE-0001.gguf"→ "MXFP4_MOE"
|
|
"""
|
|
import re
|
|
|
|
# Use only the basename (rfilename may include directory)
|
|
basename = filename.rsplit("/", 1)[-1]
|
|
# Strip .gguf and any shard suffix (-00001-of-00010)
|
|
stem = re.sub(r"-\d{3,}-of-\d{3,}", "", basename.rsplit(".", 1)[0])
|
|
# Match known quantization patterns
|
|
match = re.search(
|
|
r"(UD-)?" # Optional UD- prefix (Ultra Discrete)
|
|
r"(MXFP[0-9]+(?:_[A-Z0-9]+)*" # MXFP variants: MXFP4, MXFP4_MOE
|
|
r"|IQ[0-9]+_[A-Z]+(?:_[A-Z0-9]+)?" # IQ variants: IQ4_XS, IQ4_NL, IQ1_S
|
|
r"|TQ[0-9]+_[0-9]+" # Ternary quant: TQ1_0, TQ2_0
|
|
r"|Q[0-9]+_K_[A-Z]+" # K-quant: Q4_K_M, Q3_K_S
|
|
r"|Q[0-9]+_[0-9]+" # Standard: Q8_0, Q5_1
|
|
r"|Q[0-9]+_K" # Short K-quant: Q6_K
|
|
r"|BF16|F16|F32)", # Full precision
|
|
stem,
|
|
re.IGNORECASE,
|
|
)
|
|
if match:
|
|
prefix = match.group(1) or ""
|
|
return f"{prefix}{match.group(2)}"
|
|
# Fallback: last segment after hyphen
|
|
return stem.split("-")[-1]
|
|
|
|
|
|
def list_gguf_variants(
|
|
repo_id: str,
|
|
hf_token: Optional[str] = None,
|
|
) -> tuple[list[GgufVariantInfo], bool]:
|
|
"""
|
|
List all GGUF quantization variants in a HuggingFace repo.
|
|
|
|
Separates main model files from mmproj (vision projection) files.
|
|
The presence of mmproj files indicates a vision-capable model.
|
|
|
|
Returns:
|
|
(variants, has_vision): list of non-mmproj GGUF variants + vision flag.
|
|
"""
|
|
from huggingface_hub import model_info as hf_model_info
|
|
|
|
info = hf_model_info(repo_id, token = hf_token, files_metadata = True)
|
|
variants: list[GgufVariantInfo] = []
|
|
has_vision = False
|
|
|
|
quant_totals: dict[str, int] = {} # quant -> total bytes
|
|
quant_first_file: dict[str, str] = {} # quant -> first filename (for display)
|
|
|
|
for sibling in info.siblings:
|
|
fname = sibling.rfilename
|
|
if not fname.endswith(".gguf"):
|
|
continue
|
|
size = sibling.size or 0
|
|
|
|
# mmproj files are vision projection models, not main model files
|
|
if "mmproj" in fname.lower():
|
|
has_vision = True
|
|
continue
|
|
|
|
quant = _extract_quant_label(fname)
|
|
quant_totals[quant] = quant_totals.get(quant, 0) + size
|
|
if quant not in quant_first_file:
|
|
quant_first_file[quant] = fname
|
|
|
|
for quant, total_size in quant_totals.items():
|
|
variants.append(
|
|
GgufVariantInfo(
|
|
filename = quant_first_file[quant],
|
|
quant = quant,
|
|
size_bytes = total_size,
|
|
)
|
|
)
|
|
|
|
# Sort by size descending (largest = best quality first).
|
|
# Recommended pinning and OOM demotion are handled client-side
|
|
# where GPU VRAM info is available.
|
|
variants.sort(key = lambda v: -v.size_bytes)
|
|
|
|
return variants, has_vision
|
|
|
|
|
|
def detect_gguf_model_remote(
|
|
repo_id: str,
|
|
hf_token: Optional[str] = None,
|
|
) -> Optional[str]:
|
|
"""
|
|
Check if a HuggingFace repo contains GGUF files.
|
|
|
|
Returns the filename of the best GGUF file in the repo, or None.
|
|
"""
|
|
try:
|
|
from huggingface_hub import model_info as hf_model_info
|
|
|
|
info = hf_model_info(repo_id, token = hf_token)
|
|
repo_files = [s.rfilename for s in info.siblings]
|
|
return _pick_best_gguf(repo_files)
|
|
except Exception as e:
|
|
logger.debug(f"Could not check GGUF files for '{repo_id}': {e}")
|
|
return None
|
|
|
|
|
|
def download_gguf_file(
|
|
repo_id: str,
|
|
filename: str,
|
|
hf_token: Optional[str] = None,
|
|
) -> str:
|
|
"""
|
|
Download a specific GGUF file from a HuggingFace repo.
|
|
|
|
Returns the local path to the downloaded file.
|
|
"""
|
|
from huggingface_hub import hf_hub_download
|
|
|
|
local_path = hf_hub_download(
|
|
repo_id = repo_id,
|
|
filename = filename,
|
|
token = hf_token,
|
|
)
|
|
return local_path
|
|
|
|
|
|
# Cache embedding detection results per session to avoid repeated HF API calls
|
|
_embedding_detection_cache: Dict[tuple, bool] = {}
|
|
|
|
|
|
def is_embedding_model(model_name: str, hf_token: Optional[str] = None) -> bool:
|
|
"""
|
|
Detect embedding/sentence-transformer models using HuggingFace model metadata.
|
|
|
|
Uses a belt-and-suspenders approach combining three signals:
|
|
1. "sentence-transformers" in model tags
|
|
2. "feature-extraction" in model tags
|
|
3. pipeline_tag is "sentence-similarity" or "feature-extraction"
|
|
|
|
This catches all known embedding models including those like gte-modernbert
|
|
whose library_name is "transformers" rather than "sentence-transformers".
|
|
|
|
Args:
|
|
model_name: Model identifier (HF repo or local path)
|
|
hf_token: Optional HF token for accessing gated/private models
|
|
|
|
Returns:
|
|
True if the model is an embedding model, False otherwise.
|
|
Defaults to False for local paths or on errors.
|
|
"""
|
|
cache_key = (model_name, hf_token)
|
|
if cache_key in _embedding_detection_cache:
|
|
return _embedding_detection_cache[cache_key]
|
|
|
|
# Local paths: check for sentence-transformer marker file (modules.json)
|
|
if is_local_path(model_name):
|
|
local_dir = normalize_path(model_name)
|
|
is_emb = os.path.isfile(os.path.join(local_dir, "modules.json"))
|
|
_embedding_detection_cache[cache_key] = is_emb
|
|
return is_emb
|
|
|
|
try:
|
|
from huggingface_hub import model_info as hf_model_info
|
|
|
|
info = hf_model_info(model_name, token = hf_token)
|
|
tags = set(info.tags or [])
|
|
pipeline_tag = info.pipeline_tag or ""
|
|
|
|
is_emb = (
|
|
"sentence-transformers" in tags
|
|
or "feature-extraction" in tags
|
|
or pipeline_tag in ("sentence-similarity", "feature-extraction")
|
|
)
|
|
|
|
_embedding_detection_cache[cache_key] = is_emb
|
|
if is_emb:
|
|
logger.info(
|
|
f"Model {model_name} detected as embedding model: "
|
|
f"pipeline_tag={pipeline_tag}, "
|
|
f"sentence-transformers in tags={('sentence-transformers' in tags)}, "
|
|
f"feature-extraction in tags={('feature-extraction' in tags)}"
|
|
)
|
|
return is_emb
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Could not determine if {model_name} is embedding model: {e}")
|
|
_embedding_detection_cache[cache_key] = False
|
|
return False
|
|
|
|
|
|
def scan_trained_loras(outputs_dir: str = str(outputs_root())) -> 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 = resolve_output_dir(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 scan_exported_models(
|
|
exports_dir: str = str(exports_root()),
|
|
) -> List[Tuple[str, str, str, Optional[str]]]:
|
|
"""
|
|
Scan exports folder for exported models (merged, LoRA, GGUF).
|
|
|
|
Supports two directory layouts:
|
|
- Two-level: {run}/{checkpoint}/ (merged & LoRA exports)
|
|
- Flat: {name}-finetune-gguf/ (GGUF exports)
|
|
|
|
Returns:
|
|
List of tuples: [(display_name, model_path, export_type, base_model), ...]
|
|
export_type: "lora" | "merged" | "gguf"
|
|
"""
|
|
results = []
|
|
exports_path = resolve_export_dir(exports_dir)
|
|
|
|
if not exports_path.exists():
|
|
return results
|
|
|
|
try:
|
|
for run_dir in exports_path.iterdir():
|
|
if not run_dir.is_dir():
|
|
continue
|
|
|
|
# Check for flat GGUF export (e.g. exports/gemma-3-4b-it-finetune-gguf/)
|
|
# Filter out mmproj (vision projection) files — they aren't loadable as main models
|
|
gguf_files = [f for f in run_dir.glob("*.gguf") if not _is_mmproj(f.name)]
|
|
if gguf_files:
|
|
base_model = None
|
|
export_meta = run_dir / "export_metadata.json"
|
|
try:
|
|
if export_meta.exists():
|
|
meta = json.loads(export_meta.read_text())
|
|
base_model = meta.get("base_model")
|
|
except Exception:
|
|
pass
|
|
|
|
display_name = run_dir.name
|
|
model_path = str(gguf_files[0]) # path to the .gguf file
|
|
results.append((display_name, model_path, "gguf", base_model))
|
|
logger.debug(f"Found GGUF export: {display_name}")
|
|
continue
|
|
|
|
# Two-level: {run}/{checkpoint}/
|
|
for checkpoint_dir in run_dir.iterdir():
|
|
if not checkpoint_dir.is_dir():
|
|
continue
|
|
|
|
adapter_config = checkpoint_dir / "adapter_config.json"
|
|
config_file = checkpoint_dir / "config.json"
|
|
has_weights = any(checkpoint_dir.glob("*.safetensors")) or any(
|
|
checkpoint_dir.glob("*.bin")
|
|
)
|
|
has_gguf = any(checkpoint_dir.glob("*.gguf"))
|
|
|
|
base_model = None
|
|
export_type = None
|
|
|
|
if adapter_config.exists():
|
|
export_type = "lora"
|
|
try:
|
|
cfg = json.loads(adapter_config.read_text())
|
|
base_model = cfg.get("base_model_name_or_path")
|
|
except Exception:
|
|
pass
|
|
elif config_file.exists() and has_weights:
|
|
export_type = "merged"
|
|
export_meta = checkpoint_dir / "export_metadata.json"
|
|
try:
|
|
if export_meta.exists():
|
|
meta = json.loads(export_meta.read_text())
|
|
base_model = meta.get("base_model")
|
|
except Exception:
|
|
pass
|
|
elif has_gguf:
|
|
export_type = "gguf"
|
|
gguf_list = list(checkpoint_dir.glob("*.gguf"))
|
|
# Check checkpoint_dir first, then fall back to parent run_dir
|
|
# (export.py writes metadata to the top-level export directory)
|
|
for meta_dir in (checkpoint_dir, run_dir):
|
|
export_meta = meta_dir / "export_metadata.json"
|
|
try:
|
|
if export_meta.exists():
|
|
meta = json.loads(export_meta.read_text())
|
|
base_model = meta.get("base_model")
|
|
if base_model:
|
|
break
|
|
except Exception:
|
|
pass
|
|
|
|
display_name = f"{run_dir.name} / {checkpoint_dir.name}"
|
|
model_path = str(gguf_list[0]) if gguf_list else str(checkpoint_dir)
|
|
results.append((display_name, model_path, export_type, base_model))
|
|
logger.debug(f"Found GGUF export: {display_name}")
|
|
continue
|
|
else:
|
|
continue
|
|
|
|
# Fallback: read base model from the original training run's
|
|
# adapter_config.json in ./outputs/{run_name}/
|
|
if not base_model:
|
|
outputs_adapter_cfg = (
|
|
resolve_output_dir(run_dir.name) / "adapter_config.json"
|
|
)
|
|
try:
|
|
if outputs_adapter_cfg.exists():
|
|
cfg = json.loads(outputs_adapter_cfg.read_text())
|
|
base_model = cfg.get("base_model_name_or_path")
|
|
except Exception:
|
|
pass
|
|
|
|
display_name = f"{run_dir.name} / {checkpoint_dir.name}"
|
|
model_path = str(checkpoint_dir)
|
|
results.append((display_name, model_path, export_type, base_model))
|
|
logger.debug(f"Found exported model: {display_name} ({export_type})")
|
|
|
|
results.sort(key = lambda x: Path(x[1]).stat().st_mtime, reverse = True)
|
|
logger.info(f"Found {len(results)} exported models in {exports_dir}")
|
|
return results
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error scanning exports 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
|
|
|
|
|
|
# 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.parent
|
|
defaults_dir = script_dir / "assets" / "configs" / "model_defaults"
|
|
|
|
# First, check if model is in the mapping
|
|
if model_name.lower() in _REVERSE_MODEL_MAPPING:
|
|
canonical_file = _REVERSE_MODEL_MAPPING[model_name.lower()]
|
|
# 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
|
|
|
|
# If model_name is a local path (e.g. /home/.../Spark-TTS-0.5B/LLM from
|
|
# adapter_config.json), try matching the last 1-2 path components against
|
|
# the registry (e.g. "Spark-TTS-0.5B/LLM").
|
|
if model_name not in _REVERSE_MODEL_MAPPING and (
|
|
model_name.startswith("/") or model_name.startswith(".")
|
|
):
|
|
parts = Path(model_name).parts
|
|
for depth in [2, 1]:
|
|
if len(parts) >= depth:
|
|
suffix = "/".join(parts[-depth:])
|
|
if suffix in _REVERSE_MODEL_MAPPING:
|
|
canonical_file = _REVERSE_MODEL_MAPPING[suffix]
|
|
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 path suffix '{suffix}')"
|
|
)
|
|
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?
|
|
is_gguf: bool = False # Is this a GGUF model?
|
|
is_audio: bool = False # Is this a TTS audio model?
|
|
audio_type: Optional[str] = (
|
|
None # Audio codec type: 'snac', 'csm', 'bicodec', 'dac'
|
|
)
|
|
has_audio_input: bool = False # Accepts audio input (ASR/speech understanding)
|
|
gguf_file: Optional[str] = None # Full path to the .gguf file (local mode)
|
|
gguf_mmproj_file: Optional[str] = (
|
|
None # Full path to the mmproj .gguf file (vision projection)
|
|
)
|
|
gguf_hf_repo: Optional[str] = (
|
|
None # HF repo ID for -hf mode (e.g. "unsloth/gemma-3-4b-it-GGUF")
|
|
)
|
|
gguf_variant: Optional[str] = None # Quantization variant (e.g. "Q4_K_M")
|
|
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)
|
|
|
|
# Check if base model is audio
|
|
audio_type = detect_audio_type(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,
|
|
is_audio = audio_type is not None and audio_type != "audio_vlm",
|
|
audio_type = audio_type,
|
|
has_audio_input = is_audio_input_type(audio_type),
|
|
base_model = base_model,
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating ModelConfig from LoRA path: {e}")
|
|
return None
|
|
|
|
@classmethod
|
|
def from_identifier(
|
|
cls,
|
|
model_id: str,
|
|
hf_token: Optional[str] = None,
|
|
is_lora: bool = False,
|
|
gguf_variant: Optional[str] = None,
|
|
) -> Optional["ModelConfig"]:
|
|
"""
|
|
Create ModelConfig from a clean model identifier.
|
|
|
|
For FastAPI routes where the frontend sends sanitized model paths.
|
|
No Gradio dropdown parsing - expects clean identifiers like:
|
|
- "unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit"
|
|
- "./outputs/my_lora_adapter"
|
|
- "/absolute/path/to/model"
|
|
|
|
Args:
|
|
model_id: Clean model identifier (HF repo name or local path)
|
|
hf_token: Optional HF token for vision detection on gated models
|
|
is_lora: Whether this is a LoRA adapter
|
|
gguf_variant: Optional GGUF quantization variant (e.g. "Q4_K_M").
|
|
For remote GGUF repos, specifies which quant to load via -hf.
|
|
If None, auto-selects using _pick_best_gguf().
|
|
|
|
Returns:
|
|
ModelConfig or None if configuration cannot be created
|
|
"""
|
|
if not model_id or not model_id.strip():
|
|
return None
|
|
|
|
identifier = model_id.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
|
|
|
|
# Enforce lowercase for remote Hugging Face identifiers to prevent cache duplication
|
|
# Hugging Face Hub APIs are case-insensitive remotely, but case-sensitive locally (repo_folder_name).
|
|
if not is_local:
|
|
identifier = identifier.lower()
|
|
path = path.lower()
|
|
|
|
# Auto-detect GGUF models (check before LoRA/vision detection)
|
|
if is_local:
|
|
gguf_file = detect_gguf_model(path)
|
|
if gguf_file:
|
|
display_name = Path(gguf_file).stem
|
|
logger.info(f"Detected local GGUF model: {gguf_file}")
|
|
|
|
# Detect vision: check if base model is vision, then look for mmproj
|
|
mmproj_file = None
|
|
gguf_is_vision = False
|
|
gguf_dir = Path(gguf_file).parent
|
|
|
|
# Determine if this is a vision model from export metadata
|
|
base_is_vision = False
|
|
meta_path = gguf_dir / "export_metadata.json"
|
|
if meta_path.exists():
|
|
try:
|
|
meta = json.loads(meta_path.read_text())
|
|
base = meta.get("base_model")
|
|
if base and is_vision_model(base, hf_token = hf_token):
|
|
base_is_vision = True
|
|
logger.info(f"GGUF base model '{base}' is a vision model")
|
|
except Exception as e:
|
|
logger.debug(f"Could not read export metadata: {e}")
|
|
|
|
# If vision (or mmproj happens to exist), find the mmproj file
|
|
mmproj_file = detect_mmproj_file(gguf_file)
|
|
if mmproj_file:
|
|
gguf_is_vision = True
|
|
logger.info(f"Detected mmproj for vision: {mmproj_file}")
|
|
elif base_is_vision:
|
|
logger.warning(
|
|
f"Base model is vision but no mmproj file found in {gguf_dir}"
|
|
)
|
|
|
|
return cls(
|
|
identifier = identifier,
|
|
display_name = display_name,
|
|
path = path,
|
|
is_local = True,
|
|
is_cached = True,
|
|
is_vision = gguf_is_vision,
|
|
is_lora = False,
|
|
is_gguf = True,
|
|
gguf_file = gguf_file,
|
|
gguf_mmproj_file = mmproj_file,
|
|
)
|
|
else:
|
|
# Check if the HF repo contains GGUF files
|
|
gguf_filename = detect_gguf_model_remote(identifier, hf_token = hf_token)
|
|
if gguf_filename:
|
|
# Preflight: verify llama-server binary exists BEFORE user waits
|
|
# for a multi-GB download that llama-server handles natively
|
|
from core.inference.llama_cpp import LlamaCppBackend
|
|
|
|
if not LlamaCppBackend._find_llama_server_binary():
|
|
raise RuntimeError(
|
|
"llama-server binary not found — cannot load GGUF models. "
|
|
"Run setup.sh to build it, or set LLAMA_SERVER_PATH."
|
|
)
|
|
|
|
# Use list_gguf_variants() to detect vision & resolve variant
|
|
variants, has_vision = list_gguf_variants(identifier, hf_token = hf_token)
|
|
variant = gguf_variant
|
|
if not variant:
|
|
# Auto-select best quantization
|
|
variant_filenames = [v.filename for v in variants]
|
|
best = _pick_best_gguf(variant_filenames)
|
|
if best:
|
|
variant = _extract_quant_label(best)
|
|
else:
|
|
variant = "Q4_K_M" # Fallback — llama-server's own default
|
|
|
|
display_name = f"{identifier.split('/')[-1]} ({variant})"
|
|
logger.info(
|
|
f"Detected remote GGUF repo '{identifier}', "
|
|
f"variant={variant}, vision={has_vision}"
|
|
)
|
|
return cls(
|
|
identifier = identifier,
|
|
display_name = display_name,
|
|
path = identifier,
|
|
is_local = False,
|
|
is_cached = False,
|
|
is_vision = has_vision,
|
|
is_lora = False,
|
|
is_gguf = True,
|
|
gguf_file = None,
|
|
gguf_hf_repo = identifier,
|
|
gguf_variant = variant,
|
|
)
|
|
|
|
# Auto-detect LoRA for local paths (check adapter_config.json on disk)
|
|
if not is_lora and is_local:
|
|
detected_base = get_base_model_from_lora(path)
|
|
if detected_base:
|
|
is_lora = True
|
|
logger.info(
|
|
f"Auto-detected local LoRA adapter at '{path}' (base: {detected_base})"
|
|
)
|
|
|
|
# Auto-detect LoRA for remote HF models (check repo file listing)
|
|
if not is_lora and not is_local:
|
|
try:
|
|
from huggingface_hub import model_info as hf_model_info
|
|
|
|
info = hf_model_info(identifier, token = hf_token)
|
|
repo_files = [s.rfilename for s in info.siblings]
|
|
if "adapter_config.json" in repo_files:
|
|
is_lora = True
|
|
logger.info(f"Auto-detected remote LoRA adapter: '{identifier}'")
|
|
except Exception as e:
|
|
logger.debug(
|
|
f"Could not check remote LoRA status for '{identifier}': {e}"
|
|
)
|
|
|
|
# Handle LoRA adapters
|
|
base_model = None
|
|
if is_lora:
|
|
if is_local:
|
|
# Local LoRA: read adapter_config.json from disk
|
|
base_model = get_base_model_from_lora(path)
|
|
else:
|
|
# Remote LoRA: download adapter_config.json from HF
|
|
try:
|
|
from huggingface_hub import hf_hub_download
|
|
|
|
config_path = hf_hub_download(
|
|
identifier, "adapter_config.json", token = hf_token
|
|
)
|
|
with open(config_path, "r") as f:
|
|
adapter_config = json.load(f)
|
|
base_model = adapter_config.get("base_model_name_or_path")
|
|
if base_model:
|
|
logger.info(f"Resolved remote LoRA base model: '{base_model}'")
|
|
except Exception as e:
|
|
logger.warning(
|
|
f"Could not download adapter_config.json for '{identifier}': {e}"
|
|
)
|
|
|
|
if not base_model:
|
|
logger.warning(f"Could not determine base model for LoRA '{path}'")
|
|
return None
|
|
check_model = base_model
|
|
else:
|
|
check_model = identifier
|
|
|
|
vision = is_vision_model(check_model, hf_token = hf_token)
|
|
audio_type_val = detect_audio_type(check_model, hf_token = hf_token)
|
|
has_audio_in = is_audio_input_type(audio_type_val)
|
|
|
|
display_name = Path(path).name if is_local else identifier.split("/")[-1]
|
|
|
|
return cls(
|
|
identifier = identifier,
|
|
display_name = display_name,
|
|
path = path,
|
|
is_local = is_local,
|
|
is_cached = is_model_cached(identifier) if not is_local else True,
|
|
is_vision = vision,
|
|
is_lora = is_lora,
|
|
is_audio = audio_type_val is not None and audio_type_val != "audio_vlm",
|
|
audio_type = audio_type_val,
|
|
has_audio_input = has_audio_in,
|
|
base_model = base_model,
|
|
)
|
|
|
|
@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
|
|
)
|