df6b5a57d9* fix: handle case-variant GGUF cache hits for unsloth start * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * gguf cache: keep split shards co-located and isolate cache tests properly When a cached main shard was reused from an older snapshot, the extra shards were resolved independently and could come from a different snapshot dir (or a fresh download into the current ref), leaving llama.cpp unable to load a multi-shard GGUF whose pieces are split across directories. Only reuse a cached main shard when every sibling shard sits in the same snapshot; otherwise fetch the whole set together so they stay co-located. Also patch huggingface_hub.constants.HF_HUB_CACHE (not just the HF_HUB_CACHE env var) in the two cache tests that seeded a temp cache: the snapshot lookup reads the module constant, so the env-only override let the real cache leak in and skip an asserted download. * Do not let a companion-only cache snapshot shadow real GGUF variants When listing GGUF variants from the local HF cache, a newer snapshot may contain only a companion file (for example a vision projector fetched on demand) while the actual quant files live in an older snapshot. The prior scan returned the first snapshot whose vision flag was set, yielding an empty variant list and hiding the real quants. Keep scanning older snapshots for actual variants and carry the vision flag across snapshots. Also record the disk-space fallback variant's size in expected_sizes so the later cache-reuse probe can size-verify the fallback main shard instead of only checking for its existence. * Propagate cached repo casing to companions and preflight split co-location Two fixes to the case-variant GGUF cache reuse: - Resolve the requested repo id to its cached canonical casing once in load_model, up front, and pass it to the main GGUF and its companions (mmproj / MTP drafter). Previously only _download_gguf resolved the casing internally, so a case-variant request loaded the main file from the canonical cache dir while the companions kept the requested casing and missed the cached vision projector / drafter offline. Extracted the resolution into a shared _resolve_repo_id_casing helper. - Apply the split-shard co-location check in the disk-space preflight. When a split GGUF's shards are cached across different snapshots the whole set is refetched later, so counting them as cached made the preflight read 0 bytes to download, skip the smaller-variant fallback, and then fail the full download on a low-disk machine. * Reuse a co-located split GGUF snapshot and fix split fallback size probe - When reusing a cached split GGUF, scan snapshots for one that holds the whole set co-located instead of taking the newest snapshot's first shard. A newer snapshot with only the first shard no longer shadows an older complete snapshot, so an already-cached split model is reused rather than refetched (which would fail offline). - The disk-space fallback records its size in expected_sizes only for a single-file fallback. _find_smallest_fitting_variant returns the whole variant size, so using it as the first shard's expected size rejected a valid cached first shard of a split fallback and forced a re-download. * Scan for a complete split snapshot in the preflight; require a loaded catalog hit - The disk-space preflight now uses the same co-located snapshot scan as the download path (_cached_colocated_split_main) instead of the newest-snapshot probe, so a newer snapshot holding only the first shard no longer masks an older complete one and trips the smaller-variant fallback for a fully cached split model. - _resolve_model only attaches to a /v1/models entry that is actually loaded (loaded != False). /v1/models also lists cached-but-unloaded catalog entries, and matching one by case skipped /api/inference/load and left the agent pointed at a model that is not resident. * Restrict cross-snapshot GGUF cache reuse to offline Reusing a same-name blob from an older or case-variant snapshot bypasses the Hub revision/etag check, so a repo that updates a GGUF in place could serve stale weights online. Gate the cross-snapshot and case-variant reuse (both the disk-space preflight accounting and the download path) on HF_HUB_OFFLINE. Online, hf_hub_download fetches the current revision and resumes a partial download, so the reuse is unnecessary there; offline it remains the resilience fallback. Marked the two reuse regression tests as the offline scenarios they represent and added an online test asserting a fresh fetch. * Harden offline cache reuse and hub-id detection Three follow-ups on the case-variant GGUF cache path: - Honor every truthy HF_HUB_OFFLINE spelling (1/true/yes/on), not just "1", when gating the cross-snapshot and case-variant cache reuse. With HF_HUB_OFFLINE=true the Hub calls are already offline, so the reuse must trigger or the cached GGUF fails to load; route both the preflight accounting and the download path through the same offline parse the rest of the backend uses. - Resolve mmproj/MTP companions from the actual cached snapshot when offline. resolve_cached_repo_id_case can keep a partial lower-case spelling when any dir exists under the requested casing, so an hf_hub_download on that casing misses the canonical companion; scan every case-variant snapshot and return the cached path. - Restrict the case-insensitive model-id match to syntactically valid hub ids (a single namespace/name over the HF charset). A server-side relative path such as models/Llama/Foo.gguf is no longer treated as a hub id, so it cannot casefold-match a differently cased path on a case-sensitive filesystem. This is host independent, unlike the local-existence probe which cannot see a server path. * Only casefold-match model ids against a loopback Studio A two-segment string like Models/Foo is indistinguishable from a hub id, and the local Path.exists() probe in _is_hub_model_id cannot see a path that exists only on a remote Studio host. So against a remote server, casefolding could attach to a distinct server-side path (Models/Foo vs models/foo) on a case-sensitive filesystem. Gate the case-insensitive match on is_loopback_url(base): only a local Studio, where the existence probe is authoritative, casefolds. For a remote Studio the match is exact and a case-mismatched request falls through to /api/inference/load, whose already-loaded dedup resolves it correctly. --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Daniel Han <danielhanchen@gmail.com> Co-authored-by: Wasim Yousef Said <wasimysdev@gmail.com>
2888 lines
107 KiB
Python
2888 lines
107 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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"""Model and LoRA configuration handling."""
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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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get_cache_path,
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resolve_cached_repo_id_case,
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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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from utils.models.gguf_metadata import (
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is_mmproj_by_metadata,
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pairing_score,
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read_gguf_general_metadata,
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)
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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 re
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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 hashlib
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import json
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import threading
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import yaml
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from utils.native_path_leases import child_env_without_native_path_secret
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from utils.subprocess_compat import (
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windows_hidden_subprocess_kwargs as _windows_hidden_subprocess_kwargs,
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)
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logger = get_logger(__name__)
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_OFFLINE_TRUE_VALUES = {"1", "true", "yes", "on"}
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def _env_offline() -> bool:
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"""True if an HF offline env var is truthy (canonical strip+lower parse, on/true/yes/1)."""
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return (
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os.environ.get("HF_HUB_OFFLINE", "").strip().lower() in _OFFLINE_TRUE_VALUES
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or os.environ.get("TRANSFORMERS_OFFLINE", "").strip().lower() in _OFFLINE_TRUE_VALUES
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)
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# ── Model size extraction ────────────────────────────────────
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import re as _re
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_MODEL_SIZE_RE = _re.compile(r"(?:^|[-_/])(\d+\.?\d*)\s*([bm])(?:$|[-_/])", _re.IGNORECASE)
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# MoE active-parameter pattern: "A3B", "A3.5B", etc.
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_ACTIVE_SIZE_RE = _re.compile(r"(?:^|[-_/])a(\d+\.?\d*)\s*([bm])(?:$|[-_/])", _re.IGNORECASE)
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# Gemma 3n/4 effective-parameter pattern: "E2B", "E4B" -- the runtime
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# footprint (MatFormer + per-layer embeddings), which is the size that
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# matters for size-gated policies like sub-3B speculative-decoding fallback.
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_EFFECTIVE_SIZE_RE = _re.compile(r"(?:^|[-_/])e(\d+\.?\d*)\s*([bm])(?:$|[-_/])", _re.IGNORECASE)
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def extract_model_size_b(model_id: str) -> float | None:
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"""Extract model size in billions from a model identifier.
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Prefers MoE active-parameter notation (e.g. ``A3B`` in
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``Qwen3.5-35B-A3B``), then Gemma effective-parameter notation
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(e.g. ``E2B``), over total params. Handles ``B`` (billions) and
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``M`` (millions) suffixes.
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"""
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mid = (model_id or "").lower()
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# First match wins, in priority order: active > effective > total.
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for pattern in (_ACTIVE_SIZE_RE, _EFFECTIVE_SIZE_RE, _MODEL_SIZE_RE):
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m = pattern.search(mid)
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if m:
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val = float(m.group(1))
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return val / 1000.0 if m.group(2).lower() == "m" else val
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return None
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# Maps equivalent model names to their canonical YAML config file.
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# Format: "canonical_model_name.yaml": [equivalent model names].
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# Canonical filename derives from the first model name in each list.
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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_gemma-4-31B-it.yaml": [
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"unsloth/gemma-4-31B-it",
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"google/gemma-4-31B-it",
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],
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"unsloth_gemma-4-26B-A4B-it.yaml": [
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"unsloth/gemma-4-26B-A4B-it",
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"google/gemma-4-26B-A4B-it",
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],
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"unsloth_gemma-4-E2B-it.yaml": [
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"unsloth/gemma-4-E2B-it",
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"google/gemma-4-E2B-it",
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],
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"unsloth_gemma-4-E4B-it.yaml": [
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"unsloth/gemma-4-E4B-it",
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"google/gemma-4-E4B-it",
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],
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"unsloth_gemma-4-31B.yaml": [
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"unsloth/gemma-4-31B",
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"google/gemma-4-31B",
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],
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"unsloth_gemma-4-26B-A4B.yaml": [
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"unsloth/gemma-4-26B-A4B",
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"google/gemma-4-26B-A4B",
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],
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"unsloth_gemma-4-E2B.yaml": [
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"unsloth/gemma-4-E2B",
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"google/gemma-4-E2B",
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],
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"unsloth_gemma-4-E4B.yaml": [
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"unsloth/gemma-4-E4B",
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"google/gemma-4-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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],
|
|
"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/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/Llama-OuteTTS-1.0-1B",
|
|
"unsloth/llama-outetts-1.0-1b",
|
|
"OuteAI/OuteTTS-1.0-0.6B",
|
|
"unsloth/OuteTTS-1.0-0.6B",
|
|
"unsloth/outetts-1.0-0.6b",
|
|
],
|
|
"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",
|
|
"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 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 = False,
|
|
local_files_only: bool = False,
|
|
):
|
|
"""Load model config with optional authentication control.
|
|
|
|
``trust_remote_code`` defaults to ``False``: capability detection and
|
|
metadata lookups must never execute a model repo's ``auto_map`` Python.
|
|
Deliberate remote-code loads pass the flag explicitly through
|
|
``FastLanguageModel.from_pretrained`` with the user's own consent.
|
|
|
|
``local_files_only`` keeps the config read on the local HF cache (offline
|
|
export), so an offline probe never blocks on the network.
|
|
"""
|
|
from transformers import AutoConfig
|
|
|
|
if token:
|
|
return AutoConfig.from_pretrained(
|
|
model_name,
|
|
trust_remote_code = trust_remote_code,
|
|
token = token,
|
|
local_files_only = local_files_only,
|
|
)
|
|
|
|
if not use_auth:
|
|
# No auth, for public model checks
|
|
with without_hf_auth():
|
|
return AutoConfig.from_pretrained(
|
|
model_name,
|
|
trust_remote_code = trust_remote_code,
|
|
token = None,
|
|
local_files_only = local_files_only,
|
|
)
|
|
|
|
# Default auth (cached tokens)
|
|
return AutoConfig.from_pretrained(
|
|
model_name,
|
|
trust_remote_code = trust_remote_code,
|
|
local_files_only = local_files_only,
|
|
)
|
|
|
|
|
|
# Detection sets come from the installed transformers registry, unioned with a
|
|
# small curated set of auto_map VLMs (DeepSeek-OCR, Kimi, phi3_v) whose arch is
|
|
# repo-defined and absent from the registry. ForConditionalGeneration is NOT a
|
|
# vision signal (overloaded across text/audio/vision); ForVisionText2Text is.
|
|
_VLM_ARCH_SUFFIXES = ("ForVisionText2Text",)
|
|
|
|
_CURATED_REMOTE_VLM_TYPES = frozenset(
|
|
{
|
|
"phi3_v",
|
|
"llava",
|
|
"llava_next",
|
|
"llava_onevision",
|
|
"internvl_chat",
|
|
"cogvlm2",
|
|
"minicpmv",
|
|
"gemma4",
|
|
"deepseek_vl_v2",
|
|
"kimi_k25",
|
|
}
|
|
)
|
|
|
|
# Fallbacks used only if the transformers registry import fails.
|
|
_FALLBACK_AUDIO_MODEL_TYPES = frozenset({"csm", "whisper"})
|
|
|
|
|
|
def _build_detection_sets():
|
|
"""Return (vlm_model_types, vlm_class_names, audio_model_types) from the
|
|
installed transformers registry, unioned with the curated repo-code VLM
|
|
set. Reads only static name dicts -- no model is loaded, no code runs.
|
|
Falls back to curated/hardcoded values if transformers is unavailable.
|
|
"""
|
|
try:
|
|
from transformers.models.auto import modeling_auto as _ma
|
|
|
|
def _names(attr):
|
|
d = getattr(_ma, attr, None)
|
|
return dict(d) if d else {}
|
|
|
|
itt = _names("MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES")
|
|
v2s = _names("MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES")
|
|
vlm_types = set(itt) | set(v2s) | set(_CURATED_REMOTE_VLM_TYPES)
|
|
vlm_classes = set(itt.values()) | set(v2s.values())
|
|
|
|
audio_types: set = set()
|
|
for attr in (
|
|
"MODEL_FOR_CTC_MAPPING_NAMES",
|
|
"MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES",
|
|
"MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES",
|
|
"MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES",
|
|
"MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES",
|
|
"MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES",
|
|
):
|
|
audio_types |= set(_names(attr))
|
|
audio_types |= set(_FALLBACK_AUDIO_MODEL_TYPES)
|
|
|
|
return frozenset(vlm_types), frozenset(vlm_classes), frozenset(audio_types)
|
|
except Exception as exc: # pragma: no cover - defensive
|
|
logger.warning("Could not build detection sets from transformers: %s", exc)
|
|
return (
|
|
frozenset(_CURATED_REMOTE_VLM_TYPES),
|
|
frozenset(),
|
|
frozenset(_FALLBACK_AUDIO_MODEL_TYPES),
|
|
)
|
|
|
|
|
|
_VLM_MODEL_TYPES, _VLM_CLASS_NAMES, _AUDIO_ONLY_MODEL_TYPES = _build_detection_sets()
|
|
|
|
# Pre-computed .venv_t5 paths and backend dir for subprocess version switching.
|
|
# Vision check uses the Gemma 4 5.5 sidecar for existing Gemma 4 architectures.
|
|
from utils.paths.storage_roots import studio_root as _studio_root # noqa: E402
|
|
|
|
_VENV_T5_DIR = str(_studio_root() / ".venv_t5_550")
|
|
_BACKEND_DIR = str(Path(__file__).resolve().parent.parent.parent)
|
|
|
|
|
|
def _is_vlm(config) -> bool:
|
|
architectures = getattr(config, "architectures", None) or []
|
|
model_type = getattr(config, "model_type", None)
|
|
explicit_vision = (
|
|
hasattr(config, "vision_config")
|
|
or hasattr(config, "img_processor")
|
|
or hasattr(config, "image_token_index")
|
|
or hasattr(config, "projector_config")
|
|
)
|
|
# Audio-only models are vision only if they carry an explicit vision sub-config.
|
|
if model_type in _AUDIO_ONLY_MODEL_TYPES and not explicit_vision:
|
|
return False
|
|
return (
|
|
explicit_vision
|
|
or any(x in _VLM_CLASS_NAMES for x in architectures)
|
|
or any(isinstance(x, str) and x.endswith(_VLM_ARCH_SUFFIXES) for x in architectures)
|
|
or model_type in _VLM_MODEL_TYPES
|
|
)
|
|
|
|
|
|
def _raw_config_has_vision_config(
|
|
model_name: str,
|
|
hf_token: Optional[str] = None,
|
|
local_files_only: bool = False,
|
|
) -> Optional[bool]:
|
|
try:
|
|
if is_local_path(model_name):
|
|
config_path = Path(normalize_path(model_name)).expanduser() / "config.json"
|
|
else:
|
|
from huggingface_hub import hf_hub_download
|
|
config_path = Path(
|
|
hf_hub_download(
|
|
repo_id = model_name,
|
|
filename = "config.json",
|
|
token = hf_token,
|
|
local_files_only = local_files_only,
|
|
)
|
|
)
|
|
config = json.loads(config_path.read_text())
|
|
architectures = config.get("architectures") or []
|
|
model_type = config.get("model_type")
|
|
explicit_vision = (
|
|
"vision_config" in config
|
|
or "img_processor" in config
|
|
or "image_token_index" in config
|
|
or "projector_config" in config
|
|
)
|
|
# Audio-only models are vision only if they carry an explicit vision sub-config.
|
|
if model_type in _AUDIO_ONLY_MODEL_TYPES and not explicit_vision:
|
|
return False
|
|
return (
|
|
explicit_vision
|
|
or any(isinstance(x, str) and x in _VLM_CLASS_NAMES for x in architectures)
|
|
or any(isinstance(x, str) and x.endswith(_VLM_ARCH_SUFFIXES) for x in architectures)
|
|
or model_type in _VLM_MODEL_TYPES
|
|
)
|
|
except Exception as exc:
|
|
logger.warning("Could not read config.json for '%s': %s", model_name, exc)
|
|
return None
|
|
|
|
|
|
# why: inline _is_vlm and constants are prepended so the subprocess stays
|
|
# self-contained and does not import the parent backend module graph.
|
|
_VISION_CHECK_INLINE_HELPERS = (
|
|
"_VLM_ARCH_SUFFIXES = " + repr(tuple(_VLM_ARCH_SUFFIXES)) + "\n"
|
|
"_VLM_MODEL_TYPES = " + repr(set(_VLM_MODEL_TYPES)) + "\n"
|
|
"_VLM_CLASS_NAMES = " + repr(set(_VLM_CLASS_NAMES)) + "\n"
|
|
"_AUDIO_ONLY_MODEL_TYPES = " + repr(set(_AUDIO_ONLY_MODEL_TYPES)) + "\n"
|
|
"def _is_vlm(config):\n"
|
|
" architectures = getattr(config, 'architectures', None) or []\n"
|
|
" model_type = getattr(config, 'model_type', None)\n"
|
|
" explicit_vision = (\n"
|
|
" hasattr(config, 'vision_config')\n"
|
|
" or hasattr(config, 'img_processor')\n"
|
|
" or hasattr(config, 'image_token_index')\n"
|
|
" or hasattr(config, 'projector_config')\n"
|
|
" )\n"
|
|
" if model_type in _AUDIO_ONLY_MODEL_TYPES and not explicit_vision:\n"
|
|
" return False\n"
|
|
" return (\n"
|
|
" explicit_vision\n"
|
|
" or any(x in _VLM_CLASS_NAMES for x in architectures)\n"
|
|
" or any(isinstance(x, str) and x.endswith(_VLM_ARCH_SUFFIXES) for x in architectures)\n"
|
|
" or model_type in _VLM_MODEL_TYPES\n"
|
|
" )\n"
|
|
)
|
|
|
|
# Subprocess script run with transformers 5.x active. Takes 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)
|
|
|
|
"""
|
|
+ _VISION_CHECK_INLINE_HELPERS
|
|
+ r"""
|
|
try:
|
|
from transformers import AutoConfig
|
|
|
|
# Capability detection never executes model repo code.
|
|
kwargs = {"trust_remote_code": False}
|
|
if token:
|
|
kwargs["token"] = token
|
|
config = AutoConfig.from_pretrained(model_name, **kwargs)
|
|
|
|
is_vlm = _is_vlm(config)
|
|
|
|
model_type = getattr(config, "model_type", None)
|
|
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) -> Optional[bool]:
|
|
"""Run is_vision_model in a subprocess with transformers 5.x.
|
|
|
|
Spawns a clean subprocess with .venv_t5/ on sys.path so AutoConfig
|
|
recognizes newer architectures. Returns True/False for definitive results,
|
|
or None for transient failures (timeouts, subprocess errors), which are not
|
|
cached so they can be retried.
|
|
"""
|
|
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,
|
|
env = child_env_without_native_path_secret(),
|
|
**_windows_hidden_subprocess_kwargs(),
|
|
)
|
|
|
|
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 None
|
|
|
|
data = json.loads(result.stdout.strip())
|
|
if "error" in data:
|
|
logger.warning(
|
|
"Vision check subprocess error for '%s': %s",
|
|
model_name,
|
|
data["error"],
|
|
)
|
|
return None
|
|
|
|
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 None
|
|
except Exception as exc:
|
|
logger.warning("Vision check subprocess failed for '%s': %s", model_name, exc)
|
|
return None
|
|
|
|
|
|
def _token_fingerprint(token: Optional[str]) -> Optional[str]:
|
|
"""SHA256 digest of the token for use as a cache key (avoids storing the
|
|
raw bearer token in process memory)."""
|
|
if token is None:
|
|
return None
|
|
return hashlib.sha256(token.encode("utf-8")).hexdigest()
|
|
|
|
|
|
# Vision detection cache keyed by (name, token, local_files_only); only definitive results cached.
|
|
_vision_detection_cache: Dict[Tuple[str, Optional[str], bool], bool] = {}
|
|
_vision_cache_lock = threading.Lock()
|
|
|
|
|
|
def is_vision_model(
|
|
model_name: str,
|
|
hf_token: Optional[str] = None,
|
|
local_files_only: bool = False,
|
|
) -> bool:
|
|
"""Detect VLMs via the config architecture (works for fine-tunes); transformers-5.x
|
|
models are checked in a .venv_t5/ subprocess. Cached per (model_name, token,
|
|
local_files_only) minus transient failures; local_files_only is in the key so an
|
|
offline probe never shares an online entry."""
|
|
# Local GGUF models are served by llama-server. Their multimodal
|
|
# capability comes from a companion mmproj, not a Transformers config.
|
|
# Do not cache this lookup: a projector may be added beside an existing
|
|
# weight file after it was first inspected.
|
|
if is_local_path(model_name):
|
|
local_path = normalize_path(model_name)
|
|
gguf_file = detect_gguf_model(local_path)
|
|
if gguf_file:
|
|
companion_root = _local_gguf_companion_search_root(local_path, gguf_file)
|
|
mmproj_file = detect_mmproj_file(gguf_file, search_root = companion_root)
|
|
is_vision = mmproj_file is not None
|
|
logger.debug(
|
|
"Local GGUF vision check for '%s': mmproj=%s, is_vision=%s",
|
|
gguf_file,
|
|
mmproj_file,
|
|
is_vision,
|
|
)
|
|
return is_vision
|
|
|
|
# Normalize model name so different casings of the same repo share a key
|
|
try:
|
|
if is_local_path(model_name):
|
|
resolved_name = normalize_path(model_name)
|
|
else:
|
|
resolved_name = resolve_cached_repo_id_case(model_name)
|
|
except Exception as exc:
|
|
logger.debug(
|
|
"Could not normalize model name '%s' for cache key: %s",
|
|
model_name,
|
|
exc,
|
|
)
|
|
resolved_name = model_name
|
|
# Key on effective offline (kwarg OR env) so an offline probe can't poison a later
|
|
# online lookup once the env var is cleared.
|
|
effective_offline = bool(local_files_only or _env_offline())
|
|
cache_key = (resolved_name, _token_fingerprint(hf_token), effective_offline)
|
|
|
|
# Lock-free fast path for cache hits. Sentinel distinguishes "key not found"
|
|
# from "value is False" in a single atomic dict.get() call.
|
|
_MISS = object()
|
|
cached = _vision_detection_cache.get(cache_key, _MISS)
|
|
if cached is not _MISS:
|
|
return cached
|
|
|
|
# Compute outside the lock so long-running detection isn't serialized across
|
|
# models. Two concurrent calls may both run, but produce the same result.
|
|
result = _is_vision_model_uncached(resolved_name, hf_token, local_files_only = effective_offline)
|
|
# Only cache definitive results; None is a transient failure, retry later.
|
|
if result is not None:
|
|
with _vision_cache_lock:
|
|
_vision_detection_cache[cache_key] = result
|
|
return result
|
|
return False
|
|
|
|
|
|
def _is_vision_model_uncached(
|
|
model_name: str,
|
|
hf_token: Optional[str] = None,
|
|
local_files_only: bool = False,
|
|
) -> Optional[bool]:
|
|
"""Uncached vision detection; use is_vision_model() instead.
|
|
|
|
Returns True/False for definitive results, or None on transient errors
|
|
(network, timeout, subprocess failure) so the caller knows not to cache.
|
|
"""
|
|
# Try the raw-config reader FIRST (code-free, version-independent): it classifies
|
|
# repo-code VLMs like DeepSeek-OCR via declarative vision_config with no remote-code
|
|
# execution or transformers-5.x subprocess.
|
|
raw = _raw_config_has_vision_config(
|
|
model_name, hf_token = hf_token, local_files_only = local_files_only
|
|
)
|
|
if raw is not None:
|
|
return raw
|
|
|
|
# Raw read failed transiently: fall back to AutoConfig (remote code DISABLED), via a
|
|
# transformers-5.x subprocess if needed. Skip that subprocess offline (it probes the network).
|
|
from utils.transformers_version import needs_transformers_5
|
|
|
|
if not local_files_only and 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,
|
|
local_files_only = local_files_only,
|
|
)
|
|
|
|
if _is_vlm(config):
|
|
model_type = getattr(config, "model_type", None)
|
|
archs = getattr(config, "architectures", None) or []
|
|
logger.info(
|
|
"Model %s detected as VLM (model_type=%s, architectures=%s)",
|
|
model_name,
|
|
model_type,
|
|
archs,
|
|
)
|
|
return True
|
|
|
|
return False
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Could not determine if {model_name} is vision model: {e}")
|
|
# Permanent failures (not found, gated, bad config) cache as False;
|
|
# transient ones (network, timeout) should not.
|
|
try:
|
|
from huggingface_hub.errors import RepositoryNotFoundError, GatedRepoError
|
|
except ImportError:
|
|
try:
|
|
from huggingface_hub.utils import (
|
|
RepositoryNotFoundError,
|
|
GatedRepoError,
|
|
)
|
|
except ImportError:
|
|
RepositoryNotFoundError = GatedRepoError = None
|
|
if RepositoryNotFoundError is not None and isinstance(
|
|
e, (RepositoryNotFoundError, GatedRepoError)
|
|
):
|
|
return False
|
|
if isinstance(e, (ValueError, json.JSONDecodeError)):
|
|
return False
|
|
return None
|
|
|
|
|
|
VALID_AUDIO_TYPES = ("snac", "csm", "bicodec", "dac", "whisper", "audio_vlm")
|
|
|
|
# Keyed like the vision cache by (name, token, local_files_only) so an unauthenticated
|
|
# or offline miss cannot poison a later authenticated / online lookup.
|
|
_audio_detection_cache: Dict[Tuple[str, Optional[str], bool], Optional[str]] = {}
|
|
|
|
# Tokenizer token patterns → audio_type (all 6 types from tokenizer_config.json)
|
|
_AUDIO_TOKEN_PATTERNS = {
|
|
"csm": lambda tokens: "<|AUDIO|>" in tokens and "<|audio_eos|>" in tokens,
|
|
"whisper": lambda tokens: "<|startoftranscript|>" in tokens,
|
|
# Gemma 3n: <audio_soft_token>; Gemma 4: <|audio|> (not csm's <|AUDIO|>).
|
|
"audio_vlm": lambda tokens: "<audio_soft_token>" in tokens or "<|audio|>" 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
|
|
and "<|text_start|>" in tokens
|
|
and "<|text_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,
|
|
local_files_only: bool = False,
|
|
) -> Optional[str]:
|
|
"""Detect if a model is an audio model and return its type.
|
|
|
|
Works for any model via tokenizer_config.json special tokens.
|
|
Returns an audio_type string ('snac', 'csm', 'bicodec', 'dac', 'whisper',
|
|
'audio_vlm') or None.
|
|
|
|
When local_files_only is True (offline export) the remote HuggingFace fetch
|
|
is skipped so detection never blocks on a network read; only the local HF
|
|
cache is consulted.
|
|
"""
|
|
# Normalize casing + include the token fingerprint (mirrors is_vision_model).
|
|
try:
|
|
if is_local_path(model_name):
|
|
resolved_name = normalize_path(model_name)
|
|
else:
|
|
resolved_name = resolve_cached_repo_id_case(model_name)
|
|
except Exception:
|
|
resolved_name = model_name
|
|
# Key on effective offline (kwarg OR env), matching where the remote fetch is skipped,
|
|
# so an offline negative can't poison a later online probe.
|
|
effective_offline = bool(local_files_only or _env_offline())
|
|
cache_key = (resolved_name, _token_fingerprint(hf_token), effective_offline)
|
|
if cache_key in _audio_detection_cache:
|
|
return _audio_detection_cache[cache_key]
|
|
|
|
result, definitive = _detect_audio_from_tokenizer(
|
|
model_name, hf_token, local_files_only = effective_offline
|
|
)
|
|
# Cache only definitive results; a transient read failure stays None and retries.
|
|
if definitive:
|
|
_audio_detection_cache[cache_key] = 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,
|
|
local_files_only: bool = False,
|
|
) -> Tuple[Optional[str], bool]:
|
|
"""Detect audio type from tokenizer special tokens.
|
|
|
|
Checks local HF cache first, then (unless local_files_only) fetches
|
|
tokenizer_config.json from HF; examines added_tokens_decoder for distinctive
|
|
patterns.
|
|
|
|
Returns (audio_type_or_None, definitive). definitive is False only on a
|
|
transient read failure (network/timeout/5xx) so the caller skips caching and
|
|
retries; a successful read with no audio tokens is a definitive None.
|
|
"""
|
|
|
|
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
|
|
|
|
read_any = False # parsed at least one tokenizer_config -> a None is definitive
|
|
|
|
# 1) Local HF cache first (works for gated/offline models)
|
|
try:
|
|
repo_dir = get_cache_path(model_name)
|
|
if repo_dir is not None and 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())
|
|
read_any = True
|
|
result = _check_token_patterns(tok_config)
|
|
if result:
|
|
return result, True
|
|
except Exception as e:
|
|
logger.debug(f"Could not check local cache for {model_name}: {e}")
|
|
|
|
# 2) Fall back to the HuggingFace API. This raw requests.get ignores the HF offline
|
|
# flag, so gate it on local_files_only OR the env vars to skip the network offline.
|
|
if local_files_only or _env_offline():
|
|
return None, read_any
|
|
|
|
try:
|
|
import requests
|
|
import os
|
|
except Exception:
|
|
return None, read_any
|
|
|
|
paths_to_try = ["tokenizer_config.json", "LLM/tokenizer_config.json"]
|
|
token = hf_token or os.environ.get("HF_TOKEN")
|
|
headers = {"Authorization": f"Bearer {token}"} if token else {}
|
|
|
|
transient = False # a fetch failed for a non-404 reason (network/5xx)
|
|
for tok_path in paths_to_try:
|
|
url = f"https://huggingface.co/{model_name}/resolve/main/{tok_path}"
|
|
try:
|
|
resp = requests.get(url, headers = headers, timeout = 15)
|
|
except Exception as e:
|
|
logger.debug(f"Could not fetch {tok_path} for {model_name}: {e}")
|
|
transient = True
|
|
continue
|
|
if resp.status_code == 404:
|
|
continue # genuinely absent on this path
|
|
if not resp.ok:
|
|
transient = True # 5xx/403/etc -- can't tell, don't cache
|
|
continue
|
|
try:
|
|
tok_config = resp.json()
|
|
except Exception as e:
|
|
logger.debug(f"Bad tokenizer_config for {model_name}/{tok_path}: {e}")
|
|
transient = True
|
|
continue
|
|
read_any = True
|
|
result = _check_token_patterns(tok_config)
|
|
if result:
|
|
return result, True
|
|
|
|
# No audio tokens: definitive unless every attempt failed transiently.
|
|
return None, (read_any or not transient)
|
|
|
|
|
|
def is_audio_input_type(audio_type: Optional[str]) -> bool:
|
|
"""True if an audio_type accepts audio input: whisper (ASR), audio_vlm (Gemma3n)."""
|
|
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 _is_mtp_drafter(path: str) -> bool:
|
|
"""True for a separate-file MTP drafter (speculative head), a companion
|
|
to the main model rather than a selectable quant: the repo-root
|
|
``mtp-*.gguf`` or the ``MTP/`` subdir copies (Gemma 4).
|
|
|
|
Mirrors hub.utils.gguf.is_mtp_drafter_path (utils cannot import hub).
|
|
Must be excluded everywhere mmproj is, or the drafter leaks into variant
|
|
menus (a phantom quant) and quant-matched file lookups -- e.g. a ``Q8_0``
|
|
request must not resolve to ``MTP/...-Q8_0-MTP.gguf``, which sorts ahead
|
|
of the real weight.
|
|
"""
|
|
p = path.lower()
|
|
if not p.endswith(".gguf"):
|
|
return False
|
|
name = p.rsplit("/", 1)[-1]
|
|
return name.startswith("mtp-") or "/mtp/" in f"/{p}"
|
|
|
|
|
|
# Family tokens for #5347's filename fallback. Lowercase; order irrelevant.
|
|
_MODEL_FAMILY_TOKENS: tuple[str, ...] = (
|
|
"qwen",
|
|
"gemma",
|
|
"llama",
|
|
"mistral",
|
|
"ministral",
|
|
"magistral",
|
|
"devstral",
|
|
"phi",
|
|
"deepseek",
|
|
"internvl",
|
|
"minicpm",
|
|
"llava",
|
|
"glm",
|
|
"yi",
|
|
"command-r",
|
|
"molmo",
|
|
"pixtral",
|
|
"smolvlm",
|
|
"moondream",
|
|
"granite",
|
|
"ovis",
|
|
"nemotron",
|
|
"kimi",
|
|
"nanonets",
|
|
"cosmos",
|
|
"mimo",
|
|
"apriel",
|
|
"lfm",
|
|
)
|
|
|
|
|
|
# Word-bounded match: a letter on either side disqualifies (stops ``phi``
|
|
# matching ``sapphire``, ``yi`` matching ``tiny``).
|
|
_FAMILY_TOKEN_RE_CACHE: Dict[str, "_re.Pattern[str]"] = {}
|
|
|
|
|
|
def _family_token_re(token: str) -> "_re.Pattern[str]":
|
|
pat = _FAMILY_TOKEN_RE_CACHE.get(token)
|
|
if pat is None:
|
|
pat = _re.compile(rf"(?:^|[^a-z])({_re.escape(token)})(?:[^a-z]|$)")
|
|
_FAMILY_TOKEN_RE_CACHE[token] = pat
|
|
return pat
|
|
|
|
|
|
def _detect_family_token(filename: str) -> Optional[str]:
|
|
"""Leftmost-position match; ties prefer the longer token."""
|
|
name = filename.lower()
|
|
best: Optional[tuple[int, int, str]] = None # (start, -len, token)
|
|
for token in _MODEL_FAMILY_TOKENS:
|
|
m = _family_token_re(token).search(name)
|
|
if m is None:
|
|
continue
|
|
key = (m.start(1), -len(token), token)
|
|
if best is None or key < best:
|
|
best = key
|
|
return None if best is None else best[2]
|
|
|
|
|
|
def mmproj_matches_model_family(model_path: str, mmproj_path: str) -> bool:
|
|
"""Launcher guard: True unless both filenames carry recognised family
|
|
tokens that disagree."""
|
|
model_fam = _detect_family_token(Path(model_path).name)
|
|
mmproj_fam = _detect_family_token(Path(mmproj_path).name)
|
|
if model_fam is None or mmproj_fam is None:
|
|
return True
|
|
return model_fam == mmproj_fam
|
|
|
|
|
|
def _shared_prefix_len(a: str, b: str) -> int:
|
|
n = min(len(a), len(b))
|
|
for i in range(n):
|
|
if a[i] != b[i]:
|
|
return i
|
|
return n
|
|
|
|
|
|
def _is_gguf_filename(filename: str) -> bool:
|
|
return filename.lower().endswith(".gguf")
|
|
|
|
|
|
def _iter_gguf_files(directory: Path, recursive: bool = False):
|
|
if not directory.is_dir():
|
|
return
|
|
iterator = directory.rglob("*") if recursive else directory.iterdir()
|
|
for f in iterator:
|
|
if f.is_file() and _is_gguf_filename(f.name):
|
|
yield f
|
|
|
|
|
|
def detect_mmproj_file(path: str, search_root: Optional[str] = None) -> Optional[str]:
|
|
"""Find the mmproj GGUF for a model.
|
|
|
|
``path``: directory or a .gguf file. ``search_root``: optional ancestor
|
|
to also walk (snapshot layouts where the weight is in ``snapshot/BF16/``
|
|
but the projector sits at ``snapshot/``). Returns the projector path or
|
|
``None``."""
|
|
p = Path(path)
|
|
start_dir = p.parent if p.is_file() else p
|
|
if not start_dir.is_dir():
|
|
return None
|
|
|
|
# Walk incrementally so a sibling subdir's mmproj cannot leak in.
|
|
seen: set[Path] = set()
|
|
scan_order: list[Path] = []
|
|
|
|
def _add(d: Path) -> None:
|
|
try:
|
|
resolved = d.resolve()
|
|
except OSError:
|
|
return
|
|
if resolved in seen or not resolved.is_dir():
|
|
return
|
|
seen.add(resolved)
|
|
scan_order.append(resolved)
|
|
|
|
_add(start_dir)
|
|
|
|
# Ollama's .studio_links/foo.gguf -> blobs/sha256-...: also scan target dir.
|
|
try:
|
|
if p.is_symlink() and p.is_file():
|
|
target_parent = p.resolve().parent
|
|
if target_parent.is_dir():
|
|
_add(target_parent)
|
|
except OSError:
|
|
pass
|
|
if search_root is not None:
|
|
try:
|
|
root_resolved = Path(search_root).resolve()
|
|
start_resolved = start_dir.resolve()
|
|
if root_resolved == start_resolved or (
|
|
start_resolved.is_relative_to(root_resolved)
|
|
if hasattr(start_resolved, "is_relative_to")
|
|
else str(start_resolved).startswith(str(root_resolved) + "/")
|
|
):
|
|
cur = start_resolved
|
|
while cur != root_resolved and cur.parent != cur:
|
|
cur = cur.parent
|
|
_add(cur)
|
|
if cur == root_resolved:
|
|
break
|
|
except OSError:
|
|
pass
|
|
|
|
candidates: list[Path] = []
|
|
seen_resolved: set[Path] = set()
|
|
for d in scan_order:
|
|
for f in _iter_gguf_files(d):
|
|
try:
|
|
resolved = f.resolve()
|
|
except OSError:
|
|
continue
|
|
if resolved in seen_resolved:
|
|
continue
|
|
# Prefer ``general.type=='mmproj'``, else filename.
|
|
meta = read_gguf_general_metadata(str(resolved))
|
|
by_meta = is_mmproj_by_metadata(meta)
|
|
if by_meta is True or (by_meta is None and _is_mmproj(f.name)):
|
|
seen_resolved.add(resolved)
|
|
candidates.append(resolved)
|
|
|
|
if not candidates:
|
|
return None
|
|
|
|
# Directory path: no model name to compare against; legacy behaviour.
|
|
if not p.is_file():
|
|
return str(candidates[0])
|
|
|
|
# Stage 1: GGUF metadata. Stage 2: filename family token (#5347).
|
|
model_stem = p.stem.lower()
|
|
model_family = _detect_family_token(p.name)
|
|
weight_meta = read_gguf_general_metadata(str(p))
|
|
|
|
scored: list[tuple[int, Path]] = []
|
|
for c in candidates:
|
|
cand_meta = read_gguf_general_metadata(str(c))
|
|
meta_score = pairing_score(weight_meta, cand_meta)
|
|
if meta_score == -1:
|
|
logger.info(f"detect_mmproj_file: dropped {c.name} (metadata mismatch)")
|
|
continue
|
|
if meta_score == 0 and model_family is not None:
|
|
# Unrecognised candidate family is a wildcard (``mmproj-F16.gguf``).
|
|
cand_family = _detect_family_token(c.name)
|
|
if cand_family is not None and cand_family != model_family:
|
|
logger.info(
|
|
f"detect_mmproj_file: dropped {c.name} "
|
|
f"(filename family {cand_family!r} vs model {model_family!r})"
|
|
)
|
|
continue
|
|
scored.append((meta_score, c))
|
|
|
|
if not scored:
|
|
return None
|
|
|
|
# Score first, then longest shared prefix, then shorter stem.
|
|
best = max(
|
|
scored,
|
|
key = lambda sc: (
|
|
sc[0],
|
|
_shared_prefix_len(model_stem, sc[1].stem.lower()),
|
|
-len(sc[1].stem),
|
|
),
|
|
)
|
|
return str(best[1])
|
|
|
|
|
|
def detect_mtp_file(path: str, search_root: Optional[str] = None) -> Optional[str]:
|
|
"""Find the separate MTP drafter (``mtp-*.gguf``) for a local GGUF model.
|
|
|
|
The drafter that pairs with the main weights sits at the repo/snapshot
|
|
root (Gemma 4); the weight itself may be at the root or in a quant subdir,
|
|
so scan the weight's directory and ``search_root``. Matches by the
|
|
``mtp-`` filename prefix unsloth uses for ``-hf`` auto-discovery -- the
|
|
same signal as the HF download path. Repos that bake the head into the
|
|
main GGUF (Qwen) have no such sibling, so this returns None.
|
|
|
|
Pairs by name so a multi-model folder can't attach a foreign drafter:
|
|
unsloth names the drafter ``mtp-<model>.gguf`` where ``<model>`` prefixes
|
|
the weight filename across all Gemma 4 repos (e.g.
|
|
``mtp-gemma-4-12B-it.gguf`` next to ``gemma-4-12B-it-qat-Q4_0.gguf``).
|
|
An unmatched drafter is skipped (fail-safe: no MTP).
|
|
"""
|
|
p = Path(path)
|
|
weight_name = p.name.lower() if p.suffix.lower() == ".gguf" else None
|
|
start_dir = p.parent if p.is_file() else p
|
|
dirs = [start_dir]
|
|
if search_root is not None:
|
|
dirs.append(Path(search_root))
|
|
for d in dirs:
|
|
try:
|
|
entries = sorted(d.iterdir())
|
|
except OSError:
|
|
continue
|
|
for f in entries:
|
|
name = f.name.lower()
|
|
if not (name.startswith("mtp-") and name.endswith(".gguf")):
|
|
continue
|
|
stem = name[len("mtp-") : -len(".gguf")]
|
|
if not stem or (weight_name is not None and not weight_name.startswith(stem)):
|
|
continue
|
|
try:
|
|
if f.is_file():
|
|
return str(f.resolve())
|
|
except OSError:
|
|
continue
|
|
return None
|
|
|
|
|
|
def detect_gguf_model(path: str) -> Optional[str]:
|
|
"""Check if a local path is or contains a GGUF model file.
|
|
|
|
Handles a direct .gguf path or a directory of .gguf files. Skips mmproj
|
|
files (pass those via ``--mmproj``; see :func:`detect_mmproj_file`). Returns
|
|
the .gguf path or None. For HF repos, use detect_gguf_model_remote().
|
|
"""
|
|
p = Path(path)
|
|
|
|
# Case 1: direct .gguf file
|
|
if p.suffix.lower() == ".gguf":
|
|
# Companions are not models: rejecting a drafter here also keeps
|
|
# detect_mtp_file from pairing the same file with itself
|
|
# (-m drafter --model-draft drafter). Include the immediate parent
|
|
# dir so the MTP/ subdir copies are caught -- the basename alone
|
|
# (...-MTP.gguf) doesn't match the predicate's mtp- prefix.
|
|
rel = f"{p.parent.name}/{p.name}"
|
|
quant = _extract_quant_label(rel)
|
|
if _is_mmproj(p.name) or _is_mtp_drafter(rel) or _is_big_endian_gguf_path(rel, quant):
|
|
return None
|
|
# Extension is authoritative: don't gate on is_file()/exists(), which
|
|
# can fail in the Windows lock window after llama-server is killed.
|
|
try:
|
|
is_dir = p.is_dir()
|
|
except OSError:
|
|
is_dir = False # stat() unavailable in the lock window
|
|
if not is_dir:
|
|
return str(p.absolute()) # absolute() keeps symlink names readable
|
|
# Directory named "*.gguf": fall through to the dir scan below.
|
|
|
|
# Case 2: directory containing .gguf files (skip mmproj / MTP drafter)
|
|
if p.is_dir():
|
|
gguf_files = []
|
|
for f in _iter_gguf_files(p):
|
|
context_rel = f"{f.parent.name}/{f.name}"
|
|
quant = _extract_quant_label(context_rel)
|
|
if (
|
|
_is_mmproj(f.name)
|
|
or _is_mtp_drafter(context_rel)
|
|
or _is_big_endian_gguf_path(context_rel, quant)
|
|
):
|
|
continue
|
|
gguf_files.append(f)
|
|
gguf_files.sort(key = lambda f: f.stat().st_size, reverse = True)
|
|
if gguf_files:
|
|
return str(gguf_files[0].resolve())
|
|
|
|
return None
|
|
|
|
|
|
# Preferred GGUF quant levels, descending priority. UD (Unsloth Dynamic)
|
|
# variants beat standard quants on quality per bit; repos without UD fall back
|
|
# to standard quants. Ordered by 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: quant levels in _GGUF_QUANT_PREFERENCE order, else first .gguf."""
|
|
gguf_files = [f for f in filenames if f.lower().endswith(".gguf")]
|
|
if not gguf_files:
|
|
return None
|
|
|
|
for quant in _GGUF_QUANT_PREFERENCE:
|
|
for f in gguf_files:
|
|
if quant in f:
|
|
return f
|
|
|
|
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 quant 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"
|
|
"Qwen3.6-IQ4_XS-3.53bpw.gguf" → "IQ4_XS-3.53bpw"
|
|
"""
|
|
import re
|
|
|
|
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])
|
|
quant_re = (
|
|
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
|
|
# Optional bits-per-weight modifier so repos that ship multiple
|
|
# files at the same base quant (e.g. byteshape's IQ4_XS at 3.53,
|
|
# 3.97, 4.19 bpw) don't collapse into a single merged variant.
|
|
r"(-[0-9]+(?:\.[0-9]+)?bpw)?"
|
|
)
|
|
match = re.search(quant_re, stem, re.IGNORECASE)
|
|
# Subdir layouts like ``BF16/foo.gguf`` keep the quant in the directory,
|
|
# not the basename. Check parent dirs too so the label matches the
|
|
# snapshot-relative path produced elsewhere.
|
|
if not match and "/" in filename:
|
|
parents = filename.rsplit("/", 1)[0]
|
|
for segment in reversed(parents.split("/")):
|
|
m = re.search(quant_re, segment, re.IGNORECASE)
|
|
if m:
|
|
match = m
|
|
break
|
|
if match:
|
|
prefix = match.group(1) or ""
|
|
bpw = match.group(3) or ""
|
|
return f"{prefix}{match.group(2)}{bpw}"
|
|
# Fallback: last hyphen-separated segment
|
|
return stem.split("-")[-1]
|
|
|
|
|
|
_BIG_ENDIAN_GGUF_FILENAME_RE = re.compile(r"(^|[-_])be(?:[._-]|$)", re.IGNORECASE)
|
|
_GGUF_KNOWN_QUANT_RE = re.compile(
|
|
r"(UD-)?"
|
|
r"(MXFP[0-9]+(?:_[A-Z0-9]+)*"
|
|
r"|IQ[0-9]+_[A-Z]+(?:_[A-Z0-9]+)?"
|
|
r"|TQ[0-9]+_[0-9]+"
|
|
r"|Q[0-9]+_K_[A-Z]+"
|
|
r"|Q[0-9]+_[0-9]+"
|
|
r"|Q[0-9]+_K"
|
|
r"|BF16|F16|F32)",
|
|
re.IGNORECASE,
|
|
)
|
|
|
|
|
|
def _is_big_endian_gguf_path(path: str, quant: str = "") -> bool:
|
|
normalized = path.replace("\\", "/")
|
|
name = normalized.rsplit("/", 1)[-1]
|
|
stem = name.rsplit(".", 1)[0].lower()
|
|
quant_key = quant.strip().lower()
|
|
quant_index = stem.find(quant_key) if quant_key else -1
|
|
parent = normalized.rsplit("/", 1)[0].lower() if "/" in normalized else ""
|
|
quant_in_parent_only = (
|
|
bool(parent)
|
|
and quant_index < 0
|
|
and (
|
|
(quant_key and quant_key in parent)
|
|
or (not quant_key and _GGUF_KNOWN_QUANT_RE.search(parent) is not None)
|
|
)
|
|
)
|
|
for match in _BIG_ENDIAN_GGUF_FILENAME_RE.finditer(stem):
|
|
if quant_index >= 0 and quant_index < match.start():
|
|
return True
|
|
tail = stem[match.end() :].lstrip("._-")
|
|
if not tail or _GGUF_KNOWN_QUANT_RE.search(tail) is None:
|
|
return not quant_in_parent_only
|
|
return False
|
|
|
|
|
|
def _local_gguf_companion_search_root(selected_path: str, gguf_file: str) -> str:
|
|
"""Directory to scan upward from for local GGUF companion files."""
|
|
import re
|
|
|
|
selected = Path(selected_path)
|
|
gguf_path = Path(gguf_file)
|
|
if selected.suffix.lower() != ".gguf":
|
|
return selected_path
|
|
|
|
gguf_dir = gguf_path.parent
|
|
if not gguf_dir.name:
|
|
return str(gguf_dir)
|
|
|
|
quant_dir_re = (
|
|
r"(UD-)?("
|
|
r"MXFP[0-9]+(?:_[A-Z0-9]+)*"
|
|
r"|IQ[0-9]+_[A-Z]+(?:_[A-Z0-9]+)?"
|
|
r"|TQ[0-9]+_[0-9]+"
|
|
r"|Q[0-9]+_K_[A-Z]+"
|
|
r"|Q[0-9]+_[0-9]+"
|
|
r"|Q[0-9]+_K"
|
|
r"|BF16|F16|F32"
|
|
r")"
|
|
)
|
|
if re.fullmatch(quant_dir_re, gguf_dir.name, re.IGNORECASE):
|
|
return str(gguf_dir.parent)
|
|
return str(gguf_dir)
|
|
|
|
|
|
def _iter_hf_cache_snapshots(repo_id: str):
|
|
"""Yield HF cache snapshot dirs for *repo_id*, newest first.
|
|
|
|
Empty if HF_HUB_CACHE is missing, the repo isn't cached, or has no
|
|
snapshots. Repo name match is case-insensitive to handle casing drift
|
|
between download time and lookup.
|
|
"""
|
|
try:
|
|
from huggingface_hub import constants as hf_constants
|
|
except Exception:
|
|
return
|
|
|
|
cache_dir = Path(hf_constants.HF_HUB_CACHE)
|
|
target = f"models--{repo_id.replace('/', '--')}".lower()
|
|
repo_dirs: list[Path] = []
|
|
try:
|
|
if not cache_dir.is_dir():
|
|
return
|
|
for entry in cache_dir.iterdir():
|
|
if entry.is_dir() and entry.name.lower() == target:
|
|
repo_dirs.append(entry)
|
|
except OSError:
|
|
return
|
|
if not repo_dirs:
|
|
return
|
|
|
|
snap_dirs: list[Path] = []
|
|
for repo_dir in repo_dirs:
|
|
snapshots = repo_dir / "snapshots"
|
|
try:
|
|
if snapshots.is_dir():
|
|
for snap_dir in snapshots.iterdir():
|
|
try:
|
|
if snap_dir.is_dir():
|
|
snap_dirs.append(snap_dir)
|
|
except OSError:
|
|
continue
|
|
except OSError:
|
|
continue
|
|
if not snap_dirs:
|
|
return
|
|
snap_dirs_with_mtime = []
|
|
for snap_dir in snap_dirs:
|
|
try:
|
|
snap_dirs_with_mtime.append((snap_dir.stat().st_mtime, snap_dir))
|
|
except OSError:
|
|
continue
|
|
snap_dirs_with_mtime.sort(key = lambda item: item[0], reverse = True)
|
|
yield from (snap_dir for _, snap_dir in snap_dirs_with_mtime)
|
|
|
|
|
|
def _list_gguf_variants_from_hf_cache(repo_id: str) -> Optional[tuple[list[GgufVariantInfo], bool]]:
|
|
"""Variants from the local HF cache snapshot, or None if not cached.
|
|
|
|
A newer snapshot can hold only a companion file (for example a vision
|
|
projector fetched on demand) while the quant files live in an older
|
|
snapshot. Returning the first snapshot that merely reports a vision flag
|
|
would shadow those real variants, so keep scanning older snapshots for
|
|
actual variants and carry the vision flag across snapshots.
|
|
"""
|
|
any_vision = False
|
|
for snap in _iter_hf_cache_snapshots(repo_id):
|
|
variants, has_vision = list_local_gguf_variants(str(snap))
|
|
any_vision = any_vision or has_vision
|
|
if variants:
|
|
return variants, any_vision
|
|
if any_vision:
|
|
return [], True
|
|
return None
|
|
|
|
|
|
def list_gguf_variants(
|
|
repo_id: str, hf_token: Optional[str] = None
|
|
) -> tuple[list[GgufVariantInfo], bool]:
|
|
"""List all GGUF quant variants in a HF repo.
|
|
|
|
Separates main model files from mmproj (vision projection) files; mmproj
|
|
presence flags a vision-capable model.
|
|
|
|
Returns:
|
|
(variants, has_vision): non-mmproj GGUF variants + vision flag.
|
|
"""
|
|
from huggingface_hub import model_info as hf_model_info
|
|
|
|
# Offline: skip the API and serve from cache
|
|
if _env_offline():
|
|
cached = _list_gguf_variants_from_hf_cache(repo_id)
|
|
if cached is not None:
|
|
return cached
|
|
|
|
try:
|
|
info = hf_model_info(repo_id, token = hf_token, files_metadata = True)
|
|
except Exception as e:
|
|
# Permanent errors (deleted/gated/bad revision) must surface to the
|
|
# caller; serving stale cache would mask the real cause. Matches the
|
|
# early-return in ``detect_gguf_model_remote``.
|
|
if type(e).__name__ in (
|
|
"RepositoryNotFoundError",
|
|
"GatedRepoError",
|
|
"RevisionNotFoundError",
|
|
"EntryNotFoundError",
|
|
):
|
|
raise
|
|
# API failed transiently; fall back to local snapshot if fully downloaded.
|
|
cached = _list_gguf_variants_from_hf_cache(repo_id)
|
|
if cached is not None:
|
|
logger.warning(
|
|
"HF API unreachable for %s (%s); using local cache snapshot.",
|
|
repo_id,
|
|
e.__class__.__name__,
|
|
)
|
|
return cached
|
|
raise
|
|
variants: list[GgufVariantInfo] = []
|
|
has_vision = False
|
|
|
|
quant_totals: dict[str, int] = {} # quant -> total bytes
|
|
quant_first_file: dict[str, str] = {} # quant -> first filename (display)
|
|
|
|
for sibling in info.siblings:
|
|
fname = sibling.rfilename
|
|
if not fname.lower().endswith(".gguf"):
|
|
continue
|
|
size = sibling.size or 0
|
|
|
|
# mmproj files are vision projections, not main model files
|
|
if "mmproj" in fname.lower():
|
|
has_vision = True
|
|
continue
|
|
# MTP drafters are speculative-decoding companions, not quants.
|
|
if _is_mtp_drafter(fname):
|
|
continue
|
|
|
|
quant = _extract_quant_label(fname)
|
|
if _is_big_endian_gguf_path(fname, quant):
|
|
continue
|
|
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); pinning and OOM
|
|
# demotion happen client-side where GPU VRAM info exists.
|
|
variants.sort(key = lambda v: -v.size_bytes)
|
|
|
|
return variants, has_vision
|
|
|
|
|
|
def _resolve_gguf_dir(p: Path) -> Optional[Path]:
|
|
"""Resolve a path to the directory containing GGUF variants.
|
|
|
|
Directory *p* returns directly. A ``.gguf`` file whose parent dir has
|
|
model metadata (``config.json`` or ``adapter_config.json``) returns the
|
|
parent -- all GGUFs there belong to the same model. Returns ``None`` for
|
|
loose standalone GGUFs (no config) to avoid cross-wiring unrelated models.
|
|
"""
|
|
if p.is_dir():
|
|
return p
|
|
if p.is_file() and p.suffix.lower() == ".gguf":
|
|
parent = p.parent
|
|
if (
|
|
(parent / "config.json").exists()
|
|
or (parent / "adapter_config.json").exists()
|
|
or (parent / "export_metadata.json").exists()
|
|
):
|
|
return parent
|
|
return None
|
|
|
|
|
|
def list_local_gguf_variants(directory: str) -> tuple[list[GgufVariantInfo], bool]:
|
|
"""List GGUF quant variants in a local directory.
|
|
|
|
Like :func:`list_gguf_variants` but reads the filesystem. Aggregates shard
|
|
sizes by quant label so split GGUFs appear as one variant.
|
|
|
|
Returns:
|
|
(variants, has_vision): non-mmproj GGUF variants + vision flag.
|
|
"""
|
|
p = _resolve_gguf_dir(Path(directory))
|
|
if p is None:
|
|
return [], False
|
|
|
|
quant_totals: dict[str, int] = {}
|
|
quant_first_file: dict[str, str] = {}
|
|
has_vision = False
|
|
|
|
# Recurse so variant-specific subdirs (e.g. ``BF16/...gguf`` used by
|
|
# some HF GGUF repos for the largest quants) are picked up. Result
|
|
# filenames keep the relative subpath so ``_find_local_gguf_by_variant``
|
|
# can locate the file again.
|
|
for f in sorted(_iter_gguf_files(p, recursive = True)):
|
|
if _is_mmproj(f.name):
|
|
has_vision = True
|
|
continue
|
|
try:
|
|
size = f.stat().st_size
|
|
except OSError:
|
|
size = 0
|
|
# Use the relative path so ``BF16/foo.gguf`` and ``Q4_K_M/foo.gguf``
|
|
# get distinct quant labels instead of collapsing on basename.
|
|
rel = f.relative_to(p).as_posix()
|
|
if _is_mtp_drafter(rel):
|
|
continue
|
|
quant = _extract_quant_label(rel)
|
|
if _is_big_endian_gguf_path(rel, quant):
|
|
continue
|
|
quant_totals[quant] = quant_totals.get(quant, 0) + size
|
|
if quant not in quant_first_file:
|
|
quant_first_file[quant] = rel
|
|
|
|
variants = [
|
|
GgufVariantInfo(
|
|
filename = quant_first_file[q],
|
|
quant = q,
|
|
size_bytes = s,
|
|
)
|
|
for q, s in quant_totals.items()
|
|
]
|
|
variants.sort(key = lambda v: -v.size_bytes)
|
|
return variants, has_vision
|
|
|
|
|
|
def _find_local_gguf_by_variant(directory: str, variant: str) -> Optional[str]:
|
|
"""Find the GGUF file in *directory* matching a quantization *variant*.
|
|
|
|
For sharded GGUFs (multiple files sharing a quant label), returns the
|
|
first shard (sorted by name), which is what ``llama-server -m`` expects.
|
|
|
|
Returns the resolved absolute path, or ``None`` if no match.
|
|
"""
|
|
p = _resolve_gguf_dir(Path(directory))
|
|
if p is None:
|
|
return None
|
|
|
|
# Recurse so variants under a quant-named subdir (e.g.
|
|
# ``BF16/foo-BF16-00001-of-00002.gguf``) are found. Match the relative
|
|
# path so the quant label can come from the dir name when the basename
|
|
# omits it.
|
|
matches = []
|
|
for f in _iter_gguf_files(p, recursive = True):
|
|
rel = f.relative_to(p).as_posix()
|
|
if _is_mmproj(f.name) or _is_mtp_drafter(rel):
|
|
continue
|
|
quant = _extract_quant_label(rel)
|
|
if quant != variant or _is_big_endian_gguf_path(rel, quant):
|
|
continue
|
|
matches.append(f)
|
|
matches.sort()
|
|
if matches:
|
|
return str(matches[0].resolve())
|
|
return None
|
|
|
|
|
|
def _detect_gguf_from_hf_cache(repo_id: str) -> Optional[str]:
|
|
"""Best GGUF filename for *repo_id* from the local HF cache, or None.
|
|
|
|
Excludes mmproj (vision projector) files so a partial cache holding only
|
|
the projector cannot route it as the main model.
|
|
"""
|
|
for snap in _iter_hf_cache_snapshots(repo_id):
|
|
rel_files = []
|
|
for f in _iter_gguf_files(snap, recursive = True):
|
|
rel = f.relative_to(snap).as_posix()
|
|
quant = _extract_quant_label(rel)
|
|
if _is_mmproj(f.name) or _is_mtp_drafter(rel) or _is_big_endian_gguf_path(rel, quant):
|
|
continue
|
|
rel_files.append(rel)
|
|
if rel_files:
|
|
return _pick_best_gguf(rel_files)
|
|
return None
|
|
|
|
|
|
def detect_gguf_model_remote(repo_id: str, hf_token: Optional[str] = None) -> Optional[str]:
|
|
"""Return the best GGUF filename in a HF repo, or None.
|
|
|
|
Retries (3 attempts, 1s/2s/4s backoff) on transient HF Hub failures: a
|
|
silent None would make the caller treat a GGUF-only repo as non-GGUF and
|
|
fall through to MLX on Apple Silicon. Offline falls back to the local cache.
|
|
"""
|
|
import time
|
|
from huggingface_hub import model_info as hf_model_info
|
|
|
|
if _env_offline():
|
|
cached = _detect_gguf_from_hf_cache(repo_id)
|
|
if cached is not None:
|
|
return cached
|
|
|
|
last_err: Optional[Exception] = None
|
|
for attempt in range(3):
|
|
try:
|
|
info = hf_model_info(repo_id, token = hf_token)
|
|
repo_files = []
|
|
for sibling in info.siblings:
|
|
fname = sibling.rfilename
|
|
if not fname.lower().endswith(".gguf"):
|
|
continue
|
|
quant = _extract_quant_label(fname)
|
|
if (
|
|
_is_mmproj(fname)
|
|
or _is_mtp_drafter(fname)
|
|
or _is_big_endian_gguf_path(fname, quant)
|
|
):
|
|
continue
|
|
repo_files.append(fname)
|
|
return _pick_best_gguf(repo_files)
|
|
except Exception as e:
|
|
last_err = e
|
|
# 404 / RepoNotFound is permanent -- don't retry
|
|
err_name = type(e).__name__
|
|
if err_name in (
|
|
"RepositoryNotFoundError",
|
|
"GatedRepoError",
|
|
"RevisionNotFoundError",
|
|
"EntryNotFoundError",
|
|
):
|
|
logger.debug(f"Could not check GGUF files for '{repo_id}': {e}")
|
|
return None
|
|
if attempt < 2:
|
|
time.sleep(2**attempt)
|
|
|
|
# All attempts failed; fall back to local cache for offline users.
|
|
cached = _detect_gguf_from_hf_cache(repo_id)
|
|
if cached is not None:
|
|
logger.warning(
|
|
"HF API unreachable for '%s' (%s); using local cache to detect GGUF.",
|
|
repo_id,
|
|
type(last_err).__name__ if last_err else "unknown",
|
|
)
|
|
return cached
|
|
|
|
logger.warning(f"Could not check GGUF files for '{repo_id}' after 3 attempts: {last_err}")
|
|
return None
|
|
|
|
|
|
def download_gguf_file(
|
|
repo_id: str,
|
|
filename: str,
|
|
hf_token: Optional[str] = None,
|
|
) -> str:
|
|
"""Download a specific GGUF file from a HF repo; returns the local path."""
|
|
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 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 via HF metadata.
|
|
|
|
Combines three signals: "sentence-transformers" or "feature-extraction" in
|
|
tags, or pipeline_tag in {"sentence-similarity", "feature-extraction"}.
|
|
Catches models like gte-modernbert whose library_name is "transformers".
|
|
|
|
Args:
|
|
model_name: Model identifier (HF repo or local path)
|
|
hf_token: Optional HF token for gated/private models
|
|
|
|
Returns:
|
|
True if embedding model, else False (default for local paths or 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 (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 _has_model_weight_files(model_dir: Path) -> bool:
|
|
"""Return True when a directory contains loadable model weights."""
|
|
for item in model_dir.iterdir():
|
|
if not item.is_file():
|
|
continue
|
|
|
|
suffix = item.suffix.lower()
|
|
if suffix == ".safetensors":
|
|
return True
|
|
if suffix == ".gguf":
|
|
return "mmproj" not in item.name.lower()
|
|
if suffix == ".bin":
|
|
name = item.name.lower()
|
|
if (
|
|
name.startswith("pytorch_model")
|
|
or name.startswith("model")
|
|
or name.startswith("adapter_model")
|
|
or name.startswith("consolidated")
|
|
):
|
|
return True
|
|
return False
|
|
|
|
|
|
def _detect_training_output_type(model_dir: Path) -> Optional[str]:
|
|
"""Classify a Studio training output as LoRA or full finetune."""
|
|
adapter_config = model_dir / "adapter_config.json"
|
|
adapter_model = model_dir / "adapter_model.safetensors"
|
|
if adapter_config.exists() or adapter_model.exists():
|
|
return "lora"
|
|
|
|
config_file = model_dir / "config.json"
|
|
if config_file.exists() and _has_model_weight_files(model_dir):
|
|
return "merged"
|
|
|
|
return None
|
|
|
|
|
|
def _looks_like_lora_adapter(model_dir: Path) -> bool:
|
|
return model_dir.is_dir() and (
|
|
(model_dir / "adapter_config.json").exists()
|
|
or any(model_dir.glob("adapter_model*.safetensors"))
|
|
or any(model_dir.glob("adapter_model*.bin"))
|
|
)
|
|
|
|
|
|
def scan_trained_models(outputs_dir: str = str(outputs_root())) -> List[Tuple[str, str, str]]:
|
|
"""Scan outputs folder for trained Studio models.
|
|
|
|
Returns:
|
|
List of (display_name, model_path, model_type), where model_type is
|
|
"lora" for adapter runs or "merged" for full finetunes.
|
|
"""
|
|
trained_models = []
|
|
outputs_path = resolve_output_dir(outputs_dir)
|
|
|
|
if not outputs_path.exists():
|
|
logger.warning(f"Outputs directory not found: {outputs_dir}")
|
|
return trained_models
|
|
|
|
try:
|
|
for item in outputs_path.iterdir():
|
|
if item.is_dir():
|
|
model_type = _detect_training_output_type(item)
|
|
if model_type is None:
|
|
continue
|
|
|
|
display_name = item.name
|
|
model_path = str(item)
|
|
trained_models.append((display_name, model_path, model_type))
|
|
logger.debug("Found trained model: %s (%s)", display_name, model_type)
|
|
|
|
# Sort by mtime, newest first
|
|
trained_models.sort(key = lambda x: Path(x[1]).stat().st_mtime, reverse = True)
|
|
|
|
logger.info(
|
|
"Found %s trained models in %s",
|
|
len(trained_models),
|
|
outputs_dir,
|
|
)
|
|
return trained_models
|
|
|
|
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 layouts: two-level {run}/{checkpoint}/ (merged & LoRA) and
|
|
flat {name}-finetune-gguf/ (GGUF).
|
|
|
|
Returns:
|
|
List of (display_name, model_path, export_type, base_model), where
|
|
export_type is "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
|
|
|
|
# Flat GGUF export (e.g. exports/gemma-3-4b-it-finetune-gguf/).
|
|
# Skip mmproj (vision projection) files — not loadable as main models.
|
|
gguf_files = [f for f in _iter_gguf_files(run_dir) 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])
|
|
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(_iter_gguf_files(checkpoint_dir))
|
|
|
|
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(_iter_gguf_files(checkpoint_dir))
|
|
# checkpoint_dir first, then run_dir (export.py writes
|
|
# metadata to the top-level export dir)
|
|
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: base model from ./outputs/{run_name}/adapter_config.json
|
|
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_checkpoint(checkpoint_path: str) -> Optional[str]:
|
|
"""Read the base model name from a local training or checkpoint directory."""
|
|
try:
|
|
checkpoint_path_obj = Path(checkpoint_path)
|
|
|
|
adapter_config_path = checkpoint_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("Detected base model from adapter_config.json: %s", base_model)
|
|
return base_model
|
|
|
|
config_path = checkpoint_path_obj / "config.json"
|
|
if config_path.exists():
|
|
with open(config_path, "r") as f:
|
|
config = json.load(f)
|
|
for key in ("model_name", "_name_or_path"):
|
|
base_model = config.get(key)
|
|
if base_model and str(base_model) != str(checkpoint_path_obj):
|
|
logger.info(
|
|
"Detected base model from config.json (%s): %s",
|
|
key,
|
|
base_model,
|
|
)
|
|
return base_model
|
|
|
|
# TODO: torch.load default weights_only=True (torch >= 2.6) rejects pickled TrainingArguments; re-enable via safe_globals or weights_only=False once threat model allows.
|
|
# training_args_path = checkpoint_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(
|
|
# "Detected base model from training_args.bin: %s", base_model
|
|
# )
|
|
# return base_model
|
|
# except Exception as e:
|
|
# logger.warning(f"Could not load training_args.bin: {e}")
|
|
|
|
dir_name = checkpoint_path_obj.name
|
|
if dir_name.startswith("unsloth_"):
|
|
parts = dir_name.split("_")
|
|
if len(parts) >= 2:
|
|
model_parts = parts[1:-1]
|
|
base_model = "unsloth/" + "_".join(model_parts)
|
|
logger.info("Detected base model from directory name: %s", base_model)
|
|
return base_model
|
|
|
|
logger.warning(f"Could not detect base model for checkpoint: {checkpoint_path}")
|
|
return None
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error reading base model from checkpoint config: {e}")
|
|
return None
|
|
|
|
|
|
def get_base_model_from_lora(lora_path: str) -> Optional[str]:
|
|
"""Read the base model name from a LoRA adapter's config, or None."""
|
|
try:
|
|
lora_path_obj = Path(lora_path)
|
|
|
|
if not _looks_like_lora_adapter(lora_path_obj):
|
|
return None
|
|
|
|
# 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)
|
|
# TODO: torch.load default weights_only=True (torch >= 2.6) rejects pickled TrainingArguments; also an remote code execution sink for third-party LoRAs via this route, re-enable behind a trust check if needed.
|
|
# 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 dir name (unsloth_<model>_<timestamp>)
|
|
dir_name = lora_path_obj.name
|
|
if dir_name.startswith("unsloth_"):
|
|
parts = dir_name.split("_")
|
|
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
|
|
|
|
|
|
def get_base_model_from_lora_identifier(
|
|
identifier: str, hf_token: Optional[str] = None
|
|
) -> Optional[str]:
|
|
"""Resolve a LoRA adapter's base model for a LOCAL dir OR a REMOTE HF repo.
|
|
|
|
``get_base_model_from_lora`` only reads a local adapter directory (it requires
|
|
``is_dir()``). The SECURITY gates must also follow a *remote* adapter's base,
|
|
because the base model's code / weights are what execute on load: an attacker's
|
|
adapter repo can point ``base_model_name_or_path`` at a base carrying a poisoned
|
|
pickle or HIGH auto_map code. For a remote repo id we fetch ONLY the small
|
|
``adapter_config.json`` (metadata; never a weight file) and read the base. Use
|
|
this in the gate paths so a remote LoRA base is scanned, not just the adapter.
|
|
|
|
Returns the base model id, or ``None`` when the identifier is not a LoRA adapter
|
|
or the base cannot be determined (the caller still scans the identifier itself).
|
|
|
|
A genuine 404 (no ``adapter_config.json`` / repo absent) is distinguished from a
|
|
transient error: the latter is retried once, then logged as a WARNING (a missed
|
|
base would be scanned by neither gate), so a network blip does not silently and
|
|
invisibly skip the base.
|
|
"""
|
|
# Local path: reuse the existing directory reader (identical behavior).
|
|
try:
|
|
if is_local_path(identifier):
|
|
return get_base_model_from_lora(identifier)
|
|
except Exception:
|
|
return get_base_model_from_lora(identifier)
|
|
|
|
# Remote repo id: read base_model_name_or_path from adapter_config.json only.
|
|
from huggingface_hub import hf_hub_download
|
|
from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError
|
|
|
|
last_exc = None
|
|
for _attempt in range(2): # one retry: a transient blip must not skip the base
|
|
try:
|
|
cfg_path = hf_hub_download(
|
|
identifier, "adapter_config.json", token = hf_token if hf_token else None
|
|
)
|
|
except (EntryNotFoundError, RepositoryNotFoundError):
|
|
# No adapter_config.json -> not a resolvable LoRA; caller scans the identifier.
|
|
return None
|
|
except Exception as exc: # transient / auth / network -> retry once
|
|
last_exc = exc
|
|
continue
|
|
try:
|
|
with open(cfg_path, "r") as f:
|
|
base_model = json.load(f).get("base_model_name_or_path")
|
|
except Exception as exc:
|
|
logger.warning("Could not parse adapter_config.json for '%s': %s", identifier, exc)
|
|
return None
|
|
if base_model:
|
|
logger.info(
|
|
"Detected base model from remote adapter_config.json (%s): %s",
|
|
identifier,
|
|
base_model,
|
|
)
|
|
return base_model # may be None if the key is absent (still a valid answer)
|
|
|
|
# Both attempts failed transiently: log loudly -- a missed base is gated by neither gate.
|
|
logger.warning(
|
|
"Could not resolve remote LoRA base for '%s' after retry (%s); its base, if "
|
|
"any, will not be added to the security scan targets.",
|
|
identifier,
|
|
type(last_exc).__name__ if last_exc else "unknown",
|
|
)
|
|
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 a YAML file.
|
|
|
|
Looks in configs/model_defaults/ (incl. subfolders) by model name or its
|
|
MODEL_NAME_MAPPING aliases, else falls back to default.yaml. Returns the
|
|
parameter dict, or {} if none found.
|
|
"""
|
|
# No model selected yet (or a non-string id): nothing to load. Guard before
|
|
# the .lower() calls below so this doesn't raise and get logged as
|
|
# "Error loading model defaults for None: 'NoneType' object has no attribute
|
|
# 'lower'".
|
|
if not isinstance(model_name, str) or not model_name:
|
|
return {}
|
|
try:
|
|
script_dir = Path(__file__).parent.parent.parent
|
|
defaults_dir = script_dir / "assets" / "configs" / "model_defaults"
|
|
|
|
# Check the mapping first
|
|
if model_name.lower() in _REVERSE_MODEL_MAPPING:
|
|
canonical_file = _REVERSE_MODEL_MAPPING[model_name.lower()]
|
|
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
|
|
|
|
# For local paths (e.g. /home/.../Spark-TTS-0.5B/LLM from
|
|
# adapter_config.json, or C:\Users\...\model on Windows), match the
|
|
# last 1-2 path components against the registry (e.g. "Spark-TTS-0.5B/LLM").
|
|
_is_local_path = is_local_path(model_name)
|
|
# Normalize Windows backslash paths so Path().parts splits correctly
|
|
# on POSIX/WSL hosts (pathlib treats backslashes as literals on Linux).
|
|
_normalized = normalize_path(model_name) if _is_local_path else model_name
|
|
if model_name.lower() not in _REVERSE_MODEL_MAPPING and _is_local_path:
|
|
parts = Path(_normalized).parts
|
|
for depth in [2, 1]:
|
|
if len(parts) >= depth:
|
|
suffix = "/".join(parts[-depth:])
|
|
if suffix.lower() in _REVERSE_MODEL_MAPPING:
|
|
canonical_file = _REVERSE_MODEL_MAPPING[suffix.lower()]
|
|
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
|
|
|
|
# Exact model name match (backward compatibility). For local paths,
|
|
# use only the dir basename to avoid passing absolute paths (e.g.
|
|
# C:\...) into rglob, which raises "Non-relative patterns are
|
|
# unsupported" on Windows.
|
|
_lookup_name = Path(_normalized).name if _is_local_path else model_name
|
|
model_filename = _lookup_name.replace("/", "_") + ".yaml"
|
|
# Search 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 # Local file vs HF model?
|
|
is_cached: bool # Already in HF cache?
|
|
is_vision: bool # Vision model?
|
|
is_lora: bool # LoRA adapter?
|
|
is_gguf: bool = False # GGUF model?
|
|
is_audio: bool = False # 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_mtp_file: Optional[str] = None # Full path to the separate MTP drafter (local mode)
|
|
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, auto-detecting the
|
|
base model from adapter config.
|
|
|
|
Args:
|
|
lora_path: Path to the LoRA adapter directory
|
|
hf_token: HF token for vision detection
|
|
"""
|
|
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
|
|
|
|
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
|
|
|
|
is_vision = is_vision_model(base_model, hf_token = hf_token)
|
|
audio_type = detect_audio_type(base_model, hf_token = hf_token)
|
|
|
|
display_name = lora_path_obj.name
|
|
identifier = lora_path # path is the 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 (HF repo or local
|
|
path), for FastAPI routes that send sanitized paths.
|
|
|
|
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 quant variant (e.g. "Q4_K_M") to load
|
|
via -hf for remote repos; None auto-selects via _pick_best_gguf().
|
|
|
|
Returns:
|
|
ModelConfig or None if it 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
|
|
|
|
# Reuse a cached case-variant's exact repo_id spelling to avoid
|
|
# one-time re-downloads after #2592.
|
|
if not is_local:
|
|
resolved_identifier = resolve_cached_repo_id_case(identifier)
|
|
if resolved_identifier != identifier:
|
|
logger.info(
|
|
"Using cached repo_id casing '%s' for requested '%s'",
|
|
resolved_identifier,
|
|
identifier,
|
|
)
|
|
identifier = resolved_identifier
|
|
path = resolved_identifier
|
|
|
|
# Auto-detect GGUF models (check before LoRA/vision detection)
|
|
if is_local:
|
|
if gguf_variant:
|
|
gguf_file = _find_local_gguf_by_variant(path, gguf_variant)
|
|
else:
|
|
gguf_file = detect_gguf_model(path)
|
|
if gguf_file:
|
|
display_name = Path(gguf_file).stem
|
|
logger.info(f"Detected local GGUF model: {gguf_file}")
|
|
|
|
# Vision: check base model, then look for mmproj
|
|
mmproj_file = None
|
|
gguf_is_vision = False
|
|
gguf_dir = Path(gguf_file).parent
|
|
|
|
# Is this a vision model, per 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}")
|
|
|
|
# Direct file selections may point into a quant subdir while
|
|
# mmproj-*.gguf lives at the snapshot root.
|
|
companion_root = _local_gguf_companion_search_root(path, gguf_file)
|
|
mmproj_file = detect_mmproj_file(gguf_file, search_root = companion_root)
|
|
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}")
|
|
|
|
# Separate MTP drafter sibling (Gemma 4), mirroring mmproj.
|
|
mtp_file = detect_mtp_file(gguf_file, search_root = companion_root)
|
|
if mtp_file:
|
|
logger.info(f"Detected MTP drafter: {mtp_file}")
|
|
|
|
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,
|
|
gguf_mtp_file = mtp_file,
|
|
)
|
|
else:
|
|
# Does the HF repo contain GGUF files?
|
|
gguf_filename = detect_gguf_model_remote(identifier, hf_token = hf_token)
|
|
if gguf_filename:
|
|
# Preflight: verify llama-server binary exists before a multi-GB
|
|
# download. include_denied: a transiently locked binary still
|
|
# exists (the lock clears long before the download finishes; the
|
|
# load itself reports a still-locked binary distinctly).
|
|
from core.inference.llama_cpp import (
|
|
LLAMA_SERVER_NOT_FOUND_DETAIL,
|
|
LlamaCppBackend,
|
|
LlamaServerNotFoundError,
|
|
)
|
|
|
|
if not LlamaCppBackend._find_llama_server_binary(include_denied = True):
|
|
raise LlamaServerNotFoundError(LLAMA_SERVER_NOT_FOUND_DETAIL)
|
|
|
|
# list_gguf_variants() detects vision & resolves the variant
|
|
variants, has_vision = list_gguf_variants(identifier, hf_token = hf_token)
|
|
variant = gguf_variant
|
|
if not variant: # auto-select best quant
|
|
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 (adapter_config.json on disk)
|
|
if not is_lora and is_local:
|
|
detected_base = (
|
|
get_base_model_from_lora(path) if _looks_like_lora_adapter(Path(path)) else None
|
|
)
|
|
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. When offline, huggingface_hub
|
|
# raises OfflineModeIsEnabled in ~0ms; we fall through to the cache.
|
|
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}")
|
|
|
|
# API may have failed; adapter_config.json could still be cached.
|
|
if not is_lora:
|
|
for snap in _iter_hf_cache_snapshots(identifier):
|
|
if (snap / "adapter_config.json").is_file():
|
|
is_lora = True
|
|
logger.info(f"Auto-detected cached LoRA adapter: '{identifier}'")
|
|
break
|
|
|
|
# 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: fetch 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 ModelConfig from UI dropdown/search selections (base models and LoRAs)."""
|
|
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
|
|
|
|
# Resolve display names via the 'local_models' parameter
|
|
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
|
|
|
|
# Strip 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
|
|
|
|
if not is_local:
|
|
resolved_identifier = resolve_cached_repo_id_case(identifier)
|
|
if resolved_identifier != identifier:
|
|
identifier = resolved_identifier
|
|
path = resolved_identifier
|
|
|
|
# Keep existing local GGUF selections on the llama-server path. This
|
|
# constructor is still used by older inference helpers and must not
|
|
# describe a .gguf weight file as loadable by FastVisionModel.
|
|
if is_local and not is_lora and detect_gguf_model(path):
|
|
gguf_config = cls.from_identifier(path, hf_token = hf_token)
|
|
if gguf_config is not None:
|
|
gguf_config.display_name = display_name
|
|
return gguf_config
|
|
|
|
# --- Base Model and Vision Detection ---
|
|
base_model = None
|
|
is_vision = False
|
|
|
|
if is_lora:
|
|
# A LoRA MUST have a 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 comes from its base model.
|
|
is_vision = is_vision_model(base_model, hf_token = hf_token)
|
|
else:
|
|
# Base model: 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, # None for base models, set for LoRAs
|
|
)
|