diff --git a/.github/workflows/studio-ui-smoke.yml b/.github/workflows/studio-ui-smoke.yml index 30280c281e..0ad55ebd6d 100644 --- a/.github/workflows/studio-ui-smoke.yml +++ b/.github/workflows/studio-ui-smoke.yml @@ -237,6 +237,54 @@ jobs: kill "${STUDIO_EXTRA_PID}" 2>/dev/null || true sleep 2 + # Model-picker per-model-config regression (PR #7207 re-land of #6647). + # Fourth Unsloth on its own port; loads the tiny GGUF and drives the + # picker's run-settings surface: Context Length persists across a reload, + # Reset clears the stored override (never pins it), and the infra models + # (RAG embedder + llama.cpp probe) stay hidden from the picker. + - name: Reset auth + boot Unsloth for model-config tests (port 18898) + run: | + unsloth studio reset-password + mkdir -p logs + UNSLOTH_API_ONLY=1 unsloth studio -H 127.0.0.1 -p 18898 \ + > logs/studio_modelcfg.log 2>&1 & + echo "STUDIO_MODELCFG_PID=$!" >> "$GITHUB_ENV" + + - name: Wait for /api/health on 18898 + run: | + for i in $(seq 1 180); do + if curl -fs "http://127.0.0.1:18898/api/health" > /tmp/health4.json; then + jq -e '.status == "healthy"' /tmp/health4.json && break + fi + sleep 1 + done + jq -e '.status == "healthy"' /tmp/health4.json + + - name: Pass bootstrap pw for model-config test + run: | + NEW="CIModelCfg-$(python -c 'import secrets; print(secrets.token_urlsafe(16))')" + echo "::add-mask::$NEW" + echo "STUDIO_MODELCFG_NEW_PW=$NEW" >> "$GITHUB_ENV" + + - name: Drive model-picker per-model-config with Playwright + env: + BASE_URL: http://127.0.0.1:18898 + STUDIO_NEW_PW: ${{ env.STUDIO_MODELCFG_NEW_PW }} + PW_ART_DIR: logs/playwright_modelcfg + STUDIO_UI_STRICT: '1' + GGUF_REPO: ${{ env.GGUF_REPO }} + GGUF_VARIANT: ${{ env.GGUF_VARIANT }} + STUDIO_MODEL_HINT: gemma-3-270m + run: | + mkdir -p logs/playwright_modelcfg + python tests/studio/playwright_model_config.py + + - name: Stop fourth Unsloth + if: always() + run: | + kill "${STUDIO_MODELCFG_PID}" 2>/dev/null || true + sleep 2 + # IME + multilingual paste regression (issue #5318 / PR #5327). # Third Unsloth on its own port so a hang here cannot poison the # earlier UI tests. No GGUF -- the bug surface is the composer. @@ -297,12 +345,14 @@ jobs: path: | logs/studio.log logs/studio_extra.log + logs/studio_modelcfg.log logs/studio_ime.log logs/install.log logs/server-logs/ logs/playwright logs/playwright-permissions-* logs/playwright_extra + logs/playwright_modelcfg logs/playwright_ime logs/studio-permissions-*.log retention-days: 7 diff --git a/studio/backend/hub/routes/inventory.py b/studio/backend/hub/routes/inventory.py index 4b6c179a2b..1ffadf0544 100644 --- a/studio/backend/hub/routes/inventory.py +++ b/studio/backend/hub/routes/inventory.py @@ -28,6 +28,7 @@ from hub.schemas.inventory import ( CachedModelsResponse, DeleteCachedModelResponse, GgufVariantsResponse, + HiddenModelsResponse, LocalModelListResponse, ModelsFolderResponse, RecommendedFoldersResponse, @@ -214,6 +215,16 @@ async def list_cached_models( return await cache_inventory.list_cached_models_response(hf_token) +@router.get("/hidden-models", response_model = HiddenModelsResponse) +async def list_hidden_models(current_subject: str = Depends(get_current_subject)): + import asyncio + + from routes.models import hidden_model_matchers + + needles, exact_ids, exact_paths = await asyncio.to_thread(hidden_model_matchers) + return HiddenModelsResponse(needles = needles, exact_ids = exact_ids, exact_paths = exact_paths) + + @router.delete( "/delete-cached", response_model = DeleteCachedModelResponse, diff --git a/studio/backend/hub/schemas/inventory.py b/studio/backend/hub/schemas/inventory.py index ef95efe2f2..19d6da3e11 100644 --- a/studio/backend/hub/schemas/inventory.py +++ b/studio/backend/hub/schemas/inventory.py @@ -160,6 +160,7 @@ class CachedRepoBase(BaseModel): repo_id: str size_bytes: int = 0 cache_path: Optional[str] = None + last_modified: Optional[float] = None partial: bool = False partial_transport: Optional[str] = None inventory_id: Optional[str] = None @@ -189,6 +190,12 @@ class CachedModelsResponse(BaseModel): cached: List[CachedModelRepo] = Field(default_factory = list) +class HiddenModelsResponse(BaseModel): + needles: List[str] = Field(default_factory = list) + exact_ids: List[str] = Field(default_factory = list) + exact_paths: List[str] = Field(default_factory = list) + + class AddScanFolderRequest(BaseModel): """Request body for adding a custom scan folder.""" diff --git a/studio/backend/hub/services/models/cache_inventory.py b/studio/backend/hub/services/models/cache_inventory.py index 54a25482f2..c1b864bb63 100644 --- a/studio/backend/hub/services/models/cache_inventory.py +++ b/studio/backend/hub/services/models/cache_inventory.py @@ -31,6 +31,7 @@ from hub.services.models.common import ( _is_checkpoint_weight_name, _is_gguf_filename, _is_main_gguf_filename, + _is_mmproj_filename, _is_transformers_safetensors_weight_name, _local_inventory_id, _prefer_complete_larger, @@ -132,6 +133,39 @@ def _repo_has_gguf_files(repo_info) -> bool: return _repo_gguf_size_bytes(repo_info) > 0 +def _blob_mtime(file_obj) -> float: + ts = getattr(file_obj, "blob_last_modified", None) + if isinstance(ts, (int, float)) and ts > 0: + return float(ts) + blob_path = getattr(file_obj, "blob_path", None) + if blob_path: + try: + return float(Path(blob_path).stat().st_mtime) + except OSError: + pass + return 0.0 + + +def _repo_gguf_last_modified(repo_info) -> float: + latest = 0.0 + for revision in repo_info.revisions: + for f in revision.files: + if _is_main_gguf_filename(f.file_name): + latest = max(latest, _blob_mtime(f)) + return latest + + +def _repo_has_mmproj(repo_info) -> bool: + # An mmproj file only makes a repo vision-capable when it is an actual GGUF + # projector; a non-GGUF sidecar (e.g. mmproj_config.json) does not, and the + # runtime's projector detection is GGUF-only. + return any( + _is_gguf_filename(f.file_name) and _is_mmproj_filename(f.file_name) + for revision in repo_info.revisions + for f in revision.files + ) + + def _cached_repo_file_name(file_obj) -> str: file_path = getattr(file_obj, "file_path", None) if file_path: @@ -291,6 +325,7 @@ def _scan_cached_gguf() -> list[dict]: continue key = repo_id.lower() existing = seen_lower.get(key) + last_modified = _repo_gguf_last_modified(repo_info) row = { "repo_id": repo_id, "size_bytes": max(total_size, variant_state_size), @@ -300,6 +335,9 @@ def _scan_cached_gguf() -> list[dict]: # per-variant detail lives on GgufVariantDetail. "partial_transport": None, } + last_modified = max(last_modified, (existing or {}).get("last_modified", 0.0)) + if last_modified > 0: + row["last_modified"] = last_modified row.update( _cache_inventory_fields( repo_id, @@ -308,11 +346,20 @@ def _scan_cached_gguf() -> list[dict]: requires_variant = True, ) ) + if _repo_has_mmproj(repo_info): + row["capabilities"]["supports_vision"] = True # Visible infra variants remain management-only. if is_hidden_infra: row["capabilities"]["can_chat"] = False if _prefer_cache_row(row, existing): + if existing and existing["capabilities"].get("supports_vision"): + row["capabilities"]["supports_vision"] = True seen_lower[key] = row + else: + if last_modified > existing.get("last_modified", 0.0): + existing["last_modified"] = last_modified + if row["capabilities"].get("supports_vision"): + existing["capabilities"]["supports_vision"] = True except Exception as e: repo_label = getattr(repo_info, "repo_id", "") logger.warning(f"Skipping cached GGUF repo {repo_label}: {e}") @@ -340,13 +387,14 @@ class _CachedNonGgufPayload(NamedTuple): size_bytes: int has_runnable_weights: bool model_format: ModelFormat + last_modified: float def _repo_non_gguf_model_payload(repo_info) -> _CachedNonGgufPayload: - all_weight_blobs: dict[str, int] = {} - adapter_blobs: dict[str, int] = {} - safetensors_blobs: dict[str, int] = {} - checkpoint_blobs: dict[str, int] = {} + all_weight_blobs: dict[str, tuple[int, float]] = {} + adapter_blobs: dict[str, tuple[int, float]] = {} + safetensors_blobs: dict[str, tuple[int, float]] = {} + checkpoint_blobs: dict[str, tuple[int, float]] = {} has_config = False has_adapter_config = False has_adapter_weights = False @@ -354,12 +402,15 @@ def _repo_non_gguf_model_payload(repo_info) -> _CachedNonGgufPayload: has_transformers_safetensors = False has_checkpoint = False - def _record_blob(target: dict[str, int], file_obj, rev_id: str, file_name: str) -> None: + def _record_blob( + target: dict[str, tuple[int, float]], file_obj, rev_id: str, file_name: str + ) -> None: blob_path = getattr(file_obj, "blob_path", None) size = int(file_obj.size_on_disk or 0) key = str(blob_path) if blob_path else f"{rev_id}:{file_name}" - target[key] = size - all_weight_blobs[key] = size + value = (size, _blob_mtime(file_obj)) + target[key] = value + all_weight_blobs[key] = value for revision in repo_info.revisions: rev_id = getattr(revision, "commit_hash", None) or str(id(revision)) @@ -403,18 +454,19 @@ def _repo_non_gguf_model_payload(repo_info) -> _CachedNonGgufPayload: or "unknown" ) if model_format == "adapter": - size_bytes = sum(adapter_blobs.values()) + selected_blobs = adapter_blobs elif model_format == "safetensors": - size_bytes = sum(safetensors_blobs.values()) + selected_blobs = safetensors_blobs elif model_format == "checkpoint": - size_bytes = sum(checkpoint_blobs.values()) + selected_blobs = checkpoint_blobs else: - size_bytes = sum(all_weight_blobs.values()) + selected_blobs = all_weight_blobs return _CachedNonGgufPayload( - size_bytes = size_bytes, + size_bytes = sum(size for size, _mtime in selected_blobs.values()), has_runnable_weights = model_format != "unknown", model_format = model_format, + last_modified = max((mtime for _size, mtime in selected_blobs.values()), default = 0.0), ) @@ -544,6 +596,12 @@ def _scan_cached_models() -> list[dict]: ), **_cached_model_local_metadata(repo_path), } + last_modified = max( + payload.last_modified, + (existing or {}).get("last_modified", 0.0), + ) + if last_modified > 0: + row["last_modified"] = last_modified row.update( _cache_inventory_fields( repo_id, @@ -553,6 +611,8 @@ def _scan_cached_models() -> list[dict]: ) if _prefer_cache_row(row, existing): seen_lower[key] = row + elif last_modified > existing.get("last_modified", 0.0): + existing["last_modified"] = last_modified except Exception as e: repo_label = getattr(repo_info, "repo_id", "") logger.warning(f"Skipping cached model repo {repo_label}: {e}") diff --git a/studio/backend/main.py b/studio/backend/main.py index 48675b9539..3f244dc22e 100644 --- a/studio/backend/main.py +++ b/studio/backend/main.py @@ -315,6 +315,7 @@ from hub.routes import ( datasets_router as hub_datasets_router, token_router as hub_token_router, ) +from picker.routes import templates_router as picker_templates_router from hub.schemas.downloads import TransportCapabilities from hub.utils.download_registry import ( get_download_transport_capabilities, @@ -764,6 +765,7 @@ _BODY_PROTECTED_PREFIXES = ( "/v1/completions", "/p/", "/api/inference", + "/api/picker", "/api/data-recipe", "/api/datasets", "/api/hub", @@ -995,6 +997,7 @@ app.include_router(rag_router, prefix = "/api/rag", tags = ["rag"]) app.include_router(training_history_router, prefix = "/api/train", tags = ["training-history"]) app.include_router(hub_inventory_router, prefix = "/api/hub", tags = ["hub"]) app.include_router(hub_datasets_router, prefix = "/api/hub/datasets", tags = ["hub"]) +app.include_router(picker_templates_router, prefix = "/api/picker", tags = ["picker"]) app.include_router(hub_token_router, prefix = "/api/hub", tags = ["hub"]) # Re-wrap client-error responses on the /v1/* surface into OpenAI/Anthropic diff --git a/studio/backend/models/inference.py b/studio/backend/models/inference.py index d51d35189b..580a74dddf 100644 --- a/studio/backend/models/inference.py +++ b/studio/backend/models/inference.py @@ -18,6 +18,8 @@ from pydantic import ( model_validator, ) +from picker.schemas import MAX_CHAT_TEMPLATE_BYTES + class LoadRequest(BaseModel): """Request to load a model for inference""" @@ -54,8 +56,16 @@ class LoadRequest(BaseModel): @field_validator("chat_template_override") @classmethod def normalize_blank_chat_template_override(cls, value: Optional[str]) -> Optional[str]: - if value is not None and value.strip() == "": + if value is None: return None + # Char count is a lower bound on UTF-8 byte length: reject an oversized + # template before spending work encoding it. + if len(value) > MAX_CHAT_TEMPLATE_BYTES: + raise ValueError(f"Chat template exceeds the {MAX_CHAT_TEMPLATE_BYTES}-byte limit.") + if value.strip() == "": + return None + if len(value.encode("utf-8")) > MAX_CHAT_TEMPLATE_BYTES: + raise ValueError(f"Chat template exceeds the {MAX_CHAT_TEMPLATE_BYTES}-byte limit.") return value cache_type_kv: Optional[str] = Field( @@ -206,6 +216,13 @@ class ValidateModelRequest(BaseModel): description = "Also read the native context length from the local GGUF header. " "Opt-in so the normal load preflight doesn't pay for a cache scan it doesn't need.", ) + include_chat_template: bool = Field( + False, + description = "Also read the embedded chat template from the local GGUF header, so a " + "native (picked / drag-drop) file's default template can be shown before it is loaded. " + "Opt-in and, like include_context_length, a metadata-only probe that skips the training " + "guard. Only the leased file's own embedded template is read, never sibling sidecars.", + ) class TransformersUpgradeInfo(BaseModel): @@ -266,6 +283,11 @@ class ValidateModelResponse(BaseModel): description = "MoE expert-layer count (the manual --n-cpu-moe ceiling), read from the GGUF " "header alongside context_length; 0 for dense models, None when not read.", ) + chat_template: Optional[str] = Field( + None, + description = "Embedded GGUF chat template, read from the header when include_chat_template " + "is set (native lease-backed picks); None for non-GGUF, over-cap, or not-read templates.", + ) # Additive fields; the consuming consent dialog ships in a follow-up frontend PR. requires_transformers_upgrade: bool = Field( False, diff --git a/studio/backend/picker/__init__.py b/studio/backend/picker/__init__.py new file mode 100644 index 0000000000..32014236c6 --- /dev/null +++ b/studio/backend/picker/__init__.py @@ -0,0 +1,2 @@ +# SPDX-License-Identifier: AGPL-3.0-only +# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0 diff --git a/studio/backend/picker/routes/__init__.py b/studio/backend/picker/routes/__init__.py new file mode 100644 index 0000000000..c0e988c8bb --- /dev/null +++ b/studio/backend/picker/routes/__init__.py @@ -0,0 +1,6 @@ +# SPDX-License-Identifier: AGPL-3.0-only +# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0 + +from .templates import router as templates_router + +__all__ = ["templates_router"] diff --git a/studio/backend/picker/routes/templates.py b/studio/backend/picker/routes/templates.py new file mode 100644 index 0000000000..02b8bf7184 --- /dev/null +++ b/studio/backend/picker/routes/templates.py @@ -0,0 +1,45 @@ +# SPDX-License-Identifier: AGPL-3.0-only +# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0 + +from __future__ import annotations + +import asyncio +from typing import Optional + +from fastapi import APIRouter, Body, Depends, Query + +from auth.authentication import get_current_subject +from hub.dependencies import get_hf_token + +from ..schemas import ( + MAX_CHAT_TEMPLATE_BYTES, + ModelTemplateResponse, + ValidateChatTemplateRequest, + ValidateChatTemplateResponse, +) +from ..service import read_default_chat_template, validate_chat_template + +router = APIRouter() + + +@router.post("/validate-chat-template", response_model = ValidateChatTemplateResponse) +async def validate_chat_template_route( + body: ValidateChatTemplateRequest = Body(...), + current_subject: str = Depends(get_current_subject), +) -> ValidateChatTemplateResponse: + return await asyncio.to_thread(validate_chat_template, body.template) + + +@router.get("/chat-template/{model_name:path}", response_model = ModelTemplateResponse) +async def get_default_chat_template_route( + model_name: str, + gguf_variant: Optional[str] = Query(None), + hf_token: Optional[str] = Depends(get_hf_token), + current_subject: str = Depends(get_current_subject), +) -> ModelTemplateResponse: + template = await asyncio.to_thread( + read_default_chat_template, model_name, hf_token, gguf_variant + ) + if template is not None and len(template.encode("utf-8")) > MAX_CHAT_TEMPLATE_BYTES: + template = None + return ModelTemplateResponse(model_name = model_name, chat_template = template) diff --git a/studio/backend/picker/schemas.py b/studio/backend/picker/schemas.py new file mode 100644 index 0000000000..b4f956188f --- /dev/null +++ b/studio/backend/picker/schemas.py @@ -0,0 +1,32 @@ +# SPDX-License-Identifier: AGPL-3.0-only +# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0 + +from typing import Optional + +from pydantic import BaseModel, Field, field_validator + +# Mirror the frontend's 64 KiB chat-template contract (per-model-config.ts) at +# the API boundary so a direct caller cannot make Jinja parse an oversized +# template. MaxBodyMiddleware only caps the whole request body, not this field. +MAX_CHAT_TEMPLATE_BYTES = 65_536 + + +class ValidateChatTemplateRequest(BaseModel): + template: str = Field(default = "") + + @field_validator("template") + @classmethod + def _enforce_template_size(cls, value: str) -> str: + if len(value.encode("utf-8")) > MAX_CHAT_TEMPLATE_BYTES: + raise ValueError(f"Chat template exceeds the {MAX_CHAT_TEMPLATE_BYTES}-byte limit.") + return value + + +class ValidateChatTemplateResponse(BaseModel): + valid: bool + error: Optional[str] = None + + +class ModelTemplateResponse(BaseModel): + model_name: str + chat_template: Optional[str] = None diff --git a/studio/backend/picker/service.py b/studio/backend/picker/service.py new file mode 100644 index 0000000000..13065b2920 --- /dev/null +++ b/studio/backend/picker/service.py @@ -0,0 +1,426 @@ +# SPDX-License-Identifier: AGPL-3.0-only +# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0 + +from __future__ import annotations + +import json +import logging +import os +import re +from pathlib import Path +from typing import Optional + +from hub.services.models.folder_browser import ( + _build_browse_allowlist, + _is_path_inside_allowlist, +) +from hub.utils.gguf import extract_quant_label, iter_hf_cache_snapshots +from utils.models.gguf_metadata import read_gguf_chat_template +from utils.models.model_config import ( + _extract_quant_label, + _is_big_endian_gguf_path, + _is_mmproj, + _is_mtp_drafter, +) +from utils.paths.path_utils import ( + is_local_path, + normalize_path, + resolve_cached_repo_id_case, +) + +from .schemas import MAX_CHAT_TEMPLATE_BYTES, ValidateChatTemplateResponse + +logger = logging.getLogger(__name__) + +_VALID_REPO_ID = re.compile(r"^[A-Za-z0-9._-]+/[A-Za-z0-9._-]+$") + + +def _is_valid_repo_id(repo_id: str) -> bool: + return bool(_VALID_REPO_ID.fullmatch(repo_id)) + + +_TOKENIZER_CONFIG_PATHS = ("tokenizer_config.json", "LLM/tokenizer_config.json") +_JINJA_TEMPLATE_PATHS = ("chat_template.jinja", "LLM/chat_template.jinja") +_PROCESSOR_TEMPLATE_PATHS = ("chat_template.json", "LLM/chat_template.json") + +# Cap sidecar reads so a malformed or hostile metadata file cannot exhaust memory +# before its template is size-checked. The JSON envelope may exceed a bare template +# (it carries other tokenizer metadata); the extracted template is still bounded by +# MAX_CHAT_TEMPLATE_BYTES downstream. +MAX_TEMPLATE_METADATA_BYTES = 4 * 1024 * 1024 + + +def _read_bounded_text(path: Path, limit: int) -> Optional[str]: + """Read at most `limit` bytes of UTF-8 text; None if larger or unreadable.""" + try: + with path.open("rb") as f: + data = f.read(limit + 1) + except OSError: + return None + if len(data) > limit: + return None + try: + return data.decode("utf-8") + except UnicodeError: + return None + + +def _leaf_inside_allowlist(path: Path, allow_roots: Optional[list[Path]]) -> bool: + # Block symlinked children from escaping the validated directory (realpath-checked). + # None = trusted caller (HF cache / remote download). + return allow_roots is None or _is_path_inside_allowlist(path, allow_roots) + + +def validate_chat_template(template: str) -> ValidateChatTemplateResponse: + text = (template or "").strip() + if not text: + return ValidateChatTemplateResponse(valid = True, error = None) + # Import Jinja lazily: optional at runtime (e.g. GGUF-only installs), so a + # missing dependency must not crash API startup. + try: + from jinja2 import TemplateError + from jinja2.ext import Extension + from jinja2.sandbox import ImmutableSandboxedEnvironment + except ImportError: + return ValidateChatTemplateResponse(valid = True, error = None) + + class _GenerationTag(Extension): + # Accept Transformers' {% generation %} assistant-mask tag so a pasted HF + # chat template validates (we only parse it). + tags = {"generation"} + + def parse(self, parser): + next(parser.stream) + return parser.parse_statements(["name:endgeneration"], drop_needle = True) + + try: + env = ImmutableSandboxedEnvironment( + trim_blocks = True, + lstrip_blocks = True, + extensions = ["jinja2.ext.loopcontrols", _GenerationTag], + ) + env.parse(text) + return ValidateChatTemplateResponse(valid = True, error = None) + except TemplateError as exc: + message = getattr(exc, "message", None) or str(exc) + lineno = getattr(exc, "lineno", None) + if lineno: + message = f"Line {lineno}: {message}" + return ValidateChatTemplateResponse(valid = False, error = message) + except Exception as exc: + return ValidateChatTemplateResponse(valid = False, error = str(exc)) + + +def _chat_template_from_tokenizer_config(config: dict) -> Optional[str]: + if not isinstance(config, dict): + return None + raw = config.get("chat_template") + if isinstance(raw, str) and raw.strip(): + return raw + if isinstance(raw, list): + fallback: Optional[str] = None + for entry in raw: + if not isinstance(entry, dict): + continue + template = entry.get("template") + if not isinstance(template, str): + continue + if entry.get("name") == "default": + return template + if fallback is None: + fallback = template + return fallback + return None + + +def _chat_template_from_jinja_file( + dir_path: Path, allow_roots: Optional[list[Path]] = None +) -> Optional[str]: + for rel in _JINJA_TEMPLATE_PATHS: + template_file = dir_path / rel + if not template_file.exists() or not _leaf_inside_allowlist(template_file, allow_roots): + continue + try: + if template_file.stat().st_size > MAX_CHAT_TEMPLATE_BYTES: + continue + template = template_file.read_text(encoding = "utf-8") + except Exception: + continue + if template.strip(): + return template + return None + + +def _chat_template_from_processor_payload(payload: object) -> Optional[str]: + # processor chat_template.json may be the template string itself or a + # {name: template} map, not only a tokenizer_config-shaped object. + if isinstance(payload, str): + return payload if payload.strip() else None + template = _chat_template_from_tokenizer_config(payload) # type: ignore[arg-type] + if template: + return template + if isinstance(payload, dict): + # Named-template map: prefer "default", else the first non-empty entry + # (mirrors the tokenizer-config list fallback). + default = payload.get("default") + if isinstance(default, str) and default.strip(): + return default + for value in payload.values(): + if isinstance(value, str) and value.strip(): + return value + return None + + +def _chat_template_from_processor_json( + dir_path: Path, allow_roots: Optional[list[Path]] = None +) -> Optional[str]: + for rel in _PROCESSOR_TEMPLATE_PATHS: + config_file = dir_path / rel + if not config_file.exists() or not _leaf_inside_allowlist(config_file, allow_roots): + continue + raw = _read_bounded_text(config_file, MAX_TEMPLATE_METADATA_BYTES) + if raw is None: + continue + try: + payload = json.loads(raw) + except Exception: + continue + template = _chat_template_from_processor_payload(payload) + if template: + return template + return None + + +def _chat_template_from_tokenizer_dir( + dir_path: Path, allow_roots: Optional[list[Path]] = None +) -> Optional[str]: + jinja = _chat_template_from_jinja_file(dir_path, allow_roots) + if jinja: + return jinja + for rel in _TOKENIZER_CONFIG_PATHS: + config_file = dir_path / rel + if not config_file.exists() or not _leaf_inside_allowlist(config_file, allow_roots): + continue + raw = _read_bounded_text(config_file, MAX_TEMPLATE_METADATA_BYTES) + if raw is None: + continue + try: + config = json.loads(raw) + except Exception: + continue + template = _chat_template_from_tokenizer_config(config) + if template: + return template + return _chat_template_from_processor_json(dir_path, allow_roots) + + +_GGUF_SCAN_MAX_DEPTH = 2 + + +def _iter_ggufs(dir_path: Path) -> list[Path]: + if dir_path == dir_path.parent: + return [] + root = str(dir_path) + found: list[Path] = [] + for current, dirs, files in os.walk(root, followlinks = False): + rel = os.path.relpath(current, root) + depth = 0 if rel == os.curdir else rel.count(os.sep) + 1 + if depth >= _GGUF_SCAN_MAX_DEPTH: + dirs[:] = [] + for name in files: + if not name.lower().endswith(".gguf") or _is_mmproj(name): + continue + path = Path(current) / name + try: + rel = path.relative_to(dir_path).as_posix() + except ValueError: + rel = name + quant = _extract_quant_label(rel) + if _is_mtp_drafter(rel) or _is_big_endian_gguf_path(rel, quant): + continue + found.append(path) + return found + + +def _variant_matches(relative_path: str, needle: str) -> bool: + quant = _extract_quant_label(relative_path).lower() + if quant == needle: + return True + if extract_quant_label(relative_path).lower() == needle: + return True + prefix = f"{needle}-" + if not quant.startswith(prefix): + return False + suffix = quant[len(prefix) :] + if not suffix.endswith("bpw"): + return False + value = suffix[:-3] + return bool(value) and value.replace(".", "", 1).isdigit() + + +_GGUF_SPLIT_INDEX_RE = re.compile(r"-(\d{3,})-of-\d{3,}$", re.IGNORECASE) + + +def _is_nonfirst_gguf_split(path: Path) -> bool: + match = _GGUF_SPLIT_INDEX_RE.search(path.stem) + return match is not None and int(match.group(1)) != 1 + + +def _find_gguf_in_dir(dir_path: Path, gguf_variant: Optional[str]) -> Optional[Path]: + try: + ggufs = sorted(_iter_ggufs(dir_path)) + except OSError: + return None + if not ggufs: + return None + needle = (gguf_variant or "").strip().lower() + if needle: + for path in ggufs: + try: + relative = path.relative_to(dir_path).as_posix() + except ValueError: + relative = path.name + if _variant_matches(relative, needle): + return path + return None + candidates = [path for path in ggufs if not _is_nonfirst_gguf_split(path)] or ggufs + try: + return max(candidates, key = lambda path: path.stat().st_size) + except OSError: + return candidates[0] + + +def _chat_template_from_dir( + dir_path: Path, + gguf_variant: Optional[str] = None, + allow_roots: Optional[list[Path]] = None, +) -> Optional[str]: + def from_gguf() -> Optional[str]: + gguf = _find_gguf_in_dir(dir_path, gguf_variant) + if gguf is None or not _leaf_inside_allowlist(gguf, allow_roots): + return None + return read_gguf_chat_template(str(gguf)) + + # Sidecar tokenizer files (chat_template.jinja / tokenizer_config.json) are the + # author's maintained template and supersede the GGUF's possibly-stale embedded + # copy. The variant only picks the GGUF fallback, so tokenizer-first precedence + # holds whether or not a variant is given. + return _chat_template_from_tokenizer_dir(dir_path, allow_roots) or from_gguf() + + +def read_default_chat_template( + model_name: str, + hf_token: Optional[str] = None, + gguf_variant: Optional[str] = None, +) -> Optional[str]: + if not isinstance(model_name, str) or not model_name.strip(): + return None + name = model_name.strip() + + if is_local_path(name): + try: + target = Path(normalize_path(name)).expanduser() + allow_roots = _build_browse_allowlist() + if not _is_path_inside_allowlist(target, allow_roots): + logger.debug("Refused chat template read outside allowed folders: %s", name) + return None + if name.lower().endswith(".gguf"): + # Prefer a maintained sidecar next to the file over the GGUF's + # embedded copy (tokenizer-first precedence, as elsewhere). + sidecar = _chat_template_from_tokenizer_dir(target.parent, allow_roots) + if sidecar: + return sidecar + return read_gguf_chat_template(str(target)) + return _chat_template_from_dir(target, gguf_variant, allow_roots) + except Exception as exc: + logger.debug("Could not read local chat template for %s: %s", name, exc) + return None + + if not _is_valid_repo_id(name): + return None + + resolved = resolve_cached_repo_id_case(name) + + try: + # Resolve within each cached revision, newest first. A revision's sidecar + # supersedes its own embedded GGUF copy, but must not override a newer + # revision, so precedence stays per-snapshot rather than global. + for snapshot in iter_hf_cache_snapshots(resolved): + template = _chat_template_from_dir(snapshot, gguf_variant) + if template: + return template + except Exception as exc: + logger.debug("Could not read cached chat template for %s: %s", resolved, exc) + + try: + from huggingface_hub import HfApi, hf_hub_download + + _api = HfApi() + + def _remote_exceeds_cap(rel: str) -> bool: + # Best-effort: skip the download when the remote's advertised size + # exceeds the cap, so a maliciously large sidecar is never fetched. + try: + infos = _api.get_paths_info(resolved, [rel], repo_type = "model", token = hf_token) + except Exception: + return False + for info in infos: + size = getattr(info, "size", None) + if ( + getattr(info, "path", None) == rel + and isinstance(size, int) + and size > MAX_TEMPLATE_METADATA_BYTES + ): + return True + return False + + def _download_text(rel: str) -> Optional[str]: + if _remote_exceeds_cap(rel): + return None + try: + path = hf_hub_download(resolved, rel, token = hf_token) + return _read_bounded_text(Path(path), MAX_TEMPLATE_METADATA_BYTES) + except Exception: + return None + + for rel in _JINJA_TEMPLATE_PATHS: + template = _download_text(rel) + if not template or not template.strip(): + continue + # A raw Jinja sidecar is the whole template, so it must fit the route's + # response cap (the local path skips oversized .jinja too). Download stays + # bounded at MAX_TEMPLATE_METADATA_BYTES so a large JSON embedding a small + # template still extracts below, but an over-cap Jinja is dropped so the + # search falls through to the tokenizer/processor template. + if len(template.encode("utf-8")) > MAX_CHAT_TEMPLATE_BYTES: + continue + return template + + for rel in _TOKENIZER_CONFIG_PATHS: + raw = _download_text(rel) + if not raw: + continue + try: + config = json.loads(raw) + except Exception: + continue + template = _chat_template_from_tokenizer_config(config) + if template: + return template + + for rel in _PROCESSOR_TEMPLATE_PATHS: + raw = _download_text(rel) + if not raw: + continue + try: + payload = json.loads(raw) + except Exception: + continue + template = _chat_template_from_processor_payload(payload) + if template: + return template + + return None + except Exception as exc: + logger.debug("Could not fetch chat template for %s: %s", resolved, exc) + return None diff --git a/studio/backend/routes/inference.py b/studio/backend/routes/inference.py index afd942e9a5..d3e588bb0b 100644 --- a/studio/backend/routes/inference.py +++ b/studio/backend/routes/inference.py @@ -5144,10 +5144,10 @@ async def validate_model( latest_tier_active_for, config.identifier, request.hf_token ): effective_load_in_4bit = False - # A metadata-only probe just reads the GGUF header and allocates no VRAM, - # so it must not be refused by the training guard. Real loads validate - # without include_context_length and /load applies the guard again. - if not request.include_context_length: + # A metadata-only probe reads the GGUF header and allocates no VRAM, so the + # training guard must not refuse it. Real loads omit include_context_length / + # include_chat_template, and /load applies the guard again. + if not (request.include_context_length or request.include_chat_template): # Match /load's inherited llama.cpp extras and parallel slot count so # validation cannot pass a smaller estimate than the subsequent load. effective_extra_args = _resolve_inherited_extra_args( @@ -5189,9 +5189,15 @@ async def validate_model( context_length: Optional[int] = None layer_count: Optional[int] = None moe_layer_count: Optional[int] = None - if request.include_context_length and is_gguf: + chat_template: Optional[str] = None + # Both header probes read the same local GGUF, so resolve it once. + if (request.include_context_length or request.include_chat_template) and is_gguf: from hub.utils.gguf import resolve_local_gguf_path - from utils.models.gguf_metadata import read_gguf_staged_dims + from picker.schemas import MAX_CHAT_TEMPLATE_BYTES + from utils.models.gguf_metadata import ( + read_gguf_chat_template, + read_gguf_staged_dims, + ) # Best-effort: a header-read failure must never fail validation of an # otherwise-valid model (the outer except turns it into a 400). @@ -5207,13 +5213,24 @@ async def validate_model( model_identifier, request.gguf_variant ) if local_gguf: - # Header walk reads tokenizer arrays for dense models (tens of - # ms); keep it off the event loop. - dims = await asyncio.to_thread(read_gguf_staged_dims, local_gguf) - if dims: - context_length = dims["context_length"] - layer_count = dims["layer_count"] - moe_layer_count = dims["moe_layer_count"] + if request.include_context_length: + # Header walk reads tokenizer arrays (tens of ms); keep it + # off the event loop. + dims = await asyncio.to_thread(read_gguf_staged_dims, local_gguf) + if dims: + context_length = dims["context_length"] + layer_count = dims["layer_count"] + moe_layer_count = dims["moe_layer_count"] + if request.include_chat_template: + # Read only the leased GGUF's own embedded template (the copy + # llama.cpp loads), never a sibling sidecar: the native grant + # authorizes just this path, so neighbours would be scope escalation. + raw_template = await asyncio.to_thread(read_gguf_chat_template, local_gguf) + if ( + raw_template is not None + and len(raw_template.encode("utf-8")) <= MAX_CHAT_TEMPLATE_BYTES + ): + chat_template = raw_template except Exception as e: logger.debug("Header probe failed for %s: %s", model_log_label, e) @@ -5232,6 +5249,7 @@ async def validate_model( context_length = context_length, layer_count = layer_count, moe_layer_count = moe_layer_count, + chat_template = chat_template, requires_transformers_upgrade = transformers_upgrade is not None, transformers_upgrade = transformers_upgrade, ) diff --git a/studio/backend/routes/models.py b/studio/backend/routes/models.py index 0806c2f513..a5ce1a72f0 100644 --- a/studio/backend/routes/models.py +++ b/studio/backend/routes/models.py @@ -60,13 +60,52 @@ def _safe_is_dir(path) -> bool: # Shared with the hub inventory scans; keep the private aliases so existing -# importers (core.inference.local_model_resolver, tests) stay valid. +# importers stay valid. ``_HF_REPO_ID_RE`` is the Hub repo id shape ("owner/name"); +# anything else is treated as a local filesystem path. from utils.hidden_models import ( + _HF_REPO_ID_RE, + _existing_resolved_path, _safe_resolve, is_hidden_model as _is_hidden_model, ) +def hidden_model_matchers() -> tuple[list[str], list[str], list[str]]: + """Substring needles, exact repo ids, and exact resolved paths identifying + infra models (the RAG embedder and the llama.cpp install validation probe) + that pickers hide. Served by the ``/api/hub/hidden-models`` endpoint. A + configured HF-repo embedder is published as its exact lowercased repo id + (mirroring ``utils.hidden_models.is_hidden_model``) and a local-path + embedder as its exact resolved path only: a generic basename like "model" + must not substring-hide unrelated chat models.""" + from core.rag import config as rag_config + + needles = [ + # The validation probe's repo and its exact filename. The filename carries + # .gguf so it won't hide unrelated repos like ``user/stories260K-finetune-GGUF``. + "ggml-org/models", + "stories260k.gguf", + ] + exact_ids: list[str] = [] + exact_paths: list[str] = [] + for model in ( + rag_config.effective_embedding_model(), + rag_config.effective_gguf_repo(), + ): + # Resolve an existing local path before the repo-id regex: a local embedder + # shaped like "models/embedder" is an exact path, not a Hub repo id. + existing_path = _existing_resolved_path(model) + if existing_path: + exact_paths.append(existing_path.lower()) + elif _HF_REPO_ID_RE.match(model): + exact_ids.append(model.lower()) + else: + resolved = _safe_resolve(Path(model).expanduser()) + if resolved: + exact_paths.append(resolved.lower()) + return needles, exact_ids, exact_paths + + backend_path = Path(__file__).parent.parent.parent if str(backend_path) not in sys.path: sys.path.insert(0, str(backend_path)) @@ -91,6 +130,7 @@ try: _pick_best_gguf, _extract_quant_label, _is_big_endian_gguf_path, + _is_mtp_drafter, is_audio_input_type, ) from core.inference import get_inference_backend @@ -123,6 +163,7 @@ except ImportError: _pick_best_gguf, _extract_quant_label, _is_big_endian_gguf_path, + _is_mtp_drafter, is_audio_input_type, ) from core.inference import get_inference_backend @@ -803,7 +844,7 @@ def collect_local_models(models_root: Path) -> List[LocalModelInfo]: models = sorted( deduped.values(), - key = lambda item: (item.updated_at or 0), + key = lambda item: item.updated_at or 0, reverse = True, ) return [m for m in models if not _is_hidden_model(m.id, m.model_id, m.path)] @@ -1750,9 +1791,11 @@ def _get_model_size_bytes(model_name: str, hf_token: Optional[str] = None) -> Op async def get_model_config( model_name: str, hf_token: Optional[str] = Query(None), + header_hf_token: Optional[str] = Depends(get_hf_token), current_subject: str = Depends(get_current_subject), ): """Get configuration for a specific model (wraps load_model_defaults).""" + hf_token = _normalize_hf_token(header_hf_token) or _normalize_hf_token(hf_token) try: if not is_local_path(model_name): resolved = resolve_cached_repo_id_case(model_name) @@ -2471,6 +2514,7 @@ async def get_lora_base_model(lora_path: str, current_subject: str = Depends(get async def check_vision_model( model_name: str, hf_token: Optional[str] = Query(None), + header_hf_token: Optional[str] = Depends(get_hf_token), current_subject: str = Depends(get_current_subject), ): """ @@ -2478,6 +2522,7 @@ async def check_vision_model( This endpoint wraps the backend is_vision_model function. """ + hf_token = _normalize_hf_token(header_hf_token) or _normalize_hf_token(hf_token) try: logger.info(f"Checking if vision model: {model_name}") # Authenticate so a gated/private VLM classifies correctly (else 404 -> non-vision). @@ -2503,6 +2548,7 @@ async def check_vision_model( async def check_embedding_model( model_name: str, hf_token: Optional[str] = Query(None), + header_hf_token: Optional[str] = Depends(get_hf_token), current_subject: str = Depends(get_current_subject), ): """ @@ -2510,6 +2556,7 @@ async def check_embedding_model( This endpoint wraps the backend is_embedding_model function. """ + hf_token = _normalize_hf_token(header_hf_token) or _normalize_hf_token(hf_token) try: logger.info(f"Checking if embedding model: {model_name}") is_embedding = is_embedding_model(model_name, hf_token = hf_token) @@ -2573,12 +2620,6 @@ def _resolve_quant_gguf(repo_id: str, quant: str, is_local: bool) -> tuple[Optio Q8_0 weights). Never raises. """ try: - from utils.models.model_config import ( - _extract_quant_label, - _is_big_endian_gguf_path, - _is_mtp_drafter, - ) - if is_local: roots = [Path(repo_id)] else: @@ -2595,25 +2636,19 @@ def _resolve_quant_gguf(repo_id: str, quant: str, is_local: bool) -> tuple[Optio if snaps.is_dir(): roots.extend(s for s in snaps.iterdir() if s.is_dir()) - want = quant.lower().replace("-", "").replace("_", "") + want = _normalized_quant_label(quant) best_total = 0 best_first: Optional[str] = None for root in roots: matches: list[tuple[str, Path]] = [] total = 0 for f in _iter_gguf_paths(root): - if _is_mmproj_filename(f.name): - continue try: rel = f.relative_to(root).as_posix() except ValueError: rel = f.name - if _is_mtp_drafter(rel): - continue - q = _extract_quant_label(rel) - if _is_big_endian_gguf_path(rel, q): - continue - if q.lower().replace("-", "").replace("_", "") != want: + q = _main_variant_gguf_label(rel) + if q is None or _normalized_quant_label(q) != want: continue try: total += f.stat().st_size @@ -3035,6 +3070,22 @@ def _is_main_gguf_filename(name: str) -> bool: return _is_gguf_filename(name) and not _is_mmproj_filename(name) +def _main_variant_gguf_label(rel_path: str) -> Optional[str]: + name = rel_path.rsplit("/", 1)[-1] + if not _is_main_gguf_filename(name): + return None + if _is_mtp_drafter(rel_path): + return None + label = _extract_quant_label(rel_path) + if _is_big_endian_gguf_path(rel_path, label): + return None + return label + + +def _normalized_quant_label(label: str) -> str: + return label.lower().replace("-", "").replace("_", "") + + def _repo_has_mmproj(repo_info) -> bool: """True if the repo ships a GGUF vision adapter (mmproj), so it can take image inputs. Cheap: scans already-listed file names only.""" @@ -3362,6 +3413,170 @@ async def delete_cached_model( ) +def _resolve_cached_model_path(repo_id: str, variant: Optional[str]) -> Path: + """Absolute path of a cached repo (newest snapshot dir) or, with *variant*, + that quant's main GGUF file (first split of a sharded quant). Paths come + from the HF cache scan only, so callers can't probe arbitrary paths.""" + cache_scans = _all_hf_cache_scans() + + matching_repos = [] + for hf_cache in cache_scans: + for repo_info in hf_cache.repos: + if repo_info.repo_type != "model": + continue + if repo_info.repo_id.lower() == repo_id.lower(): + matching_repos.append(repo_info) + if not matching_repos: + raise HTTPException(status_code = 404, detail = "Model not found in cache") + + if variant: + want = _normalized_quant_label(variant) + candidate_revisions = sorted( + (rev for repo_info in matching_repos for rev in repo_info.revisions), + key = lambda rev: getattr(rev, "last_modified", 0) or 0, + reverse = True, + ) + for rev in candidate_revisions: + snapshot = getattr(rev, "snapshot_path", None) + matches = [] + for f in rev.files: + p = Path(f.file_path) + rel = f.file_name + if snapshot: + try: + rel = p.relative_to(snapshot).as_posix() + except ValueError: + pass + label = _main_variant_gguf_label(rel) + if label is None or _normalized_quant_label(label) != want: + continue + if p.exists() or p.is_symlink(): + matches.append((rel, p)) + if matches: + # Path-sorted so a sharded quant deterministically yields its first split. + return sorted(matches, key = lambda m: m[0].lower())[0][1] + raise HTTPException( + status_code = 404, + detail = f"Variant {variant} not found in cache for {repo_id}", + ) + + def repo_size(repo_info) -> int: + gguf_size = _repo_gguf_size_bytes(repo_info) + if gguf_size > 0: + return gguf_size + return sum( + (getattr(f, "size_on_disk", None) or 0) + for rev in repo_info.revisions + for f in rev.files + ) + + def repo_last_modified(repo_info) -> float: + return max( + (getattr(rev, "last_modified", 0) or 0 for rev in repo_info.revisions), + default = 0, + ) + + target_repo = max( + matching_repos, + key = lambda repo_info: (repo_size(repo_info), repo_last_modified(repo_info)), + ) + + # Whole repo: the newest revision's snapshot dir holds the visible files. + revisions = sorted( + (rev for rev in target_repo.revisions if getattr(rev, "snapshot_path", None)), + key = lambda rev: getattr(rev, "last_modified", 0) or 0, + reverse = True, + ) + for rev in revisions: + p = Path(rev.snapshot_path) + if p.exists(): + return p + p = Path(target_repo.repo_path) + if p.exists(): + return p + raise HTTPException(status_code = 404, detail = "Cached model path not found") + + +def _wsl_reveal_in_explorer(path: Path) -> bool: + import subprocess + + from utils.paths.path_utils import _IS_WSL + + if not _IS_WSL: + return False + try: + windows_path = subprocess.run( + ["wslpath", "-w", str(path)], + capture_output = True, + text = True, + check = True, + timeout = 10, + ).stdout.strip() + if not windows_path: + return False + argument = f"/select,{windows_path}" if path.is_file() else windows_path + subprocess.Popen(["explorer.exe", argument]) + return True + except (OSError, subprocess.SubprocessError): + return False + + +def _reveal_in_file_manager(path: Path) -> None: + """Open the OS file manager with *path* selected (best effort per platform).""" + import subprocess + + target = str(path) + if sys.platform == "darwin": + cmd = ["open", "-R", target] if path.is_file() else ["open", target] + subprocess.Popen(cmd) + elif os.name == "nt": + if path.is_file(): + subprocess.Popen(["explorer", f"/select,{target}"]) + else: + os.startfile(target) # noqa: S606 - local user's own file manager + elif not _wsl_reveal_in_explorer(path): + # No cross-desktop "select file" standard on Linux; open the directory. + directory = target if path.is_dir() else str(path.parent) + subprocess.Popen(["xdg-open", directory]) + + +class CachedModelPathResponse(BaseModel): + path: str + is_dir: bool + + +@router.get("/cached-model-path", response_model = CachedModelPathResponse) +async def get_cached_model_path( + repo_id: str = Query(..., description = "HuggingFace repo ID"), + variant: str = Query("", description = "Quantization variant (empty for whole repo)"), + current_subject: str = Depends(get_current_subject), +): + """Absolute on-disk path of a cached repo or one of its GGUF variants.""" + if not _is_valid_repo_id(repo_id): + raise HTTPException(status_code = 400, detail = "Invalid repo_id format") + path = await asyncio.to_thread(_resolve_cached_model_path, repo_id, variant.strip() or None) + return {"path": str(path), "is_dir": path.is_dir()} + + +@router.post("/reveal-cached-model") +async def reveal_cached_model( + repo_id: str = Body(...), + variant: Optional[str] = Body(None), + current_subject: str = Depends(get_current_subject), +): + """Reveal a cached repo (or one GGUF variant's file) in the OS file manager.""" + if not _is_valid_repo_id(repo_id): + raise HTTPException(status_code = 400, detail = "Invalid repo_id format") + variant = (variant or "").strip() or None + path = await asyncio.to_thread(_resolve_cached_model_path, repo_id, variant) + try: + await asyncio.to_thread(_reveal_in_file_manager, path) + except Exception as e: + logger.error(f"Failed to reveal {path}: {e}") + raise HTTPException(status_code = 500, detail = "Failed to open file manager") + return {"status": "ok", "path": str(path)} + + @router.get("/checkpoints", response_model = CheckpointListResponse) async def list_checkpoints( outputs_dir: str = Query( diff --git a/studio/backend/tests/test_chat_load_during_training.py b/studio/backend/tests/test_chat_load_during_training.py index 7daa4224aa..a5fd71b6a0 100644 --- a/studio/backend/tests/test_chat_load_during_training.py +++ b/studio/backend/tests/test_chat_load_during_training.py @@ -801,6 +801,80 @@ class TestValidateRefusesDuringTraining(unittest.TestCase): asyncio.run(self.route.validate_model(request, current_subject = "u")) self.assertEqual(guard_called, []) + def _validate_gguf_template( + self, + *, + template, + canonical_path = "/picked/model.gguf", + ): + # Drive validate_model for a native lease-backed GGUF template probe and + # capture what the embedded-template reader was called with. + from models.inference import ValidateModelRequest + + request = ValidateModelRequest( + model_path = "model.gguf", + gguf_variant = "Q4_K_M", + native_path_lease = "signed-lease", + include_chat_template = True, + ) + cfg = SimpleNamespace( + identifier = canonical_path, + display_name = "model.gguf", + is_gguf = True, + is_lora = False, + is_vision = False, + gguf_file = canonical_path, + path = None, + base_model = None, + ) + import utils.models.gguf_metadata as gguf_meta + + seen = {} + + def _fake_read(path): + seen["path"] = path + return template + + guard_called = [] + with ( + patch.object( + self.route, + "_resolve_model_identifier_for_request", + return_value = (canonical_path, "model.gguf", True), + ), + patch.object(self.route.ModelConfig, "from_identifier", return_value = cfg), + patch.object(self.route, "load_inference_config", return_value = {}), + patch.object(gguf_meta, "read_gguf_chat_template", _fake_read), + patch.object( + self.route, + "_guard_chat_load_against_training", + lambda *a, **kw: guard_called.append(True), + ), + ): + resp = asyncio.run(self.route.validate_model(request, current_subject = "u")) + return resp, seen, guard_called + + def test_include_chat_template_reads_leased_gguf_embedded_template(self): + # The picker chat-template GET has no lease plumbing, so a native picked + # GGUF surfaces its default template through this lease-aware probe: the + # embedded template is read from the granted canonical path and returned. + resp, seen, _ = self._validate_gguf_template(template = "{{ messages }}") + self.assertEqual(resp.chat_template, "{{ messages }}") + # Read strictly the leased file's own embedded template, never a sibling + # sidecar: the grant authorizes just this one path. + self.assertEqual(seen["path"], "/picked/model.gguf") + + def test_include_chat_template_skips_training_guard(self): + # A template-only probe allocates no VRAM, so like include_context_length + # it must not be refused by the training guard. + _, _, guard_called = self._validate_gguf_template(template = "{{ messages }}") + self.assertEqual(guard_called, []) + + def test_include_chat_template_over_cap_is_dropped(self): + from picker.schemas import MAX_CHAT_TEMPLATE_BYTES + resp, _, _ = self._validate_gguf_template(template = "a" * (MAX_CHAT_TEMPLATE_BYTES + 1)) + self.assertIsNone(resp.chat_template) + # ── _estimate_gguf_required_gb (sizes the same weights the loader loads) ────── diff --git a/studio/backend/tests/test_export_absolute_paths.py b/studio/backend/tests/test_export_absolute_paths.py index 761ea08e3f..5097f9f53a 100644 --- a/studio/backend/tests/test_export_absolute_paths.py +++ b/studio/backend/tests/test_export_absolute_paths.py @@ -158,6 +158,7 @@ def _install_lightweight_backend_stubs(monkeypatch): utils_model_config._pick_best_gguf = lambda variants: variants[0] if variants else None utils_model_config._extract_quant_label = lambda value: value utils_model_config._is_big_endian_gguf_path = lambda *args, **kwargs: False + utils_model_config._is_mtp_drafter = lambda *args, **kwargs: False utils_model_config.is_audio_input_type = lambda *args, **kwargs: None monkeypatch.setitem( sys.modules, diff --git a/studio/backend/tests/test_model_picker_regression.py b/studio/backend/tests/test_model_picker_regression.py new file mode 100644 index 0000000000..f38a4d0b8d --- /dev/null +++ b/studio/backend/tests/test_model_picker_regression.py @@ -0,0 +1,232 @@ +# SPDX-License-Identifier: AGPL-3.0-only +# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0 + +"""Regression guards for the model-picker per-model-config feature (the set of +bugs that got the predecessor PR reverted). Pure-function / validation checks +only, so they run on CPU in the backend pytest job with no model download. + +Covers, at the backend layer: + - infra-model hiding: the RAG embedder (bge-small-en-v1.5) and the llama.cpp + install-validation probe (ggml-org/models / stories260K) stay hidden, while + normal chat repos are not hidden; + - the HF token is honored from the dedicated header with the query string as a + fallback, never the other way around; + - the chat-template byte caps reject oversized overrides (both the char-count + fast path and the UTF-8 byte path) and the sidecar reader is size-bounded. +""" + +from __future__ import annotations + +import sys +import types + +import pytest + +# Keep this test runnable without the optional structlog dependency (mirrors +# tests/test_cached_gguf_routes.py), since importing routes.models pulls it in. +if "structlog" not in sys.modules: + + class _DummyLogger: + def __getattr__(self, _name): + return lambda *args, **kwargs: None + + sys.modules["structlog"] = types.SimpleNamespace( + BoundLogger = _DummyLogger, + get_logger = lambda *args, **kwargs: _DummyLogger(), + ) + +import routes.models as models_route +from core.rag import config as rag_config +from hub.dependencies import get_hf_token +from models.inference import LoadRequest +from picker.schemas import MAX_CHAT_TEMPLATE_BYTES +from picker.service import _read_bounded_text +from utils.hidden_models import is_hidden_model + + +@pytest.fixture(autouse = True) +def _pin_default_embedder(monkeypatch): + """Pin the effective embedder to Studio's static default so hiding is + deterministic and cannot depend on ambient RAG config / env.""" + default = "unsloth/bge-small-en-v1.5" + monkeypatch.setattr(rag_config, "EMBEDDING_MODEL", default, raising = False) + monkeypatch.setattr(rag_config, "effective_embedding_model", lambda: default) + monkeypatch.setattr(rag_config, "effective_gguf_repo", lambda: default) + monkeypatch.setattr(rag_config, "default_gguf_repo", lambda: default) + + +# --------------------------------------------------------------------------- # +# Infra-model hiding (the "infra models resurfaced in the picker" regression) # +# --------------------------------------------------------------------------- # + + +@pytest.mark.parametrize( + "value", + [ + "ggml-org/models", # the probe repo id + "unsloth/bge-small-en-v1.5", # the RAG embedder repo + "unsloth/bge-small-en-v1.5-GGUF", # its GGUF companion + "/root/.cache/huggingface/hub/x/stories260K.gguf", # probe on disk + "/root/.cache/x/Stories260K.GGUF", # case-insensitive + r"C:\\models\\stories260K.gguf", # windows-style path + "/opt/models/bge-small-en-v1.5", # embedder basename folder + "/opt/models/bge-small-en-v1.5-Q8_0.gguf", # suffixed local weight + ], +) +def test_infra_models_are_hidden(value): + assert is_hidden_model(value) is True + + +@pytest.mark.parametrize( + "value", + [ + "unsloth/gemma-3-270m-it-GGUF", # a normal small chat GGUF + "unsloth/Qwen3-0.6B", # a normal non-GGUF chat model + "user/stories260K-finetune-GGUF", # repo id merely contains "stories260k" + "user/model-chat", # generic repo must not be hidden + "meta-llama/Llama-3.1-8B-Instruct", + ], +) +def test_normal_models_are_not_hidden(value): + assert is_hidden_model(value) is False + + +def test_is_hidden_model_ignores_empty_values(): + assert is_hidden_model(None) is False + assert is_hidden_model("") is False + assert is_hidden_model(None, "", "unsloth/gemma-3-270m-it-GGUF") is False + + +def test_hidden_model_matchers_expose_probe_needles(): + needles, exact_ids, _exact_paths = models_route.hidden_model_matchers() + lowered = [n.lower() for n in needles] + assert "ggml-org/models" in lowered + assert "stories260k.gguf" in lowered + # The configured embedder is exposed as an exact repo id, never as a + # basename needle that would substring-hide unrelated chat models. + assert "bge-small-en-v1.5" not in lowered + assert "unsloth/bge-small-en-v1.5" in exact_ids + + +def test_hidden_model_matchers_custom_repo_publishes_exact_ids(monkeypatch): + monkeypatch.setattr(rag_config, "effective_embedding_model", lambda: "org/model") + monkeypatch.setattr(rag_config, "effective_gguf_repo", lambda: "org/model-GGUF") + needles, exact_ids, exact_paths = models_route.hidden_model_matchers() + assert needles == ["ggml-org/models", "stories260k.gguf"] + assert "org/model" in exact_ids + assert "org/model-gguf" in exact_ids + assert exact_paths == [] + + +def test_hidden_model_matchers_local_owner_name_path_is_exact_path(monkeypatch, tmp_path): + # A local embedder shaped like owner/name that exists on disk must be an + # exact resolved path, not a Hub repo id (mirroring is_hidden_model), so the + # local row stays hidden instead of showing as a chat model. + (tmp_path / "models" / "embedder").mkdir(parents = True) + monkeypatch.chdir(tmp_path) + monkeypatch.setattr(rag_config, "effective_embedding_model", lambda: "models/embedder") + monkeypatch.setattr(rag_config, "effective_gguf_repo", lambda: "ggml-org/models") + _needles, exact_ids, exact_paths = models_route.hidden_model_matchers() + resolved = str((tmp_path / "models" / "embedder").resolve()).lower() + assert resolved in exact_paths + assert "models/embedder" not in exact_ids + + +# --------------------------------------------------------------------------- # +# HF token via header, query string only as a fallback (the token-leak fix) # +# --------------------------------------------------------------------------- # + + +def test_get_hf_token_strips_and_returns(): + assert get_hf_token(" hf_abc ") == "hf_abc" + + +@pytest.mark.parametrize("value", [None, "", " ", "\n\t"]) +def test_get_hf_token_blank_is_none(value): + assert get_hf_token(value) is None + + +@pytest.mark.parametrize( + "value,expected", + [(" hf_x ", "hf_x"), ("", None), (" ", None), (None, None), (1234, None)], +) +def test_normalize_hf_token(value, expected): + assert models_route._normalize_hf_token(value) == expected + + +def test_header_token_wins_over_query(): + header, query = "hf_header", "hf_query" + resolved = models_route._normalize_hf_token(header) or models_route._normalize_hf_token(query) + assert resolved == "hf_header" + + +def test_query_token_is_fallback_when_header_absent(): + resolved = models_route._normalize_hf_token(None) or models_route._normalize_hf_token( + "hf_query" + ) + assert resolved == "hf_query" + + +# --------------------------------------------------------------------------- # +# Chat-template byte caps (the unbounded-template hardening) # +# --------------------------------------------------------------------------- # + + +def _load_request(**overrides): + data = {"model_path": "unsloth/test-model-GGUF", "gguf_variant": "Q4_K_M"} + data.update(overrides) + return LoadRequest.model_validate(data) + + +def test_blank_chat_template_override_normalizes_to_none(): + assert _load_request(chat_template_override = " \n\t").chat_template_override is None + + +def test_nonblank_chat_template_override_preserved_verbatim(): + template = " {{ messages }} " + assert _load_request(chat_template_override = template).chat_template_override == template + + +def test_chat_template_at_byte_limit_is_accepted(): + template = "a" * MAX_CHAT_TEMPLATE_BYTES # exactly the limit, 1 byte/char + assert ( + len(_load_request(chat_template_override = template).chat_template_override) + == MAX_CHAT_TEMPLATE_BYTES + ) + + +def test_chat_template_over_char_limit_is_rejected(): + with pytest.raises(Exception): # pydantic ValidationError wrapping ValueError + _load_request(chat_template_override = "a" * (MAX_CHAT_TEMPLATE_BYTES + 1)) + + +def test_chat_template_over_byte_limit_is_rejected(): + # Char count stays under the limit but UTF-8 bytes exceed it (3 bytes/char), + # so only the byte-count branch can catch this. + multibyte = "€" * (MAX_CHAT_TEMPLATE_BYTES // 2) # euro sign, 3 bytes each + assert len(multibyte) <= MAX_CHAT_TEMPLATE_BYTES + assert len(multibyte.encode("utf-8")) > MAX_CHAT_TEMPLATE_BYTES + with pytest.raises(Exception): + _load_request(chat_template_override = multibyte) + + +def test_read_bounded_text_reads_within_limit(tmp_path): + p = tmp_path / "t.json" + p.write_text("hello", encoding = "utf-8") + assert _read_bounded_text(p, 16) == "hello" + + +def test_read_bounded_text_rejects_over_limit(tmp_path): + p = tmp_path / "big.json" + p.write_bytes(b"x" * 100) + assert _read_bounded_text(p, 50) is None + + +def test_read_bounded_text_at_limit_is_read(tmp_path): + p = tmp_path / "exact.json" + p.write_bytes(b"x" * 50) + assert _read_bounded_text(p, 50) == "x" * 50 + + +def test_read_bounded_text_missing_file_is_none(tmp_path): + assert _read_bounded_text(tmp_path / "nope.json", 50) is None diff --git a/studio/backend/tests/test_model_update_robustness.py b/studio/backend/tests/test_model_update_robustness.py index edf55812e2..7d98766616 100644 --- a/studio/backend/tests/test_model_update_robustness.py +++ b/studio/backend/tests/test_model_update_robustness.py @@ -314,6 +314,7 @@ def test_cached_model_scan_keeps_local_safetensors_repo(monkeypatch, tmp_path): file_name = "model.safetensors", size_on_disk = 100, blob_path = str(repo_path / "blobs" / "modelsha"), + blob_last_modified = 3_000.0, ), ] ) @@ -336,6 +337,51 @@ def test_cached_model_scan_keeps_local_safetensors_repo(monkeypatch, tmp_path): assert rows[0]["repo_id"] == "Org/SafeTensorRepo" assert rows[0]["model_format"] == "safetensors" assert rows[0]["size_bytes"] == 100 + assert rows[0]["last_modified"] == 3_000.0 + + +def test_cached_gguf_scan_keeps_download_timestamp(monkeypatch, tmp_path): + repo_path = tmp_path / "models--Org--GgufRepo" + repo = SimpleNamespace( + repo_id = "Org/GgufRepo", + repo_type = "model", + repo_path = repo_path, + revisions = [ + SimpleNamespace( + files = [ + SimpleNamespace( + file_name = "model-Q4_K_M.gguf", + size_on_disk = 100, + blob_path = None, + blob_last_modified = 5_000.0, + ), + ] + ) + ], + ) + monkeypatch.setattr( + CI, + "all_hf_cache_scans", + lambda: [SimpleNamespace(repos = [repo])], + ) + monkeypatch.setattr( + CI.hf_cache_scan, + "is_gguf_repo_partial", + lambda *args, **kwargs: False, + ) + monkeypatch.setattr( + CI, + "_gguf_variant_state_summary", + lambda _repo_id: (False, 0), + ) + + rows = CI._scan_cached_gguf() + + assert len(rows) == 1 + assert rows[0]["repo_id"] == "Org/GgufRepo" + assert rows[0]["model_format"] == "gguf" + assert rows[0]["size_bytes"] == 100 + assert rows[0]["last_modified"] == 5_000.0 # ── hf_hub_download_with_xet_fallback force_download bypass (X2/F2) ─── @@ -636,3 +682,18 @@ def test_reclaim_replaced_gguf_variant_keeps_no_symlink_current_file(monkeypatch assert snap.exists() is True # the current file must survive assert result["removed_snapshots"] == 0 assert result["deleted_blobs"] == 0 + + +def _mmproj_repo(*file_names: str): + return SimpleNamespace( + revisions = [SimpleNamespace(files = [SimpleNamespace(file_name = n) for n in file_names])] + ) + + +def test_repo_has_mmproj_requires_gguf_projector(): + # A non-GGUF sidecar whose name merely contains "mmproj" must NOT mark the + # repo vision-capable; the runtime's projector detection is GGUF-only. + assert CI._repo_has_mmproj(_mmproj_repo("model-Q4_K_M.gguf", "mmproj_config.json")) is False + assert CI._repo_has_mmproj(_mmproj_repo("model-Q4_K_M.gguf", "README-mmproj.md")) is False + # A real GGUF projector still marks the repo vision-capable. + assert CI._repo_has_mmproj(_mmproj_repo("model-Q4_K_M.gguf", "mmproj-F16.gguf")) is True diff --git a/studio/backend/tests/test_picker_service.py b/studio/backend/tests/test_picker_service.py new file mode 100644 index 0000000000..be7ea18f03 --- /dev/null +++ b/studio/backend/tests/test_picker_service.py @@ -0,0 +1,266 @@ +# SPDX-License-Identifier: AGPL-3.0-only +# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0 + +import json +from types import SimpleNamespace + +from picker.service import ( + MAX_TEMPLATE_METADATA_BYTES, + _chat_template_from_dir, + _chat_template_from_processor_json, + _chat_template_from_tokenizer_config, + _chat_template_from_tokenizer_dir, + _find_gguf_in_dir, + _iter_ggufs, + read_default_chat_template, + validate_chat_template, +) + + +def test_iter_ggufs_skips_gguf_companions(tmp_path): + mtp_dir = tmp_path / "MTP" + mtp_dir.mkdir() + main = tmp_path / "model-Q8_0.gguf" + main.write_bytes(b"") + (tmp_path / "mmproj-F16.gguf").write_bytes(b"") + (tmp_path / "mtp-model-Q8_0.gguf").write_bytes(b"") + (mtp_dir / "model-Q8_0-MTP.gguf").write_bytes(b"") + (tmp_path / "model-Q8_0-be.gguf").write_bytes(b"") + + assert _iter_ggufs(tmp_path) == [main] + + +def test_find_gguf_in_dir_matches_quant_label(tmp_path): + mtp_dir = tmp_path / "MTP" + mtp_dir.mkdir() + main = tmp_path / "model-Q8_0.gguf" + main.write_bytes(b"") + (mtp_dir / "model-Q8_0-MTP.gguf").write_bytes(b"") + (tmp_path / "model-Q4_K_M.gguf").write_bytes(b"") + + assert _find_gguf_in_dir(tmp_path, "Q8_0") == main + assert _find_gguf_in_dir(tmp_path, "Q4_K") is None + + +def test_find_gguf_in_dir_without_variant_prefers_largest_model(tmp_path): + smaller = tmp_path / "a-model-Q4_K_M.gguf" + larger = tmp_path / "z-model-Q8_0.gguf" + smaller.write_bytes(b"0") + larger.write_bytes(b"00") + + assert _find_gguf_in_dir(tmp_path, None) == larger + + +def test_find_gguf_in_dir_without_variant_prefers_first_split(tmp_path): + first = tmp_path / "model-Q4_K_M-00001-of-00003.gguf" + second = tmp_path / "model-Q4_K_M-00002-of-00003.gguf" + third = tmp_path / "model-Q4_K_M-00003-of-00003.gguf" + first.write_bytes(b"0") + second.write_bytes(b"000") + third.write_bytes(b"00") + + assert _find_gguf_in_dir(tmp_path, None) == first + + first.unlink() + assert _find_gguf_in_dir(tmp_path, None) == second + + +def test_find_gguf_in_dir_matches_bpw_variant_base_label(tmp_path): + target = tmp_path / "model-IQ4_XS-3.53bpw.gguf" + target.write_bytes(b"") + (tmp_path / "model-Q4_K_M.gguf").write_bytes(b"") + + assert _find_gguf_in_dir(tmp_path, "IQ4_XS") == target + assert _find_gguf_in_dir(tmp_path, "IQ4_XS-3.53bpw") == target + assert _find_gguf_in_dir(tmp_path, "Q4_K") is None + + +def test_validate_chat_template_accepts_valid_and_empty(): + assert validate_chat_template("{{ messages[0].content }}").valid is True + assert validate_chat_template("").valid is True + assert validate_chat_template(" ").valid is True + + +def test_validate_chat_template_reports_syntax_error_with_line(): + result = validate_chat_template("{% if %}{% endif %}") + assert result.valid is False + assert result.error is not None + assert result.error.startswith("Line ") + + +def test_chat_template_from_tokenizer_config_reads_string(): + assert _chat_template_from_tokenizer_config({"chat_template": "HELLO"}) == "HELLO" + assert _chat_template_from_tokenizer_config({"chat_template": " "}) is None + assert _chat_template_from_tokenizer_config({}) is None + + +def test_chat_template_from_tokenizer_config_prefers_named_default(): + config = { + "chat_template": [ + {"name": "tool_use", "template": "TOOL"}, + {"name": "default", "template": "DEFAULT"}, + ] + } + assert _chat_template_from_tokenizer_config(config) == "DEFAULT" + + +def test_chat_template_from_tokenizer_config_falls_back_to_first_entry(): + config = { + "chat_template": [ + {"name": "tool_use", "template": "TOOL"}, + {"name": "other", "template": "OTHER"}, + ] + } + assert _chat_template_from_tokenizer_config(config) == "TOOL" + + +def test_chat_template_from_tokenizer_dir_prefers_jinja_file(tmp_path): + (tmp_path / "chat_template.jinja").write_text("FROM_JINJA", encoding = "utf-8") + (tmp_path / "tokenizer_config.json").write_text( + json.dumps({"chat_template": "FROM_CONFIG"}), encoding = "utf-8" + ) + assert _chat_template_from_tokenizer_dir(tmp_path) == "FROM_JINJA" + + +def test_chat_template_from_tokenizer_dir_reads_tokenizer_config(tmp_path): + (tmp_path / "tokenizer_config.json").write_text( + json.dumps({"chat_template": "FROM_CONFIG"}), encoding = "utf-8" + ) + assert _chat_template_from_tokenizer_dir(tmp_path) == "FROM_CONFIG" + + +def test_chat_template_from_dir_without_variant_prefers_tokenizer(tmp_path): + (tmp_path / "tokenizer_config.json").write_text( + json.dumps({"chat_template": "FROM_CONFIG"}), encoding = "utf-8" + ) + assert _chat_template_from_dir(tmp_path) == "FROM_CONFIG" + + +def test_chat_template_from_dir_with_variant_still_prefers_tokenizer(tmp_path, monkeypatch): + (tmp_path / "tokenizer_config.json").write_text( + json.dumps({"chat_template": "FROM_CONFIG"}), encoding = "utf-8" + ) + (tmp_path / "model-Q4_K_M.gguf").write_bytes(b"") + monkeypatch.setattr("picker.service.read_gguf_chat_template", lambda _path: "FROM_GGUF") + # Selecting a variant must not flip precedence to the embedded GGUF template. + assert _chat_template_from_dir(tmp_path, "Q4_K_M") == "FROM_CONFIG" + + +def test_chat_template_from_dir_with_variant_falls_back_to_gguf(tmp_path, monkeypatch): + (tmp_path / "model-Q4_K_M.gguf").write_bytes(b"") + monkeypatch.setattr("picker.service.read_gguf_chat_template", lambda _path: "FROM_GGUF") + # With no tokenizer sidecar, the embedded GGUF template is still the fallback. + assert _chat_template_from_dir(tmp_path, "Q4_K_M") == "FROM_GGUF" + + +def test_chat_template_from_dir_returns_none_when_absent(tmp_path): + assert _chat_template_from_dir(tmp_path) is None + + +def test_read_default_chat_template_direct_gguf_prefers_sidecar(tmp_path, monkeypatch): + gguf = tmp_path / "model-Q4_K_M.gguf" + gguf.write_bytes(b"") + (tmp_path / "tokenizer_config.json").write_text( + json.dumps({"chat_template": "FROM_CONFIG"}), encoding = "utf-8" + ) + monkeypatch.setattr("picker.service._build_browse_allowlist", lambda: [tmp_path]) + monkeypatch.setattr("picker.service.read_gguf_chat_template", lambda _path: "FROM_GGUF") + # A directly selected .gguf must prefer a maintained sidecar over its embedded copy. + assert read_default_chat_template(str(gguf)) == "FROM_CONFIG" + + +def test_read_default_chat_template_direct_gguf_falls_back_to_embedded(tmp_path, monkeypatch): + gguf = tmp_path / "model-Q4_K_M.gguf" + gguf.write_bytes(b"") + monkeypatch.setattr("picker.service._build_browse_allowlist", lambda: [tmp_path]) + monkeypatch.setattr("picker.service.read_gguf_chat_template", lambda _path: "FROM_GGUF") + # With no sidecar next to the file, the embedded GGUF template is the fallback. + assert read_default_chat_template(str(gguf)) == "FROM_GGUF" + + +def test_tokenizer_config_over_size_limit_is_skipped_not_parsed(tmp_path): + # An oversized tokenizer_config.json must be skipped before json.loads so a + # hostile sidecar cannot exhaust memory. + padding = "x" * (MAX_TEMPLATE_METADATA_BYTES + 1024) + (tmp_path / "tokenizer_config.json").write_text( + json.dumps({"chat_template": "HELLO", "_pad": padding}), encoding = "utf-8" + ) + assert _chat_template_from_tokenizer_dir(tmp_path) is None + + +def test_processor_json_over_size_limit_is_skipped_not_parsed(tmp_path): + padding = "x" * (MAX_TEMPLATE_METADATA_BYTES + 1024) + (tmp_path / "chat_template.json").write_text( + json.dumps({"default": "HELLO", "_pad": padding}), encoding = "utf-8" + ) + assert _chat_template_from_processor_json(tmp_path) is None + + +def test_tokenizer_config_at_size_limit_is_still_read(tmp_path): + # A normal-sized config is unaffected by the bound (regression guard). + (tmp_path / "tokenizer_config.json").write_text( + json.dumps({"chat_template": "FROM_CONFIG"}), encoding = "utf-8" + ) + assert _chat_template_from_tokenizer_dir(tmp_path) == "FROM_CONFIG" + + +def test_remote_template_over_size_limit_is_skipped_before_download(monkeypatch): + # An uncached Hub repo whose template exceeds the cap must be skipped via the + # remote size pre-check, never downloaded. + import huggingface_hub + + monkeypatch.setattr("picker.service.resolve_cached_repo_id_case", lambda name: name) + monkeypatch.setattr("picker.service.iter_hf_cache_snapshots", lambda resolved: []) + + def _fail_download(*args, **kwargs): + raise AssertionError("oversized remote template must not be downloaded") + + def _fake_get_paths_info(self, repo_id, paths, **kwargs): + return [SimpleNamespace(path = p, size = MAX_TEMPLATE_METADATA_BYTES + 1) for p in paths] + + monkeypatch.setattr(huggingface_hub, "hf_hub_download", _fail_download) + monkeypatch.setattr(huggingface_hub.HfApi, "get_paths_info", _fake_get_paths_info) + + assert read_default_chat_template("org/oversized-model") is None + + +def test_remote_oversized_jinja_falls_through_to_tokenizer_template(tmp_path, monkeypatch): + # A raw chat_template.jinja between the response cap (MAX_CHAT_TEMPLATE_BYTES) + # and the download bound (MAX_TEMPLATE_METADATA_BYTES) must not be returned: the + # route drops it, so the remote path must skip the oversized Jinja and fall + # through to the smaller tokenizer_config.json. + import huggingface_hub + from picker.schemas import MAX_CHAT_TEMPLATE_BYTES + + big_jinja = tmp_path / "chat_template.jinja" + big_jinja.write_text("{{ x }}" * (MAX_CHAT_TEMPLATE_BYTES // 4), encoding = "utf-8") + assert MAX_CHAT_TEMPLATE_BYTES < big_jinja.stat().st_size < MAX_TEMPLATE_METADATA_BYTES + tokenizer_config = tmp_path / "tokenizer_config.json" + tokenizer_config.write_text(json.dumps({"chat_template": "SMALL_TEMPLATE"}), encoding = "utf-8") + files = { + "chat_template.jinja": big_jinja, + "tokenizer_config.json": tokenizer_config, + } + + monkeypatch.setattr("picker.service.resolve_cached_repo_id_case", lambda name: name) + monkeypatch.setattr("picker.service.iter_hf_cache_snapshots", lambda resolved: []) + + def _fake_download(repo_id, rel, **kwargs): + target = files.get(rel) + if target is None: + raise FileNotFoundError(rel) + return str(target) + + def _fake_get_paths_info(self, repo_id, paths, **kwargs): + return [ + SimpleNamespace( + path = p, + size = files[p].stat().st_size if p in files else 0, + ) + for p in paths + ] + + monkeypatch.setattr(huggingface_hub, "hf_hub_download", _fake_download) + monkeypatch.setattr(huggingface_hub.HfApi, "get_paths_info", _fake_get_paths_info) + + assert read_default_chat_template("org/big-jinja-model") == "SMALL_TEMPLATE" diff --git a/studio/backend/utils/models/gguf_metadata.py b/studio/backend/utils/models/gguf_metadata.py index 50b3cd3513..749f2c9234 100644 --- a/studio/backend/utils/models/gguf_metadata.py +++ b/studio/backend/utils/models/gguf_metadata.py @@ -50,10 +50,14 @@ _CACHE_MAX_ENTRIES = 4096 # keyed by (file cache key, wanted key). None = key absent / file unreadable. _BOOL_CACHE: Dict[Tuple[_CacheKey, str], Optional[bool]] = {} +_STRING_CACHE: Dict[Tuple[_CacheKey, str], Optional[str]] = {} + # GGUF header dims for the staged/deferred-load UI: context_length, layer_count # (block_count), and moe_layer_count (block_count minus leading dense layers; 0 # if not MoE). One cached pass fills all three so the staged sheet can size every -# slider before the model loads. None = unreadable / not a GGUF. +# slider before the model loads. None = unreadable / not a GGUF. The native +# training context length (``{arch}.context_length``) the UI shows before a model +# loads is read from here via read_gguf_context_length. _DIMS_CACHE: Dict[_CacheKey, Optional[Dict[str, Optional[int]]]] = {} @@ -408,6 +412,83 @@ def _read_gguf_bool(path: str, wanted_key: str) -> Optional[bool]: return result +def _parse_gguf_string(path: str, wanted_key: str) -> Optional[str]: + try: + with open(path, "rb") as f: + head = f.read(24) + if len(head) < 24: + return None + magic, _version, _tcount, kv_count = struct.unpack(" 1 << 20: + break + kbytes = f.read(klen) + if len(kbytes) < klen: + break + key = kbytes.decode("utf-8", "replace") + vt_bytes = f.read(4) + if len(vt_bytes) < 4: + break + vtype = struct.unpack(" 1 << 22: + break + sbytes = f.read(slen) + if len(sbytes) < slen: + break + return sbytes.decode("utf-8", "replace") + if not _skip_gguf_value(f, vtype): + break + except (struct.error, UnicodeDecodeError): + break + except OSError as e: + logger.debug(f"_parse_gguf_string: cannot open {path}: {e}") + return None + except Exception as e: + logger.debug(f"_parse_gguf_string: parse failure on {path}: {e}") + return None + return None + + +def _read_gguf_string(path: str, wanted_key: str) -> Optional[str]: + fkey = _cache_key(path) + if fkey is None: + return None + ckey = (fkey, wanted_key) + with _CACHE_LOCK: + if ckey in _STRING_CACHE: + return _STRING_CACHE[ckey] + result = _parse_gguf_string(path, wanted_key) + with _CACHE_LOCK: + while len(_STRING_CACHE) >= _CACHE_MAX_ENTRIES: + try: + _STRING_CACHE.pop(next(iter(_STRING_CACHE))) + except StopIteration: + break + _STRING_CACHE[ckey] = result + return result + + +def read_gguf_chat_template(path: str) -> Optional[str]: + template = _read_gguf_string(path, "tokenizer.chat_template") + if isinstance(template, str) and template.strip(): + return template + return None + + def read_mmproj_audio_capability(path: str) -> Optional[bool]: """``clip.has_audio_encoder`` from an mmproj GGUF (e.g. Gemma 4's gemma4ua): ``True``/``False`` if present, ``None`` if absent/unreadable. diff --git a/studio/frontend/src/app/routes/__root.tsx b/studio/frontend/src/app/routes/__root.tsx index 57e890dd5a..7137fd6f96 100644 --- a/studio/frontend/src/app/routes/__root.tsx +++ b/studio/frontend/src/app/routes/__root.tsx @@ -196,9 +196,6 @@ function RootLayout() { chatRuntime.setActiveThreadId(null); chatRuntime.setActiveProjectId(null); chatRuntime.setIncognito(false); - // Detach the staging UI but keep any in-flight download running, like Hub. - if (chatRuntime.pendingSelection) - chatRuntime.abandonStagedModel({ keepDownload: true }); void navigate({ to: "/chat", search: { new: crypto.randomUUID() }, @@ -221,10 +218,6 @@ function RootLayout() { chatRuntime.setActiveProjectId(null); chatRuntime.setActiveThreadId(null); chatRuntime.setIncognito(false); - // Leaving chat must not kill an in-flight download: detach the staging UI - // but keep the transfer running in the manager, like a Hub download. - if (chatRuntime.pendingSelection) - chatRuntime.abandonStagedModel({ keepDownload: true }); }, [isChatRoute]); return ( diff --git a/studio/frontend/src/components/app-sidebar.tsx b/studio/frontend/src/components/app-sidebar.tsx index 8eab03133b..293971904f 100644 --- a/studio/frontend/src/components/app-sidebar.tsx +++ b/studio/frontend/src/components/app-sidebar.tsx @@ -1011,28 +1011,6 @@ export function AppSidebar() { - {isPinned ? ( - - - - - - Unpin - - - ) : null} ); } diff --git a/studio/frontend/src/components/assistant-ui/model-selector/remembered-load-settings.ts b/studio/frontend/src/components/assistant-ui/model-selector/remembered-load-settings.ts deleted file mode 100644 index 08492ab480..0000000000 --- a/studio/frontend/src/components/assistant-ui/model-selector/remembered-load-settings.ts +++ /dev/null @@ -1,79 +0,0 @@ -// SPDX-License-Identifier: AGPL-3.0-only -// Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0 - -// Per-model pre-load inference settings, persisted in localStorage so the load -// dialog can offer "Remember settings for ". GGUF picks only: every -// field is a llama.cpp load knob, so all save/restore call sites gate on -// GGUF-ness (a non-GGUF blob would only snapshot leftover standing values). - -const KEY = "unsloth_load_settings"; - -export interface RememberedLoadSettings { - contextLength: number | null; - kvCacheDtype: string | null; - speculativeType: string | null; - specDraftNMax: number | null; - tensorParallel: boolean; - // GPU Memory controls. Optional so an older blob (which lacked them) still - // parses, leaving the live knobs untouched on apply. The mode is kept with the - // manual knobs (gpuLayers/nCpuMoe are ignored outside Manual mode). A null - // selectedGpuIds is meaningful (all GPUs), so it's distinguished from absent. - // The per-GPU split ratio is deliberately NOT remembered: it's positionally - // bound to the exact GPU set/order and unvalidated, so it would mismatch. - gpuMemoryMode?: "auto" | "manual"; - gpuLayers?: number; - nCpuMoe?: number; - selectedGpuIds?: number[] | null; -} - -// Storage key for a pick's remembered settings, scoped per quant (the VRAM-budget -// knobs differ per quant). An HF repo collapses its GGUF variants into one `id`, -// so fold the variant in. Local .gguf paths are already file-specific; native -// drag-drop files key by display label, so same-named files share an entry. -export function rememberedLoadSettingsKey(selection: { - id: string; - ggufVariant?: string | null; -}): string { - return selection.ggufVariant - ? `${selection.id}::${selection.ggufVariant}` - : selection.id; -} - -function readAll(): Record { - try { - return JSON.parse(localStorage.getItem(KEY) ?? "{}"); - } catch { - return {}; - } -} - -function writeAll(all: Record) { - try { - localStorage.setItem(KEY, JSON.stringify(all)); - } catch { - // Ignore quota / unavailable storage. - } -} - -export function loadRememberedLoadSettings( - key: string, -): RememberedLoadSettings | null { - return readAll()[key] ?? null; -} - -export function saveRememberedLoadSettings( - key: string, - settings: RememberedLoadSettings, -) { - const all = readAll(); - all[key] = settings; - writeAll(all); -} - -export function clearRememberedLoadSettings(key: string) { - const all = readAll(); - if (key in all) { - delete all[key]; - writeAll(all); - } -} diff --git a/studio/frontend/src/features/chat/api/chat-adapter.ts b/studio/frontend/src/features/chat/api/chat-adapter.ts index 7083f02288..b0127b5e40 100644 --- a/studio/frontend/src/features/chat/api/chat-adapter.ts +++ b/studio/frontend/src/features/chat/api/chat-adapter.ts @@ -2,10 +2,7 @@ // Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0 import { getAuthToken } from "@/features/auth"; -import { - loadRememberedLoadSettings, - rememberedLoadSettingsKey, -} from "@/components/assistant-ui/model-selector/remembered-load-settings"; +import { resolveInitialConfig } from "@/features/model-picker"; import { projectHasSources } from "@/features/rag/api/rag-api"; import { apiUrl } from "@/lib/api-base"; import { parseParamCountB } from "@/lib/model-size"; @@ -46,7 +43,7 @@ import { type PendingImageEditReference, type RagAutoInject, GPU_LAYERS_AUTO, - loadedGpuMemoryFieldsUnlessStaged, + loadedGpuMemoryFields, reconcilePersistedGpuIds, resolveLoadedSpeculativeSettings, resolveSpeculativeSettingsForLoad, @@ -1533,65 +1530,56 @@ async function autoLoadSmallestModel(): Promise<{ return false; } const currentStore = useChatRuntimeStore.getState(); - // Blobs are saved for GGUF picks only (the sheet gates on it), so don't - // let a legacy non-GGUF blob feed a stale context/spec choice into a - // safetensors auto-load. - const remembered = - candidate.kind === "gguf" - ? loadRememberedLoadSettings( - rememberedLoadSettingsKey({ - id: candidate.id, - ggufVariant: candidate.ggufVariant, - }), - ) - : null; + const { config } = resolveInitialConfig(candidate.id, candidate.ggufVariant); const effectiveMaxSeqLength = resolveLoadMaxSeqLength({ modelId: candidate.id, ggufVariant: candidate.ggufVariant, isGguf: candidate.kind === "gguf", - customContextLength: remembered?.contextLength ?? null, + customContextLength: config.customContextLength, ggufContextLength: null, currentCheckpoint: currentStore.params.checkpoint, activeGgufVariant: currentStore.activeGgufVariant, - maxSeqLength: candidate.maxSeqLength, + maxSeqLength: config.maxSeqLength ?? candidate.maxSeqLength, presetSource: currentStore.activePresetSource, }); - // The GPU knobs are per-model, so read them from the same remembered - // settings that fed effectiveMaxSeqLength -- on a background auto-load the - // live store holds session defaults, not the saved Manual mode / layer pin / - // GPU pick. Absent fields fall back like applyRememberedLoadSettings: the - // mode to the store (a persisted standing preference), the per-model knobs to - // their defaults. The saved GPU pick is reconciled against the GPUs present - // now, like the interactive restore. + // The GPU knobs are per-model, so read them from the same per-model config + // that fed effectiveMaxSeqLength -- on a background auto-load the live store + // holds session defaults, not the saved Manual mode / layer pin / GPU pick. + // Absent fields fall back like the interactive restore: the mode to the store + // (a persisted standing preference), the per-model knobs to their defaults. + // The saved GPU pick is reconciled against the GPUs present now. const effectiveGpuMemoryMode = - remembered?.gpuMemoryMode ?? currentStore.gpuMemoryMode; - const effectiveGpuLayers = remembered?.gpuLayers ?? GPU_LAYERS_AUTO; - const effectiveNCpuMoe = remembered?.nCpuMoe ?? 0; - if (remembered?.selectedGpuIds != null) { + config.gpuMemoryMode ?? currentStore.gpuMemoryMode; + const effectiveGpuLayers = config.gpuLayers ?? GPU_LAYERS_AUTO; + const effectiveNCpuMoe = config.nCpuMoe ?? 0; + if (config.selectedGpuIds != null) { // Warm the device cache first: on a cold cache the reconcile passes the // saved pick through unvalidated, and a stale cross-host pick then fails // the load with the picker hidden. await ensureGpuDeviceCache(); } const effectiveGpuIds = - remembered?.selectedGpuIds !== undefined - ? reconcilePersistedGpuIds(remembered.selectedGpuIds) + config.selectedGpuIds !== undefined + ? reconcilePersistedGpuIds(config.selectedGpuIds) : null; // Under Manual GPU memory + Auto layers, llama.cpp's --fit owns context // sizing, so send 0 (or the pinned length). GGUF-only; a no-op otherwise. - // The context pin is per-model too, so it comes from remembered settings, - // not the live store. + // The context pin is per-model too, so it comes from the saved config, not + // the live store. const fitMaxSeqLength = resolveFitMaxSeqLength( candidate.kind === "gguf", effectiveGpuMemoryMode, effectiveGpuLayers, - remembered?.contextLength ?? null, + config.customContextLength ?? null, effectiveMaxSeqLength, ); const effectiveSpeculativeType = - remembered?.speculativeType ?? specSettings.speculativeType; + config.speculativeType ?? specSettings.speculativeType; const effectiveSpecDraftNMax = - remembered?.specDraftNMax ?? specSettings.specDraftNMax; + config.specDraftNMax ?? specSettings.specDraftNMax; + const effectiveChatTemplateOverride = config.chatTemplateOverride?.trim() + ? config.chatTemplateOverride + : null; if ( !(await canAutoLoad({ model_path: candidate.id, @@ -1621,10 +1609,11 @@ async function autoLoadSmallestModel(): Promise<{ is_lora: false, gguf_variant: candidate.ggufVariant, trust_remote_code: trustRemoteCode, - cache_type_kv: remembered?.kvCacheDtype ?? null, + chat_template_override: effectiveChatTemplateOverride, + cache_type_kv: config.kvCacheDtype, speculative_type: effectiveSpeculativeType, spec_draft_n_max: effectiveSpecDraftNMax, - tensor_parallel: remembered?.tensorParallel ?? false, + tensor_parallel: config.tensorParallel, // GGUF-only: the safetensors fallback loads via HF auto-placement (no // explicit pins). The split ratio is deliberately never remembered // (positionally bound to an exact GPU set), so auto-load leaves llama.cpp's @@ -1638,7 +1627,12 @@ async function autoLoadSmallestModel(): Promise<{ } : {}), }); - saveSpeculativeType(effectiveSpeculativeType); + // Only persist the global preference when the value came from the global + // settings. A per-model config's choice must stay load-local, or autoloading + // a remembered model on startup would rewrite the global default. + if (config.speculativeType == null) { + saveSpeculativeType(effectiveSpeculativeType); + } // Self-gates on is_gguf (skips diffusion), so persists only for a real GGUF load. persistGpuMemoryModeOnLoad(loadResp, effectiveGpuMemoryMode); useChatRuntimeStore @@ -1650,6 +1644,9 @@ async function autoLoadSmallestModel(): Promise<{ ); store.setParams({ ...store.params, + ...(candidate.kind === "gguf" + ? {} + : { maxSeqLength: effectiveMaxSeqLength }), maxTokens: candidate.kind === "gguf" ? loadResp.context_length ?? 131072 @@ -1676,7 +1673,7 @@ async function autoLoadSmallestModel(): Promise<{ const keepCustomCtx = resolveManualAutoCtxPin( effectiveGpuMemoryMode, effectiveGpuLayers, - remembered?.contextLength ?? null, + config.customContextLength ?? null, ); useChatRuntimeStore.setState({ ggufContextLength: loadResp.context_length ?? 131072, @@ -1694,13 +1691,14 @@ async function autoLoadSmallestModel(): Promise<{ loadedKvCacheDtype: loadResp.cache_type_kv ?? null, tensorParallel: loadResp.tensor_parallel ?? false, loadedTensorParallel: loadResp.tensor_parallel ?? false, - ...loadedGpuMemoryFieldsUnlessStaged(loadResp, { - customContextLength: keepCustomCtx, - }), + ...loadedGpuMemoryFields(loadResp), loadedCustomContextLength: keepCustomCtx, defaultChatTemplate: loadResp.chat_template ?? null, - chatTemplateOverride: null, - loadedChatTemplateOverride: null, + chatTemplateOverride: effectiveChatTemplateOverride, + loadedChatTemplateOverride: effectiveChatTemplateOverride, + // Retain the saved requested context so re-saving the config keeps the + // override; null stays null (auto/VRAM-fit). + customContextLength: config.customContextLength, loadedIsMultimodal: isMultimodalResponse(loadResp), loadedIsDiffusion: loadResp.is_diffusion ?? false, ...resolveLoadedSpeculativeSettings(loadResp), @@ -1720,10 +1718,11 @@ async function autoLoadSmallestModel(): Promise<{ loadedTensorParallel: loadResp.tensor_parallel ?? false, // Non-GGUF response: clears any stale GPU baseline a prior manual-GPU // GGUF load left, matching the interactive/status sibling load paths. - ...loadedGpuMemoryFieldsUnlessStaged(loadResp), + ...loadedGpuMemoryFields(loadResp), defaultChatTemplate: loadResp.chat_template ?? null, - chatTemplateOverride: null, - loadedChatTemplateOverride: null, + chatTemplateOverride: effectiveChatTemplateOverride, + loadedChatTemplateOverride: effectiveChatTemplateOverride, + customContextLength: null, ...resolveLoadedSpeculativeSettings(loadResp), loadedIsMultimodal: isMultimodalResponse(loadResp), loadedIsDiffusion: loadResp.is_diffusion ?? false, @@ -1988,7 +1987,7 @@ async function autoLoadSmallestModel(): Promise<{ loadedKvCacheDtype: loadResp.cache_type_kv ?? null, tensorParallel: loadResp.tensor_parallel ?? false, loadedTensorParallel: loadResp.tensor_parallel ?? false, - ...loadedGpuMemoryFieldsUnlessStaged(loadResp), + ...loadedGpuMemoryFields(loadResp), // Drives the GPU Memory controls' diffusion gate; set alongside the // GPU fields on every load path so the gate can't read stale. loadedIsDiffusion: loadResp.is_diffusion ?? false, diff --git a/studio/frontend/src/features/chat/api/chat-api.ts b/studio/frontend/src/features/chat/api/chat-api.ts index 631474c39a..de3e5e370c 100644 --- a/studio/frontend/src/features/chat/api/chat-api.ts +++ b/studio/frontend/src/features/chat/api/chat-api.ts @@ -377,14 +377,33 @@ export async function listCachedModels( return data.cached; } -export async function deleteCachedModel( +export interface CachedModelPath { + path: string; + is_dir: boolean; +} + +/** Absolute on-disk path of a cached repo or one of its GGUF variants. */ +export async function getCachedModelPath( + repoId: string, + variant?: string, +): Promise { + const params = new URLSearchParams({ repo_id: repoId }); + if (variant) params.set("variant", variant); + const response = await authFetch( + `/api/models/cached-model-path?${params.toString()}`, + ); + return parseJsonOrThrow(response); +} + +/** Reveal a cached repo (or one GGUF variant's file) in the OS file manager. */ +export async function revealCachedModel( repoId: string, variant?: string, ): Promise { const payload: Record = { repo_id: repoId }; if (variant) payload.variant = variant; - const response = await authFetch("/api/models/delete-cached", { - method: "DELETE", + const response = await authFetch("/api/models/reveal-cached-model", { + method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify(payload), }); diff --git a/studio/frontend/src/features/chat/chat-page.tsx b/studio/frontend/src/features/chat/chat-page.tsx index 217eaf8b6d..ef018445e0 100644 --- a/studio/frontend/src/features/chat/chat-page.tsx +++ b/studio/frontend/src/features/chat/chat-page.tsx @@ -2,16 +2,19 @@ // Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0 import { + applyModelLoadConfigToRuntime, + currentRuntimePerModelConfig, type DeletedModelRef, type ExternalModelOption, type LoraModelOption, type ModelOption, ModelSelector, -} from "@/components/assistant-ui/model-selector"; -import { - loadRememberedLoadSettings, - rememberedLoadSettingsKey, -} from "@/components/assistant-ui/model-selector/remembered-load-settings"; + type ModelSelectorChangeMeta, + type PerModelConfig, + resolveInitialConfig, + SidebarModelConfig, + useActiveModelConfig, +} from "@/features/model-picker"; import { ProjectComposer, Thread } from "@/components/assistant-ui/thread"; import { CopyableErrorChip } from "@/components/ui/copyable-error-chip"; import { @@ -27,10 +30,10 @@ import { } from "@/components/ui/resizable"; import { useSidebar } from "@/components/ui/sidebar"; import { Tooltip, TooltipContent } from "@/components/ui/tooltip"; -import { useLatestRef } from "@/features/hub/hooks/use-latest-ref"; import { DOWNLOAD_KIND, downloadManager, + useRepoDownload, } from "@/features/hub/download-manager"; import { type NativeIntent, @@ -93,7 +96,6 @@ import { renameChatItem, useChatSidebarItems, } from "./hooks/use-chat-sidebar-items"; -import { useStagedModelPreparation } from "./hooks/use-staged-model-preparation"; import { clearTrainingCompareHandoff, getTrainingCompareHandoff, @@ -128,10 +130,8 @@ import { hasGgufSource, isDownloadableHubRepo, loadOptionalBool, - pendingSelectionMatches, useChatRuntimeStore, } from "./stores/chat-runtime-store"; -import type { PendingModelSelection } from "./stores/chat-runtime-store"; import { useChatPreferencesStore } from "./stores/chat-preferences-store"; import { useExternalProvidersStore } from "./stores/external-providers-store"; import { buildChatTourSteps } from "./tour"; @@ -385,6 +385,7 @@ type CompareModelSelection = { id: string; isLora: boolean; ggufVariant?: string; + config?: PerModelConfig; }; function modelMatchesDeleted( @@ -645,6 +646,8 @@ function GeneralCompareHeader({ loraModels, externalModels, value, + selectedConfig, + selectedGgufVariant, onValueChange, onFoldersChange, onModelsChange, @@ -655,9 +658,11 @@ function GeneralCompareHeader({ loraModels: LoraModelOption[]; externalModels: ExternalModelOption[]; value: string; + selectedConfig?: PerModelConfig | null; + selectedGgufVariant?: string | null; onValueChange: ( id: string, - meta: { isLora: boolean; ggufVariant?: string }, + meta: ModelSelectorChangeMeta, ) => void; onFoldersChange?: () => void; onModelsChange?: (deletedModel?: DeletedModelRef) => void; @@ -684,6 +689,8 @@ function GeneralCompareHeader({ loraModels={loraModels} externalModels={externalModels} value={value} + selectedConfig={selectedConfig} + selectedGgufVariant={selectedGgufVariant} onValueChange={onValueChange} onFoldersChange={onFoldersChange} onModelsChange={onModelsChange} @@ -811,11 +818,14 @@ const GeneralCompareContent = memo(function GeneralCompareContent({ loraModels={loraModels} externalModels={externalModels} value={model1.id} + selectedConfig={model1.config} + selectedGgufVariant={model1.ggufVariant} onValueChange={(id, meta) => setModel1({ id, isLora: meta.isLora, ggufVariant: meta.ggufVariant, + config: meta.config, }) } onFoldersChange={onFoldersChange} @@ -838,11 +848,14 @@ const GeneralCompareContent = memo(function GeneralCompareContent({ loraModels={loraModels} externalModels={externalModels} value={model2.id} + selectedConfig={model2.config} + selectedGgufVariant={model2.ggufVariant} onValueChange={(id, meta) => setModel2({ id, isLora: meta.isLora, ggufVariant: meta.ggufVariant, + config: meta.config, }) } onFoldersChange={onFoldersChange} @@ -1236,6 +1249,13 @@ export function validateChatSearch(search: Record): ChatSearch }; } +type PendingHubAutoLoad = { + selection: SelectedModelInput; + contextKey: string; + originCheckpoint: string; + originGgufVariant: string | null; +}; + // `search` comes from RootLayout (not useSearch) so ChatPage stays mounted off-route // (keeping an in-flight generation alive), frozen to the last /chat search. `active` // is false off-route: close body-portaled surfaces and stop route-specific listeners @@ -1248,30 +1268,6 @@ export function ChatPage({ const settingsOpen = useChatRuntimeStore((s) => s.settingsPanelOpen); const setSettingsOpen = useChatRuntimeStore((s) => s.setSettingsPanelOpen); - // Deferred-load staging: downloads a staged GGUF (if needed) and reads its - // header context so the sheet can show the context slider before the load. - // autoLoad picks instead load the cached file as soon as the download ends; - // selectModel is defined below, so the load runs through a ref. - const autoLoadStagedRef = useRef< - ((pending: PendingModelSelection) => void) | null - >(null); - const stagedDownload = useStagedModelPreparation({ - onAutoLoad: (pending) => autoLoadStagedRef.current?.(pending), - }); - // Abandon a staged pick: the store action cancels its in-flight download and - // reverts the edited knobs, so nothing lingers after the user walks away. - const abandonStaged = useCallback(() => { - useChatRuntimeStore.getState().abandonStagedModel(); - }, []); - // Detach a staged pick on navigation without cancelling its download: the - // transfer keeps running in the manager and lands in cache, like Hub. - const detachStaged = useCallback(() => { - useChatRuntimeStore.getState().abandonStagedModel({ keepDownload: true }); - }, []); - // Tracks whether the chat page is still mounted, so a staged-load failure that - // resolves after the user left chat doesn't resurrect the abandoned pick. - const mountedRef = useRef(true); - useEffect(() => () => void (mountedRef.current = false), []); const incognito = useChatRuntimeStore((s) => s.incognito); const setIncognito = useChatRuntimeStore((s) => s.setIncognito); const incognitoLabel = incognito @@ -1363,6 +1359,9 @@ export function ChatPage({ const ggufContextLength = useChatRuntimeStore( (state) => state.ggufContextLength, ); + const ggufNativeContextLength = useChatRuntimeStore( + (state) => state.ggufNativeContextLength, + ); const contextUsage = useChatRuntimeStore((state) => state.contextUsage); const modelsFromStore = useChatRuntimeStore((state) => state.models); const lorasFromStore = useChatRuntimeStore((state) => state.loras); @@ -1440,39 +1439,37 @@ export function ChatPage({ refreshRef.current = refresh; selectModelRef.current = selectModel; }, [refresh, selectModel]); - // Load a cached autoLoad pick once its download finishes. The sheet was never - // opened, so on a load failure just drop the orphaned staged knobs. The knobs - // were already seeded on stage, so keepSpeculative only when a config was - // saved -- otherwise the standing speculative preference should win. - autoLoadStagedRef.current = (pending) => { - // Blobs are saved for GGUF picks only (the sheet gates on it), so don't - // let a legacy non-GGUF blob claim a seeded config here. - const remembered = hasGgufSource(pending) - ? loadRememberedLoadSettings(rememberedLoadSettingsKey(pending)) - : null; - void selectModel({ - ...pending, - isDownloaded: true, - forceReload: true, - keepSpeculative: remembered != null, - throwOnError: true, - }).catch(() => { - const store = useChatRuntimeStore.getState(); - // selectModel only clears pendingSelection on success, so a failed - // auto-load leaves our staged pick (and its edited load knobs) behind. - // Abandon it when it is still the active stage; otherwise just revert the - // settings if the stage was already cleared by something else. - if (pendingSelectionMatches(store.pendingSelection, pending)) { - store.abandonStagedModel(); - } else if (!store.pendingSelection) { - store.resetModelSettingsToLoaded(); - } - }); - }; + const rememberedConfigFor = useCallback( + (selection: { + id: string; + ggufVariant?: string | null; + source?: string; + }) => { + if (selection.source === "external") return null; + const resolved = resolveInitialConfig(selection.id, selection.ggufVariant); + return resolved.remembered ? resolved.config : null; + }, + [], + ); const isExternalModel = useMemo( () => isExternalModelId(inferenceParams.checkpoint), [inferenceParams.checkpoint], ); + const { + checkpoint: runtimeCheckpoint, + isGguf: runtimeModelIsGguf, + config: activeModelConfig, + } = useActiveModelConfig(); + const activeModelIsGguf = + runtimeCheckpoint != null && !isExternalModel && runtimeModelIsGguf; + const activeModelIsLora = useMemo(() => { + const checkpoint = inferenceParams.checkpoint; + if (!checkpoint || isExternalModel) return false; + const model = modelsFromStore.find((entry) => entry.id === checkpoint); + if (model) return model.isLora; + const lora = lorasFromStore.find((entry) => entry.id === checkpoint); + return lora?.exportType === "lora"; + }, [inferenceParams.checkpoint, isExternalModel, modelsFromStore, lorasFromStore]); const reasoningEnabled = useChatRuntimeStore((s) => s.reasoningEnabled); const reasoningStyle = useChatRuntimeStore((s) => s.reasoningStyle); const reasoningEffort = useChatRuntimeStore((s) => s.reasoningEffort); @@ -1783,75 +1780,21 @@ export function ChatPage({ closeArtifactSurface(); }, [activeThreadId, closeArtifactSurface, selectedArtifact, view]); - // Abandon a staged (not-yet-loaded) pick when the chat context actually - // changes — switching threads, leaving single view, or starting a new chat / - // project — so a stale Load button can't resurface in a different context. - // New Chat keeps activeThreadId null and only bumps the `new` search nonce, so - // the key includes the route identity, not just the thread. Mirrors the - // incognito reset pattern. (Route exit is handled in __root.tsx, which runs - // after this unmounts.) Clear only on a real change, never on mount: staging - // from the Hub sets pendingSelection then navigates here, and clearing on - // mount would wipe it. Comparing the previous context (rather than a first-run - // flag) is also safe under StrictMode's double-invoke and component remounts. - const chatContextKey = `${view.mode}|${activeThreadId ?? ""}|${search.new ?? ""}|${search.project ?? ""}`; - const chatContextKeyRef = useLatestRef(chatContextKey); - const prevChatContextRef = useRef(null); - useEffect(() => { - const prev = prevChatContextRef.current; - prevChatContextRef.current = chatContextKey; - if (prev === null || prev === chatContextKey) return; - detachStaged(); - }, [chatContextKey, detachStaged]); - const hasActiveModel = Boolean(inferenceParams.checkpoint); - // Load immediately, or — when "Load on selection" is off — stage the pick so - // its load options can be set first. Shared by the main selector, native - // drag-drop/picker, and the dropped-file chip (the Hub stages via the store). + const chatContextKey = `${view.mode}|${activeThreadId ?? ""}|${search.new ?? ""}|${search.project ?? ""}`; + const [pendingHubAutoLoad, setPendingHubAutoLoad] = + useState(null); const stageOrLoad = useCallback( async (selection: SelectedModelInput) => { const store = useChatRuntimeStore.getState(); - // An un-cached HF repo (GGUF variant or a full non-GGUF snapshot) downloads - // through the manager first (global indicator), then auto-loads. Everything - // else -- cached picks, local/native files, LoRA, external -- loads now. const wantManagerDownload = isDownloadableHubRepo(selection) && !selection.isDownloaded; - if ( - (!hasGgufSource(selection) && !wantManagerDownload) || - (store.loadOnSelection && selection.isDownloaded) - ) { - // Detach any staged pick first so its edited knobs (e.g. a custom - // context length) don't leak into this immediate load -- resolveLoad - // reads customContextLength before checking the target is GGUF. Detach - // (not abandon) keeps its download running. - detachStaged(); - // Load-on-selection skips the sheet, so seed the saved knobs here the - // way the sheet's restore effect would; the switch would otherwise reset - // the remembered speculative choice (keepSpeculative below prevents it). - const remembered = hasGgufSource(selection) - ? loadRememberedLoadSettings(rememberedLoadSettingsKey(selection)) - : null; - if (remembered) store.applyRememberedLoadSettings(remembered); - await selectModel( - remembered ? { ...selection, keepSpeculative: true } : selection, - ); - return; - } - // Loads can't queue behind each other, but a download is independent: if - // the pick needs downloading, start it in the manager so it runs alongside - // the load. Nothing to download (already on device) just waits. if (store.modelLoading) { - // Both an uncached non-GGUF snapshot (wantManagerDownload) and an - // uncached remote GGUF quant download through the manager, so either can - // run in the background while another model loads. wantManagerDownload - // excludes GGUF by design, so the GGUF case is checked separately. const wantBackgroundDownload = wantManagerDownload || (selection.source === "hub" && hasGgufSource(selection) && !selection.isDownloaded); - // The model currently loading already downloads as part of its own load - // (the /load flow fetches before setting the checkpoint), so re-picking - // it must not kick off a second transfer against the same cache. const isLoadingThisPick = !!loadingModel && normalizeModelRef(loadingModel.id) === @@ -1862,11 +1805,6 @@ export function ChatPage({ description: "It's downloading as part of the load in progress.", }); } else if (wantBackgroundDownload) { - // Only claim the download started once a job is actually created. A - // transport conflict records state that is only resolvable from the - // Hub download card, so point the user there instead of showing a - // success toast for a transfer that never began; "busy" and "error" - // already surface their own toasts. const outcome = await downloadManager.requestStart({ kind: DOWNLOAD_KIND.MODEL, repoId: selection.id, @@ -1883,6 +1821,11 @@ export function ChatPage({ description: "An earlier partial download used a different transport. Open the Hub tab to resume or restart it.", }); + } else if (outcome === "busy") { + toast.info("Download already in progress", { + description: + "Another download for this model is still running. Reselect it once that finishes to load it.", + }); } } else { toast.info("Another model is already loading", { @@ -1891,23 +1834,128 @@ export function ChatPage({ } return; } - // Detach the prior staged pick (keeping its download) before rebinding, so - // a second pick downloads alongside the first instead of cancelling it. - detachStaged(); - store.stageModel({ - id: selection.id, - isLora: selection.isLora, - ggufVariant: selection.ggufVariant, - isDownloaded: selection.isDownloaded, - expectedBytes: selection.expectedBytes, - nativePathToken: selection.nativePathToken, - isGguf: selection.isGguf, - isHubRepo: wantManagerDownload || undefined, - autoLoad: store.loadOnSelection, + const wantManagerStage = + wantManagerDownload || + (selection.source === "hub" && + hasGgufSource(selection) && + !selection.isDownloaded); + if (wantManagerStage) { + setPendingHubAutoLoad((current) => + current && + current.selection.id === selection.id && + (current.selection.ggufVariant ?? null) === + (selection.ggufVariant ?? null) && + current.contextKey === chatContextKey && + current.originCheckpoint === store.params.checkpoint && + current.originGgufVariant === store.activeGgufVariant + ? current + : { + selection, + contextKey: chatContextKey, + originCheckpoint: store.params.checkpoint, + originGgufVariant: store.activeGgufVariant, + }, + ); + return; + } + setPendingHubAutoLoad(null); + const previousConfig = currentRuntimePerModelConfig({ + includeMaxSeqLength: true, + }); + const hasAppliedConfig = applyModelLoadConfigToRuntime( + selection.config ?? rememberedConfigFor(selection), + ); + await selectModel({ + ...selection, + ...(hasAppliedConfig ? { keepSpeculative: true } : {}), + previousConfig, }); }, - [detachStaged, selectModel, loadingModel], + [selectModel, loadingModel, rememberedConfigFor, chatContextKey], ); + useRepoDownload({ + kind: DOWNLOAD_KIND.MODEL, + repoId: pendingHubAutoLoad?.selection.id ?? "__hub_autoload_idle__", + activeVariant: pendingHubAutoLoad?.selection.ggufVariant ?? null, + onComplete: (variant) => { + const pending = pendingHubAutoLoad; + if ( + !pending || + (pending.selection.ggufVariant ?? null) !== (variant ?? null) + ) { + return; + } + setPendingHubAutoLoad(null); + const store = useChatRuntimeStore.getState(); + if ( + !active || + pending.contextKey !== chatContextKey || + normalizeModelRef(pending.originCheckpoint) !== + normalizeModelRef(store.params.checkpoint) || + pending.originGgufVariant !== store.activeGgufVariant + ) { + return; + } + void stageOrLoad({ ...pending.selection, isDownloaded: true }); + }, + onError: (variant) => { + if ( + pendingHubAutoLoad && + (pendingHubAutoLoad.selection.ggufVariant ?? null) === (variant ?? null) + ) { + setPendingHubAutoLoad(null); + } + }, + onCancelled: (variant) => { + if ( + pendingHubAutoLoad && + (pendingHubAutoLoad.selection.ggufVariant ?? null) === (variant ?? null) + ) { + setPendingHubAutoLoad(null); + } + }, + }); + useEffect(() => { + const pending = pendingHubAutoLoad; + if (!pending) return; + let active = true; + void (async () => { + const outcome = await downloadManager.requestStart({ + kind: DOWNLOAD_KIND.MODEL, + repoId: pending.selection.id, + variant: pending.selection.ggufVariant ?? null, + expectedBytes: pending.selection.expectedBytes ?? 0, + }); + if (!active) return; + if (outcome === "started") { + toast.info("Downloading model", { + description: "It'll load automatically once the download finishes.", + }); + return; + } + if (outcome === "conflict") { + // Keep pendingHubAutoLoad bound so this surface's cleanup does not wipe + // the conflict just recorded by requestStart (which the toast points the + // user to); resolving it from the Hub completes the download and this + // surface's onComplete auto-loads, mirroring the "started" branch. + toast.info("Resume this download from the Hub", { + description: + "An earlier partial download used a different transport. Open the Hub tab to resume or restart it.", + }); + return; + } + if (outcome === "busy") { + toast.info("Download already in progress", { + description: + "Another download for this model is still running. Reselect it once that finishes to load it.", + }); + } + setPendingHubAutoLoad((current) => (current === pending ? null : current)); + })(); + return () => { + active = false; + }; + }, [pendingHubAutoLoad]); const loadNativeModelIntent = useCallback( async (intent: NativeIntent, loadingDescription: string) => { const label = @@ -1915,6 +1963,7 @@ export function ChatPage({ await stageOrLoad({ id: label, nativePathToken: intent.path.token, + nativePathExpiresAtMs: intent.path.expiresAtMs ?? null, isDownloaded: true, loadingDescription, forceReload: true, @@ -1965,28 +2014,20 @@ export function ChatPage({ const handleCheckpointChange = useCallback( ( value: string, - meta?: { - source?: string; - isLora: boolean; - ggufVariant?: string; - isDownloaded?: boolean; - expectedBytes?: number; - isGguf?: boolean; - }, + meta?: ModelSelectorChangeMeta, ) => { const store = useChatRuntimeStore.getState(); const currentCheckpoint = store.params.checkpoint; const currentVariant = store.activeGgufVariant; - if ( - !value || - (value === currentCheckpoint && - (meta?.ggufVariant ?? null) === (currentVariant ?? null)) - ) + if (!value) return; + setPendingHubAutoLoad(null); + const isSameLoadedModel = + value === currentCheckpoint && + (meta?.ggufVariant ?? null) === (currentVariant ?? null); + if (isSameLoadedModel && !meta?.forceReload) { return; + } if (meta?.source === "external" || isExternalModelId(value)) { - // Switching to an external model abandons any staged local pick: cancel - // its download too (setCheckpoint below only clears the pending + knobs). - abandonStaged(); const selectedExternal = parseExternalModelId(value); const selectedProvider = selectedExternal ? externalProvidersForChat.find( @@ -2087,6 +2128,7 @@ export function ChatPage({ ggufMaxContextLength: null, ggufNativeContextLength: null, activeNativePathToken: null, + activeNativePathExpiresAtMs: null, // Clear previous-model counters, else the relaxed external-provider // render gate shows stale stats until the next completion. contextUsage: null, @@ -2158,19 +2200,18 @@ export function ChatPage({ source: meta?.source, isLora: meta?.isLora, ggufVariant: meta?.ggufVariant, - isDownloaded: meta?.isDownloaded, + isDownloaded: meta?.isDownloaded || isSameLoadedModel, expectedBytes: meta?.expectedBytes, isGguf: meta?.isGguf, + config: meta?.config, + nativePathToken: meta?.nativePathToken, + nativePathExpiresAtMs: meta?.nativePathExpiresAtMs, + forceReload: isSameLoadedModel || undefined, }; - // "Load on selection" off: stage the model and open settings so its - // load knobs (tensor parallel, context length…) can be set, then it - // loads once via the sheet's Load button. The currently loaded model - // stays put until the user commits. await stageOrLoad(selection); })(); }, [ - abandonStaged, activeThreadId, externalProvidersForChat, modelsFromStore, @@ -2178,6 +2219,45 @@ export function ChatPage({ view, ], ); + const handleReloadActiveModel = useCallback( + (config: PerModelConfig) => { + const checkpoint = inferenceParams.checkpoint; + if (!checkpoint) return; + const runtime = useChatRuntimeStore.getState(); + const nativeToken = runtime.activeNativePathToken; + const nativeExpiry = runtime.activeNativePathExpiresAtMs; + // A file-picked GGUF is reachable only via its native path token, which + // the desktop host prunes after a TTL. Reusing an expired token makes the + // reload fail with an opaque error, so prompt the user to re-select the + // file instead. + if (nativeToken && nativeExpiry != null && Date.now() >= nativeExpiry) { + toast.error("This local model file's access has expired.", { + description: "Re-select the model file to reload it.", + }); + return; + } + handleCheckpointChange(checkpoint, { + source: "local", + isLora: activeModelIsLora, + ggufVariant: activeGgufVariant ?? undefined, + // Without the native token the reload validates the display label as a + // repo and fails. + nativePathToken: nativeToken ?? undefined, + nativePathExpiresAtMs: nativeExpiry, + isGguf: activeModelIsGguf, + isDownloaded: true, + config, + forceReload: true, + }); + }, + [ + inferenceParams.checkpoint, + activeGgufVariant, + activeModelIsLora, + activeModelIsGguf, + handleCheckpointChange, + ], + ); const handleEject = useCallback(() => { void (async () => { if (await ejectModel()) { @@ -2446,12 +2526,27 @@ export function ChatPage({ const state = useChatRuntimeStore.getState(); const targetLora = pickBestLoraForBase(state.loras, handoff.baseModel); + const selectWithConfig = async ( + selection: Pick, + ) => { + const previousConfig = currentRuntimePerModelConfig({ + includeMaxSeqLength: true, + }); + const hasAppliedConfig = applyModelLoadConfigToRuntime( + rememberedConfigFor(selection), + ); + await selectModelRef.current({ + ...selection, + ...(hasAppliedConfig ? { keepSpeculative: true } : {}), + previousConfig, + }); + }; if (targetLora) { console.info("[chat-handoff] loading lora", { id: targetLora.id, baseModel: targetLora.baseModel, }); - await selectModelRef.current({ id: targetLora.id, isLora: true }); + await selectWithConfig({ id: targetLora.id, isLora: true }); if (canceled) return; useChatRuntimeStore.getState().setActiveThreadId(null); useChatRuntimeStore.getState().setContextUsage(null); @@ -2468,10 +2563,7 @@ export function ChatPage({ console.info("[chat-handoff] no lora match, loading base", { id: handoff.baseModel, }); - await selectModelRef.current({ - id: handoff.baseModel, - isLora: false, - }); + await selectWithConfig({ id: handoff.baseModel, isLora: false }); if (canceled) return; } else { console.warn("[chat-handoff] no lora/base match found", { @@ -2491,7 +2583,7 @@ export function ChatPage({ return () => { canceled = true; }; - }, [active, navigate]); + }, [active, navigate, rememberedConfigFor]); const tourSteps = useMemo( () => @@ -2580,6 +2672,8 @@ export function ChatPage({ externalModels={externalModels} value={inferenceParams.checkpoint} activeGgufVariant={activeGgufVariant} + activeModelConfig={activeModelConfig} + activeGgufContextLength={ggufContextLength} onValueChange={handleCheckpointChange} onEject={handleEject} onFoldersChange={refreshLocalModels} @@ -2633,7 +2727,12 @@ export function ChatPage({ stageOrLoad(selection)} + onLoad={() => + loadNativeModelIntent( + pendingNativeModelIntent, + "Loading selected local GGUF model.", + ) + } /> ) : null} {loadingModel && loadToastDismissed ? ( @@ -2790,13 +2889,22 @@ export function ChatPage({ open={active && settingsOpen} onOpenChange={(open) => { setSettingsOpen(open); - // Closing the sheet abandons a staged (not-yet-loaded) pick: cancel its - // download and revert the staged knobs so nothing lingers as a dirty - // edit (or a background download) on the loaded model. - if (!open) abandonStaged(); }} params={inferenceParams} onParamsChange={setInferenceParams} + modelConfig={ + view.mode !== "compare" && activeModelConfig && !modelLoading ? ( + + ) : null + } isExternalModel={isExternalModel} providerCapabilities={activeProviderCapabilities} activeExternalProvider={activeExternalProvider} @@ -2808,67 +2916,6 @@ export function ChatPage({ ); }} externalProviderType={activeExternalProviderType} - loadingModel={loadingModel} - onReloadModel={() => { - const state = useChatRuntimeStore.getState(); - if (state.params.checkpoint) { - selectModel({ - id: state.params.checkpoint, - ggufVariant: state.activeGgufVariant ?? undefined, - // A native (drag-drop / picked) GGUF's checkpoint is only a display - // label, so the reload needs its path token to re-mint a lease -- - // else applying the now-exposed GPU/context controls can't resolve - // the file. Null for non-native loads, which reload by id as before. - nativePathToken: state.activeNativePathToken ?? undefined, - forceReload: true, - isDownloaded: true, - loadingDescription: "Reloading with updated chat template.", - }); - } - }} - onLoadPendingModel={() => { - const pending = useChatRuntimeStore.getState().pendingSelection; - if (!pending) return; - const keyAtLoad = chatContextKey; - // forceReload: the staged model isn't loaded yet, so bypass the - // same-checkpoint dedupe. keepSpeculative: honor the speculative mode - // set on the sidebar. - void selectModel({ - ...pending, - forceReload: true, - keepSpeculative: true, - throwOnError: true, - }).catch(() => { - // Recoverable failure (expired token, gated repo, OOM…): the pick is - // cleared only on success, so it normally stays staged with edited - // knobs intact — nothing to restore. - const store = useChatRuntimeStore.getState(); - // Still staged (this pick, or a newer one queued meanwhile): leave it. - if (store.pendingSelection) return; - // Cleared mid-load (sheet closed / switched chats). Re-stage only if - // the staged-load is still wanted: same chat context, sheet still - // open, page still mounted. - const stillWanted = - mountedRef.current && - store.settingsPanelOpen && - chatContextKeyRef.current === keyAtLoad; - if (stillWanted) { - store.setPendingSelection(pending); - } else { - // Abandoned (closed the sheet / switched chats / left chat): drop - // the orphaned staged knob edits so they don't linger as dirty - // settings over the loaded model. - store.resetModelSettingsToLoaded(); - } - }); - }} - stagedDownloadFraction={stagedDownload.progress?.fraction ?? null} - onCancelStagedDownload={() => - stagedDownload.cancelDownload( - useChatRuntimeStore.getState().pendingSelection?.ggufVariant ?? - null, - ) - } /> diff --git a/studio/frontend/src/features/chat/chat-settings-sheet.tsx b/studio/frontend/src/features/chat/chat-settings-sheet.tsx index bd22cc4f55..d4f154882c 100644 --- a/studio/frontend/src/features/chat/chat-settings-sheet.tsx +++ b/studio/frontend/src/features/chat/chat-settings-sheet.tsx @@ -1,19 +1,7 @@ // SPDX-License-Identifier: AGPL-3.0-only // Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0 -import { - Alert, - AlertDescription, - AlertTitle, -} from "@/components/ui/alert"; import { Button } from "@/components/ui/button"; -import { Checkbox } from "@/components/ui/checkbox"; -import { - clearRememberedLoadSettings, - loadRememberedLoadSettings, - rememberedLoadSettingsKey, - saveRememberedLoadSettings, -} from "@/components/assistant-ui/model-selector/remembered-load-settings"; import { Dialog, DialogContent, @@ -29,7 +17,7 @@ import { DropdownMenuSeparator, DropdownMenuTrigger, } from "@/components/ui/dropdown-menu"; -import { Input } from "@/components/ui/input"; +import { InfoHint } from "@/components/ui/info-hint"; import { InputGroup, InputGroupAddon, @@ -50,27 +38,22 @@ import { SheetTitle, } from "@/components/ui/sheet"; import { Slider } from "@/components/ui/slider"; -import { Spinner } from "@/components/ui/spinner"; import { Switch } from "@/components/ui/switch"; import { Textarea } from "@/components/ui/textarea"; -import { InfoHint } from "@/components/ui/info-hint"; import { Tooltip, TooltipContent } from "@/components/ui/tooltip"; -import { useGpuDevices } from "@/hooks/use-gpu-info"; -import { useIsMobile } from "@/hooks/use-mobile"; +import { NumericValueInput, snapToStep } from "@/features/model-picker"; +import { RetrievalSettingsSection } from "@/features/rag"; import { useLlamaUpdateCheck } from "@/hooks/use-llama-update-check"; -import { cn } from "@/lib/utils"; -import { - ArrowTurnBackwardIcon, - Edit03Icon, - LayoutAlignRightIcon, -} from "@hugeicons/core-free-icons"; +import { useIsMobile } from "@/hooks/use-mobile"; import { ChevronDownStandardIcon } from "@/lib/chevron-icons"; +import { toast } from "@/lib/toast"; +import { cn } from "@/lib/utils"; +import { Edit03Icon, LayoutAlignRightIcon } from "@hugeicons/core-free-icons"; import { HugeiconsIcon } from "@hugeicons/react"; import { Braces, ChevronDown, ExternalLink } from "lucide-react"; import { Tooltip as TooltipPrimitive } from "radix-ui"; import { Fragment, type ReactNode } from "react"; import { useCallback, useEffect, useMemo, useRef, useState } from "react"; -import { toast } from "@/lib/toast"; import { OpenAICodeExecSection } from "./components/openai-code-exec-section"; import { PermissionModeDropdown } from "./permission-mode-select"; import { resyncInferenceStatusAfterServerModelChange } from "./hooks/use-chat-model-runtime"; @@ -78,8 +61,8 @@ import { type ExternalProviderConfig, getExternalProviderApiKey, parseExternalModelId, - supportsProviderPromptCaching, supportsProviderPromptCacheTtl, + supportsProviderPromptCaching, } from "./external-providers"; import { BUILTIN_PRESETS, @@ -99,15 +82,7 @@ import { providerSupportsBuiltinCodeExecution, providerSupportsFastMode, } from "./provider-capabilities"; -import { - GPU_LAYERS_AUTO, - distributeByWeight, - isPendingGguf, - pendingSelectionMatches, - rebalanceSplit, - useChatRuntimeStore, -} from "./stores/chat-runtime-store"; -import { RetrievalSettingsSection } from "@/features/rag/components/retrieval-settings-section"; +import { useChatRuntimeStore } from "./stores/chat-runtime-store"; import type { InferenceParams } from "./types/runtime"; export { defaultInferenceParams, type Preset } from "./presets/preset-policy"; @@ -130,7 +105,7 @@ function getPromptVariablesError(raw: string): string | null { return null; } } catch { - return "Use valid JSON, for example { \"env\": \"staging\" }."; + return 'Use valid JSON, for example { "env": "staging" }.'; } return "Variables must be a JSON object."; } @@ -139,112 +114,7 @@ function hasPromptVariableSyntax(prompt: string): boolean { return PROMPT_VARIABLE_PATTERN.test(prompt); } -/** - * Editable numeric value display, shared by every slider value and the Context - * Length input. An that looks like text (shows `displayValue ?? value`, - * so "Off"/"Max" labels render) until focus, when it swaps to the raw number, - * selects it, and accepts free text. Commits on blur/Enter, reverts on Escape. - * Clamping happens on commit so typing intermediate values isn't fought. - */ -function snapToStep( - value: number, - step: number, - min?: number, - max?: number, -): number { - const lo = min ?? Number.NEGATIVE_INFINITY; - const hi = max ?? Number.POSITIVE_INFINITY; - const clamped = Math.min(Math.max(value, lo), hi); - const stepStr = String(step); - const decimals = stepStr.includes(".") ? stepStr.split(".")[1].length : 0; - const base = Number.isFinite(lo) ? lo : 0; - const snapped = base + Math.round((clamped - base) / step) * step; - const reclamped = Math.min(Math.max(snapped, lo), hi); - return Number(reclamped.toFixed(decimals)); -} - -function NumericValueInput({ - value, - min, - max, - step, - onChange, - displayValue, - className, - ariaLabel, - size: sizeAttr, - disabled = false, -}: { - value: number; - min?: number; - max?: number; - step: number; - onChange: (v: number) => void; - displayValue?: string; - className?: string; - ariaLabel?: string; - size?: number; - disabled?: boolean; -}) { - const [focused, setFocused] = useState(false); - const [draft, setDraft] = useState(""); - const cancelBlurCommitRef = useRef(false); - - const commit = (raw: string) => { - const parsed = Number.parseFloat(raw); - if (!Number.isFinite(parsed)) { - return; - } - const final = snapToStep(parsed, step, min, max); - if (final !== value) { - onChange(final); - } - }; - - const displayed = focused ? draft : (displayValue ?? String(value)); - - return ( - { - cancelBlurCommitRef.current = false; - setDraft(String(value)); - setFocused(true); - // Defer select() so it runs after the value swap above. - const target = e.currentTarget; - requestAnimationFrame(() => target.select()); - }} - onBlur={() => { - if (cancelBlurCommitRef.current) { - cancelBlurCommitRef.current = false; - } else { - commit(draft); - } - setFocused(false); - }} - onChange={(e) => setDraft(e.target.value)} - onKeyDown={(e) => { - if (e.key === "Enter") { - e.currentTarget.blur(); - } else if (e.key === "Escape") { - cancelBlurCommitRef.current = true; - setDraft(String(value)); - e.currentTarget.blur(); - } - }} - className={cn("panel-number-input", className)} - /> - ); -} - -function ParamSlider({ +export function ParamSlider({ label, value, min, @@ -285,6 +155,7 @@ function ParamSlider({ displayValue={displayValue} ariaLabel={label} size={valueSize ?? 4} + className="panel-number-input" disabled={disabled} /> @@ -385,8 +256,7 @@ function CollapsibleSection({ return (
{labelHref ? ( @@ -458,6 +328,7 @@ interface ChatSettingsPanelProps { onOpenChange?: (open: boolean) => void; params: InferenceParams; onParamsChange: (params: InferenceParams) => void; + modelConfig?: ReactNode; isExternalModel?: boolean; /** * Sampling-param capabilities for the active external provider, or `null` for @@ -472,21 +343,6 @@ interface ChatSettingsPanelProps { * Max Tokens floor in the slider. */ externalProviderType?: string | null; - onReloadModel?: () => void; - /** The in-flight load (id + GGUF variant + native path token), or null when - * idle. Used to show a loading state for the staged pick only — not for an - * unrelated load or a cancel's background unload. */ - loadingModel?: { - id: string; - ggufVariant?: string | null; - nativePathToken?: string | null; - } | null; - /** Loads the staged `pendingSelection` (deferred "Load on selection" flow). */ - onLoadPendingModel?: () => void; - /** Download progress (0–1) for a staged GGUF being fetched, or null when idle. */ - stagedDownloadFraction?: number | null; - /** Cancels the in-flight staged download (paired with abandoning the stage). */ - onCancelStagedDownload?: () => void; } export function ChatSettingsPanel({ @@ -494,16 +350,12 @@ export function ChatSettingsPanel({ onOpenChange, params, onParamsChange, + modelConfig = null, isExternalModel = false, providerCapabilities = null, activeExternalProvider = null, onExternalProviderChange, externalProviderType = null, - onReloadModel, - loadingModel = null, - onLoadPendingModel, - stagedDownloadFraction, - onCancelStagedDownload, }: ChatSettingsPanelProps) { // Local models show every knob; providerCapabilities is only consulted when // isExternalModel. Unknown providers fall back to the OpenAI-compat shape via @@ -518,64 +370,23 @@ export function ChatSettingsPanel({ const showPresencePenalty = !isExternalModel || Boolean(providerCapabilities?.presencePenalty); const isMobile = useIsMobile(); - const pendingSelection = useChatRuntimeStore((s) => s.pendingSelection); - // "Loading" only when the in-flight load IS this staged pick (full id + GGUF - // variant + native token match), not an unrelated load or a cancel's - // background unload. The variant matters: a different quant of the same repo - // staged mid-load must not read as this one loading. - const stagedLoading = - loadingModel != null && - pendingSelectionMatches(pendingSelection, { - id: loadingModel.id, - ggufVariant: loadingModel.ggufVariant, - nativePathToken: loadingModel.nativePathToken, - }); - // Load settings are snapshotted at click time; lock them while loading. - const modelControlsDisabled = stagedLoading; - const abandonStagedModel = useChatRuntimeStore((s) => s.abandonStagedModel); - const resetModelSettingsToLoaded = useChatRuntimeStore( - (s) => s.resetModelSettingsToLoaded, + const isLoadedGguf = useChatRuntimeStore((s) => s.activeGgufVariant) != null; + const currentCheckpoint = params.checkpoint; + const ggufContextLength = useChatRuntimeStore((s) => s.ggufContextLength); + // Direct-file / custom-folder GGUFs load without a variant label but still + // report a GGUF context, so detect them via the context and the checkpoint + // suffix too (mirrors the chat page's activeModelIsGguf). Otherwise Max Tokens + // would fall back to params.maxSeqLength instead of the loaded GGUF context. + const isGguf = + isLoadedGguf || + ggufContextLength != null || + (currentCheckpoint?.toLowerCase().endsWith(".gguf") ?? false); + const ggufMaxContextLength = useChatRuntimeStore( + (s) => s.ggufMaxContextLength, ); - // A staged GGUF pick (deferred load) shows the GGUF load knobs so they can be - // set before the single load. - const pendingIsGguf = isPendingGguf(pendingSelection); - // Short, human-readable name for the staged pick (HF ids carry an org prefix; - // native picks are already a display label). Drives the "staged, not loaded" - // callout so it's obvious the selection hasn't loaded yet. - const stagedLabel = (() => { - const id = pendingSelection?.id ?? ""; - const slash = id.lastIndexOf("/"); - const base = slash >= 0 ? id.slice(slash + 1) : id; - return base || id; - })(); - const activeNativePathToken = useChatRuntimeStore( - (s) => s.activeNativePathToken, - ); - const loadedGgufContextLength = useChatRuntimeStore((s) => s.ggufContextLength); - // A GGUF loaded from a native path / direct .gguf has no HF variant, so key - // off the same signal the status hydration uses -- variant OR native token OR - // a GGUF context -- else the GPU Memory controls hide for a loaded local GGUF. - const isLoadedGguf = - useChatRuntimeStore((s) => s.activeGgufVariant) != null || - activeNativePathToken != null || - loadedGgufContextLength != null; - // While a pick is staged the sheet configures *that* model, so its GGUF-ness - // (not the currently loaded model's) decides whether the GGUF-only controls - // show. Otherwise a staged non-GGUF Hub repo would inherit the loaded GGUF's - // context/KV/speculative controls. - const isGguf = pendingSelection != null ? pendingIsGguf : isLoadedGguf; - // The Model section (and Load button) shows for any staged pick, even when the - // currently active model is external. - const hasModelContent = - pendingSelection != null || - (!isExternalModel && (isGguf || Boolean(params.checkpoint))); + const customContextLength = useChatRuntimeStore((s) => s.customContextLength); const speculativeType = useChatRuntimeStore((s) => s.speculativeType); - const setSpeculativeType = useChatRuntimeStore((s) => s.setSpeculativeType); - const loadedSpeculativeType = useChatRuntimeStore( - (s) => s.loadedSpeculativeType, - ); const specFallbackReason = useChatRuntimeStore((s) => s.specFallbackReason); - // Only binary fallback states are solved by a newer prebuilt. const mtpUpdatable = specFallbackReason === "binary_no_mtp" || specFallbackReason === "binary_outdated"; @@ -597,65 +408,27 @@ export function ChatSettingsPanel({ `llama.cpp updated to ${result.tag ?? "the latest build"}.${reloadHint}`, ); } else { - toast.error(`llama.cpp update failed: ${result.error ?? "unknown error"}`); + toast.error( + `llama.cpp update failed: ${result.error ?? "unknown error"}`, + ); } }, [applyLlamaUpdate]); - const specDraftNMax = useChatRuntimeStore((s) => s.specDraftNMax); - const setSpecDraftNMax = useChatRuntimeStore((s) => s.setSpecDraftNMax); - const loadedSpecDraftNMax = useChatRuntimeStore( - (s) => s.loadedSpecDraftNMax, - ); - const currentCheckpoint = params.checkpoint; - const ggufContextLength = useChatRuntimeStore((s) => s.ggufContextLength); - const ggufMaxContextLength = useChatRuntimeStore( - (s) => s.ggufMaxContextLength, - ); - const ggufNativeContextLength = useChatRuntimeStore( - (s) => s.ggufNativeContextLength, - ); - const kvCacheDtype = useChatRuntimeStore((s) => s.kvCacheDtype); - const setKvCacheDtype = useChatRuntimeStore((s) => s.setKvCacheDtype); - const applyRememberedLoadSettings = useChatRuntimeStore( - (s) => s.applyRememberedLoadSettings, - ); - const loadedKvCacheDtype = useChatRuntimeStore((s) => s.loadedKvCacheDtype); - const tensorParallel = useChatRuntimeStore((s) => s.tensorParallel); - const setTensorParallel = useChatRuntimeStore((s) => s.setTensorParallel); - const loadedTensorParallel = useChatRuntimeStore( - (s) => s.loadedTensorParallel, - ); - const gpuMemoryMode = useChatRuntimeStore((s) => s.gpuMemoryMode); - const setGpuMemoryMode = useChatRuntimeStore((s) => s.setGpuMemoryMode); - const loadedGpuMemoryMode = useChatRuntimeStore((s) => s.loadedGpuMemoryMode); - const loadedIsDiffusion = useChatRuntimeStore((s) => s.loadedIsDiffusion); - const gpuLayers = useChatRuntimeStore((s) => s.gpuLayers); - const setGpuLayers = useChatRuntimeStore((s) => s.setGpuLayers); - const loadedGpuLayers = useChatRuntimeStore((s) => s.loadedGpuLayers); - const nCpuMoe = useChatRuntimeStore((s) => s.nCpuMoe); - const setNCpuMoe = useChatRuntimeStore((s) => s.setNCpuMoe); - const loadedNCpuMoe = useChatRuntimeStore((s) => s.loadedNCpuMoe); - const splitRatio = useChatRuntimeStore((s) => s.splitRatio); - const setSplitRatio = useChatRuntimeStore((s) => s.setSplitRatio); - const loadedSplitRatio = useChatRuntimeStore((s) => s.loadedSplitRatio); - const ggufLayerCount = useChatRuntimeStore((s) => s.ggufLayerCount); - const moeLayerCount = useChatRuntimeStore((s) => s.moeLayerCount); - const selectedGpuIds = useChatRuntimeStore((s) => s.selectedGpuIds); - const setSelectedGpuIds = useChatRuntimeStore((s) => s.setSelectedGpuIds); - const loadedGpuIds = useChatRuntimeStore((s) => s.loadedGpuIds); - const gpuDevices = useGpuDevices(); - const chatTemplateOverride = useChatRuntimeStore( - (s) => s.chatTemplateOverride, - ); - const loadedChatTemplateOverride = useChatRuntimeStore( - (s) => s.loadedChatTemplateOverride, - ); - const customContextLength = useChatRuntimeStore((s) => s.customContextLength); - const loadedCustomContextLength = useChatRuntimeStore( - (s) => s.loadedCustomContextLength, - ); - const setCustomContextLength = useChatRuntimeStore( - (s) => s.setCustomContextLength, - ); + const loadedEffectiveContext = customContextLength ?? ggufContextLength; + const showSpecFallback = + !isExternalModel && + isGguf && + specFallbackReason != null && + (speculativeType === "auto" || + speculativeType === "mtp" || + speculativeType === "mtp+ngram"); + const showContextVramWarning = + !isExternalModel && + isGguf && + ggufMaxContextLength != null && + loadedEffectiveContext != null && + loadedEffectiveContext > ggufMaxContextLength; + const showLoadedDiagnostics = showSpecFallback || showContextVramWarning; + const hasModelContent = showLoadedDiagnostics; const setActivePresetSource = useChatRuntimeStore( (s) => s.setActivePresetSource, ); @@ -666,170 +439,7 @@ export function ChatSettingsPanel({ const setActivePreset = useChatRuntimeStore((s) => s.setActivePreset); const settingsHydrated = useChatRuntimeStore((s) => s.settingsHydrated); - // A staged (not-yet-loaded) GGUF carries its own header context length on - // pendingSelection, so the slider can use the staged model's real ceiling - // without reading the loaded model's `ggufContextLength`. - const stagedContextLength = pendingSelection?.contextLength ?? null; - // "Remember settings next time" tick for a staged model. Seeds the store from - // the saved per-model settings on stage, so the sheet opens with what was used - // last time; the tick reflects whether a saved entry exists. - const [remember, setRemember] = useState(false); - // Keyed per quant: a different variant of the same repo has its own settings. - const pendingKey = pendingSelection - ? rememberedLoadSettingsKey(pendingSelection) - : null; - useEffect(() => { - if (!pendingKey) return; - // GGUF-only, like the stageOrLoad / Hub restore paths: every remembered - // field is a llama.cpp knob, so a non-GGUF pick has nothing to restore -- - // and applying its blob would clobber the standing gpuMemoryMode with a - // stale snapshot (the save on Load below is gated the same way). - const saved = pendingIsGguf ? loadRememberedLoadSettings(pendingKey) : null; - setRemember(saved != null); - if (saved) applyRememberedLoadSettings(saved); - }, [pendingKey, pendingIsGguf, applyRememberedLoadSettings]); - // While staging, the sheet reflects the STAGED model, so its header context - // takes precedence over the loaded model's (which may differ or be larger). - const baseContext = pendingIsGguf ? stagedContextLength : ggufContextLength; - const baseNativeContext = pendingIsGguf - ? stagedContextLength - : ggufNativeContextLength; - // Context controls render once we actually have a ceiling: for a staged GGUF, - // once its header metadata arrives (post-download); otherwise post-load. - const showContextControl = pendingIsGguf - ? stagedContextLength != null - : isLoadedGguf; - const stagedDownloading = - stagedDownloadFraction != null && stagedDownloadFraction < 1; - const ctxDisplayValue = customContextLength ?? baseContext ?? ""; - const ctxMaxValue = baseNativeContext ?? baseContext ?? null; - const kvDirty = kvCacheDtype !== loadedKvCacheDtype; - const ctxDirty = customContextLength !== loadedCustomContextLength; - const specDirty = speculativeType !== loadedSpeculativeType; - const specDraftDirty = specDraftNMax !== loadedSpecDraftNMax; - const tpDirty = tensorParallel !== (loadedTensorParallel ?? false); - // A loaded diffusion GGUF runs mode-agnostic (pins all layers on one GPU, - // ignores --fit/--gpu-layers), so the GPU Memory mode + manual controls don't - // apply -- hide them and don't let the preserved standing mode read as dirty. - // The GPU picker still applies (diffusion pins the chosen device). A staged pick - // keeps the controls (a pending pick's diffusion-ness isn't known until load). - const gpuModeApplies = - isGguf && (pendingSelection != null || !loadedIsDiffusion); - const gpuDirty = - gpuModeApplies && gpuMemoryMode !== (loadedGpuMemoryMode ?? "auto"); - const isManual = gpuModeApplies && gpuMemoryMode === "manual"; - // Manual with the GPU Layers slider at "Auto" (leftmost): --fit owns the whole - // layout, so the offload knobs (MoE, split, TP) don't apply. - const autoLayers = isManual && gpuLayers < 0; - // GPUs actually in use: the picked subset, or all visible when none picked. - const gpusInUse = selectedGpuIds ?? gpuDevices.map((d) => d.index); - // The picker must keep one GPU selected. - const singleGpuInUse = gpusInUse.length <= 1; - // TP needs at least two GPUs because tensor split is a no-op on one and may - // abort. Auto layers hides TP because --fit aborts under --split-mode tensor. - const tpDisabled = singleGpuInUse; - // Manual gpu-layers ceiling = model layer count + 1 (else a safe fallback): - // llama.cpp counts the output layer as one more offloadable layer past the - // repeating blocks ("offloaded 33/33" needs -ngl 33 on a 32-block model), so - // the slider max must reach it or full offload is unreachable. While staging, - // use the staged model's layer count (read from its header). - const stagedLayerCount = pendingSelection?.layerCount ?? null; - const modelLayerCount = pendingIsGguf ? stagedLayerCount : ggufLayerCount; - const gpuLayersMax = modelLayerCount != null ? modelLayerCount + 1 : 256; - // MoE-offload slider: shown only for MoE models, capped at their MoE-layer - // count. While staging, use the staged model's count (read from its header); - // otherwise the loaded model's. - const stagedMoeLayerCount = pendingSelection?.moeLayerCount ?? null; - const moeLayersMax = pendingIsGguf - ? (stagedMoeLayerCount ?? 0) - : (moeLayerCount ?? 0); - const showMoeSlider = isManual && !autoLayers && moeLayersMax > 0; - // gpuLayers always counts; MoE only with an explicit layer count (see above). - const manualDirty = - isManual && - (gpuLayers !== loadedGpuLayers || - (!autoLayers && nCpuMoe !== (loadedNCpuMoe ?? 0))); - // GPU picker: only meaningful on multi-GPU, and only when the reported - // indices are physical (relative ordinals from a parent CUDA_VISIBLE_DEVICES - // mask can't be mapped back to pin a device). null = use all (auto). - const showGpuPicker = - isGguf && - gpuDevices.length > 1 && - gpuDevices.every((d) => d.physicalIndex); - const isGpuChecked = (index: number) => - selectedGpuIds === null || selectedGpuIds.includes(index); - const toggleGpu = (index: number) => { - const all = gpuDevices.map((d) => d.index); - const current = selectedGpuIds ?? all; - const next = current.includes(index) - ? current.filter((i) => i !== index) - : [...current, index].sort((a, b) => a - b); - if (next.length === 0) return; // keep at least one GPU selected - setSelectedGpuIds(next.length === all.length ? null : next); - // The per-GPU split is positional, so any change to the set of GPUs in use - // invalidates it: drop it (the sliders fall back to the VRAM-weighted - // default). TP needs 2+ GPUs, so disable it when only one remains. - setSplitRatio(null); - if (next.length <= 1) { - setTensorParallel(false); - } - }; - const gpuIdsKey = (ids: number[] | null) => (ids === null ? "auto" : ids.join(",")); - const gpuIdsDirty = gpuIdsKey(selectedGpuIds) !== gpuIdsKey(loadedGpuIds); - // Per-GPU layer split (--tensor-split): manual + 2+ GPUs in use. One slider - // per GPU, each a layer count; together they sum to the GPU Layers total. - const showSplitRatio = - isManual && !autoLayers && showGpuPicker && gpusInUse.length > 1; - // The total the per-GPU counts sum to (the GPU Layers slider value); 0 under - // Auto, where the split is hidden. The devices behind the GPUs in use, for - // labels + the VRAM-weighted default. - const splitTotal = Math.max(0, Math.min(gpuLayers, gpuLayersMax)); - const gpusInUseDevices = gpusInUse.map( - (i) => gpuDevices.find((d) => d.index === i) ?? null, - ); - // Displayed per-GPU counts. splitRatio is a stable reference balance (only a - // slider edit changes it), rescaled to the current total; deriving rather than - // mutating it on GPU Layers changes keeps the balance intact when the total - // passes through low values or Auto. No saved split: free-VRAM-weighted default - // (llama.cpp's unset default splits by free VRAM, so the first edit starts from - // the default's placement, not a total-VRAM ratio that can land layers on a - // busy GPU). A genuine 0 (a full GPU) is a real weight, not missing data: the - // probe's no-data case degrades to the total server-side, and an all-zero list - // falls back to an even split in distributeByWeight. Not yet sent. - const splitCounts = - splitRatio && splitRatio.length === gpusInUse.length - ? distributeByWeight(splitTotal, splitRatio) - : distributeByWeight( - splitTotal, - gpusInUseDevices.map((d) => d?.memoryFreeGb ?? d?.memoryTotalGb ?? 1), - ); - const setSplitCount = (k: number, v: number) => - setSplitRatio(rebalanceSplit(splitTotal, splitCounts, k, v)); - const splitRatioDirty = - isManual && - !autoLayers && - JSON.stringify(splitRatio ?? null) !== JSON.stringify(loadedSplitRatio ?? null); - // Auto-fit context (Manual + Auto layers): <= 0 means "Auto" (--fit sizes it); - // a positive value pins it. Surface the length --fit chose once it's loaded. - const fitCtxAuto = autoLayers && (customContextLength ?? 0) <= 0; - const loadedAutoLayers = - loadedGpuMemoryMode === "manual" && (loadedGpuLayers ?? GPU_LAYERS_AUTO) < 0; - const fitResolvedCtx = - fitCtxAuto && loadedAutoLayers ? ggufContextLength : null; - // A saved chat-template override is a reload-time setting too, so surface - // Apply for a template-only edit (otherwise it could never be applied). - const templateDirty = chatTemplateOverride !== loadedChatTemplateOverride; - const modelSettingsDirty = - kvDirty || - ctxDirty || - specDirty || - specDraftDirty || - tpDirty || - gpuDirty || - manualDirty || - gpuIdsDirty || - splitRatioDirty || - templateDirty; + const baseContext = ggufContextLength; const [presetNameInput, setPresetNameInput] = useState(activePreset); const [systemPromptEditorOpen, setSystemPromptEditorOpen] = useState(false); const [systemPromptDraft, setSystemPromptDraft] = useState(""); @@ -855,8 +465,7 @@ export function ChatSettingsPanel({ BUILTIN_PRESETS.find((preset) => preset.name === activePreset) ?? null, [activePreset], ); - const hasUnsavedPresetChanges = useMemo( - () => { + const hasUnsavedPresetChanges = useMemo(() => { if (activePresetDefinition == null) { return false; } @@ -864,9 +473,7 @@ export function ChatSettingsPanel({ return activePresetSource === "modified"; } return !isSamePresetConfig(activePresetDefinition.params, params); - }, - [activePresetDefinition, activePresetSource, params], - ); + }, [activePresetDefinition, activePresetSource, params]); const presetSaveState = useMemo( () => getPresetSaveState({ @@ -895,6 +502,14 @@ export function ChatSettingsPanel({ const externalSelection = currentCheckpoint ? parseExternalModelId(currentCheckpoint) : null; + const maxTokensMax = isExternalModel + ? getExternalMaxOutputTokens( + externalProviderType, + externalSelection?.modelId, + ) + : isGguf && baseContext + ? baseContext + : Math.max(64, params.maxSeqLength); const showOpenAICodeExecSection = activeExternalProvider != null && providerSupportsBuiltinCodeExecution( @@ -977,8 +592,7 @@ export function ChatSettingsPanel({ return; } const fallbackPreset = - BUILTIN_PRESETS.find((preset) => preset.name === "Default") ?? - null; + BUILTIN_PRESETS.find((preset) => preset.name === "Default") ?? null; const next = customPresets.filter((preset) => preset.name !== name); setCustomPresets(next); if (activePreset === name) { @@ -1090,7 +704,7 @@ export function ChatSettingsPanel({ Run settings - + - )} -
- )} - {(speculativeType === "mtp" || - speculativeType === "mtp+ngram") && ( -
-
- - Draft Tokens - - - Max MTP draft tokens per step - (--spec-draft-n-max). Lower = less wasted - draft decode; higher = bigger speedup when - acceptance stays high. Default: 2 on GPU, - 3 on CPU/Mac. - -
- { - const raw = e.target.value; - if (raw === "") { - setSpecDraftNMax(null); - return; - } - const parsed = Number.parseInt(raw, 10); - if (Number.isFinite(parsed)) { - const clamped = Math.max(1, Math.min(16, parsed)); - setSpecDraftNMax(clamped); - } - }} - data-test-id="spec-draft-n-max-input" - aria-label="Speculative decoding draft tokens" - className="h-7 w-[88px] rounded-full border-border bg-background hover:bg-accent/50 dark:border-transparent dark:bg-white/[0.05] dark:hover:bg-white/[0.1] pl-3 py-0 text-[13px] font-medium text-nav-fg outline-none focus-visible:ring-0" - /> -
- )} - - )} - {gpuModeApplies && ( -
-
- - GPU Memory - - -
-
- Default: Unsloth - fits the model and context to your GPUs. -
-
- Manual: set GPU - Layers yourself. Leave it on Auto to let llama.cpp size - the context and offload overflow (including MoE experts) - to RAM. -
-
- -
-
- -
-
- )} - {isManual && ( - <> - - Layers to keep on the GPU (--gpu-layers); the rest run - on CPU. Auto lets llama.cpp size the split (and the - context) to fit VRAM. At the maximum, the whole model - is on the GPU. - - } - /> - {showMoeSlider && ( - - Keep the experts of this many MoE layers on the CPU - (--n-cpu-moe) to save VRAM. 0 = all experts on the - GPU; at the maximum, all are on the CPU. - - } - /> - )} - {showSplitRatio && ( -
-
- - Layers per GPU - - - Splits GPU Layers across GPUs (--tensor-split). - Without Tensor Parallelism each value is the layer - count on that GPU; with it, every GPU holds a slice - of each layer, so the values are only a ratio. - -
- {gpusInUseDevices.map((d, k) => ( - setSplitCount(k, v)} - valueSize={6} - disabled={modelControlsDisabled} - /> - ))} -
- )} - - )} - {showGpuPicker && ( -
-
- - GPUs - - - Which GPUs this model may use. Unchecked GPUs are hidden - from llama.cpp (CUDA_VISIBLE_DEVICES, or - HIP_VISIBLE_DEVICES on ROCm). Leave all checked to use - every GPU. At least one GPU must stay selected. - -
-
- {gpuDevices.map((d) => ( -
- - GPU {d.index}: {d.name} - {d.memoryTotalGb - ? ` · ${Math.round(d.memoryTotalGb)} GB` - : ""} - - toggleGpu(d.index)} - data-test-id={`gpu-pick-${d.index}`} - disabled={ - modelControlsDisabled || - (isGpuChecked(d.index) && singleGpuInUse) - } - /> -
- ))} -
-
- )} - {gpuModeApplies && !autoLayers && ( -
-
- - Tensor Parallelism - - - No effect on a single GPU. On multi-GPU setups, improves - tokens/sec during generation when using dense models. MoE - models don't benefit and can be much slower. - -
- -
- )} - - )} - {/* No persistent "enable custom code" toggle: it is consented per model - via the load-time review dialog. */} - {/* Apply/Reset belongs to the model-reload settings above (context - length, KV cache, speculative decoding). Render it here, before - the Chat Template row, so it never reads as attached to Chat - Template (which is edited via its own dialog). When a model is - staged (deferred load), Load/Cancel takes its place: there's - nothing loaded to "apply" against yet. */} - {pendingSelection ? ( -
- {stagedDownloading && ( -

- Downloading…{" "} - {Math.round((stagedDownloadFraction ?? 0) * 100)}% + : "" + }`}

- )} - {/* GGUF picks only: a non-GGUF pick shows none of the load - knobs the blob captures, so there is nothing to remember. */} - {pendingIsGguf && ( - - )} - {stagedLoading ? ( - // Mid-load: nothing to load or abandon until it settles, so disable. - - ) : ( -
+ {mtpUpdatable && llamaUpdateStatus?.update_available && ( - -
- )} -
- ) : modelSettingsDirty ? ( -
- - -
- ) : null} - {/* The template override is a load-time knob too (applied on the next - reload) and the in-flight load already snapshotted it, so lock its - editors like the sibling controls -- a mid-load save would be - silently clobbered by the load response despite its toast. */} - - - + )} + + )} + {showContextVramWarning && ( +

+ Context length exceeds the estimated VRAM capacity ( + {ggufMaxContextLength?.toLocaleString()} tokens). The + model may use system RAM. +

+ )} + + )}
- +
savePresetWithName(presetNameInput)} disabled={!(settingsHydrated && presetSaveState.canSubmit)} - variant={presetSaveState.isSaveReady ? "default" : "outline"} + variant={ + presetSaveState.isSaveReady ? "default" : "outline" + } size="sm" className={cn( "h-9 w-full rounded-full text-[13px] font-medium tracking-nav", @@ -1850,7 +912,8 @@ export function ChatSettingsPanel({ Prompt caching - Reuse compatible prompt prefixes for lower latency and cost. + Reuse compatible prompt prefixes for lower latency and + cost.
Anthropic exposes a 5 minute and a 1 hour ephemeral - cache pool. The 1 hour pool costs 2x base input on - write vs 1.25x for 5 minute, but reads stay 0.1x for - both, so a single read landing more than 5 minutes - after the write pays off the premium. + cache pool. The 1 hour pool costs 2x base input on write + vs 1.25x for 5 minute, but reads stay 0.1x for both, so + a single read landing more than 5 minutes after the + write pays off the premium.