f08aef1804
* Rebuild Studio branch on top of main * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix security and code quality issues for Studio PR #4237 - Validate models_dir query param against allowed directory roots to prevent path traversal in /api/models/local endpoint - Replace string startswith() with Path.is_relative_to() for frontend path traversal check in serve_frontend - Sanitize SSE error messages to not leak exception details to clients (4 locations in inference.py) - Bind port-discovery socket to 127.0.0.1 instead of all interfaces in llama_cpp backend - Import datasets_root and resolve_output_dir in embedding training function to fix NameError and use managed output directory - Remove stale .gitignore entries for package-lock.json and test directories so tests can be tracked in version control - Add venv-reexecution logic to ui CLI command matching the studio command behavior * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Move models_dir path validation before try/except block The HTTPException(403) was inside the try/except Exception handler, so it would be caught and re-raised as a 500. Moving the validation before the try block ensures the 403 is returned directly and also makes the control flow clearer for static analysis (path is validated before any filesystem operations). * Use os.path.realpath + startswith for models_dir validation CodeQL py/path-injection does not recognize Path.is_relative_to() as a sanitizer. Switched to os.path.realpath + str.startswith which is a recognized sanitizer pattern in CodeQL's taint analysis. The startswith check uses root_str + os.sep to prevent prefix collisions (e.g. /app/models_evil matching /app/models). * Never pass user input to Path constructor in models_dir validation CodeQL traces taint through Path(resolved) even after a startswith barrier guard. Fix: the user-supplied models_dir is only used as a string for comparison against allowed roots. The Path object passed to _scan_models_dir comes from the trusted allowed_roots list, not from user input. This fully breaks the taint chain. --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
132 lines
4 KiB
Python
132 lines
4 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""
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Pydantic schemas for Export API.
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"""
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from pydantic import BaseModel, Field
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from typing import List, Optional, Literal, Dict, Any
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class LoadCheckpointRequest(BaseModel):
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"""Request for loading a checkpoint into the export backend."""
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checkpoint_path: str = Field(..., description = "Path to the checkpoint directory")
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max_seq_length: int = Field(
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2048,
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ge = 128,
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le = 32768,
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description = "Maximum sequence length for loading the model",
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)
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load_in_4bit: bool = Field(
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True,
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description = "Whether to load the model in 4-bit quantization",
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)
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trust_remote_code: bool = Field(
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False,
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description = "Allow loading models with custom code. Only enable for checkpoints/base models you trust.",
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)
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class ExportStatusResponse(BaseModel):
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"""Current export backend status."""
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current_checkpoint: Optional[str] = Field(
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None,
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description = "Path to the currently loaded checkpoint, if any",
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)
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is_vision: bool = Field(
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False,
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description = "True if the loaded checkpoint is a vision model",
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)
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is_peft: bool = Field(
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False,
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description = "True if the loaded checkpoint is a PEFT (LoRA) model",
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)
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class ExportOperationResponse(BaseModel):
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"""Generic response for export operations."""
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success: bool = Field(..., description = "True if the operation succeeded")
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message: str = Field(..., description = "Human-readable status or error message")
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details: Optional[Dict[str, Any]] = Field(
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default = None,
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description = "Optional extra details about the operation",
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)
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class ExportCommonOptions(BaseModel):
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"""Common options for export operations that save locally and/or push to Hub."""
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save_directory: str = Field(
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...,
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description = "Local directory where the exported artifacts will be written",
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)
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push_to_hub: bool = Field(
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False,
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description = "If True, also push the exported model to the Hugging Face Hub",
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)
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repo_id: Optional[str] = Field(
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None,
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description = "Hugging Face Hub repository ID (username/model-name)",
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)
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hf_token: Optional[str] = Field(
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None,
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description = "Hugging Face access token used for Hub operations",
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)
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private: bool = Field(
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False,
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description = "If True, create a private repository on the Hub (where applicable)",
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)
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base_model_id: Optional[str] = Field(
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None,
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description = "HuggingFace model ID of the base model (for model card metadata)",
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)
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class ExportMergedModelRequest(ExportCommonOptions):
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"""Request for exporting a merged PEFT model."""
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format_type: Literal["16-bit (FP16)", "4-bit (FP4)"] = Field(
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"16-bit (FP16)",
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description = "Export precision / format for the merged model",
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)
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class ExportBaseModelRequest(ExportCommonOptions):
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"""Request for exporting a non-PEFT (base) model."""
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# Uses fields from ExportCommonOptions only
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class ExportGGUFRequest(BaseModel):
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"""Request for exporting the current model to GGUF format."""
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save_directory: str = Field(
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...,
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description = "Directory where GGUF files will be saved",
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)
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quantization_method: str = Field(
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"Q4_K_M",
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description = 'GGUF quantization method (e.g. "Q4_K_M")',
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)
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push_to_hub: bool = Field(
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False,
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description = "If True, also push GGUF artifacts to the Hugging Face Hub",
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)
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repo_id: Optional[str] = Field(
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None,
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description = "Hugging Face Hub repository ID for GGUF upload",
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)
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hf_token: Optional[str] = Field(
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None,
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description = "Hugging Face token for GGUF upload",
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)
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class ExportLoRAAdapterRequest(ExportCommonOptions):
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"""Request for exporting only the LoRA adapter (not merged)."""
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# Uses fields from ExportCommonOptions only
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