b8656
common : fix tool call type detection for nullable and enum schemas ( #21327 ) common : fix tool call type detection for nullable and enum schemas common, tests : fix grammar delegation for nullable/enum schemas and add tests Fix enum type inference to scan all enum values (not just index 0) so schemas like {"enum": [0, "celsius"]} correctly detect string type. Fix schema_delegates in peg-parser to handle nullable type arrays (["string", "null"]) and typeless enum schemas in raw mode, allowing the tagged parser to use raw text instead of JSON-formatted strings. Add test cases for Qwen3-Coder (TAG_WITH_TAGGED format): nullable string ["string", "null"] nullable string with null first ["null", "string"] nullable integer ["integer", "null"] enum without explicit type key macOS/iOS: macOS Appl
common : fix tool call type detection for nullable and enum schemas (#21327)
-
common : fix tool call type detection for nullable and enum schemas
-
common, tests : fix grammar delegation for nullable/enum schemas and add tests
Fix enum type inference to scan all enum values (not just index 0) so schemas like {"enum": [0, "celsius"]} correctly detect string type.
Fix schema_delegates in peg-parser to handle nullable type arrays (["string", "null"]) and typeless enum schemas in raw mode, allowing the tagged parser to use raw text instead of JSON-formatted strings.
Add test cases for Qwen3-Coder (TAG_WITH_TAGGED format):
-
nullable string ["string", "null"]
-
nullable string with null first ["null", "string"]
-
nullable integer ["integer", "null"]
-
enum without explicit type key
macOS/iOS:
-
macOS Apple Silicon (arm64)
-
macOS Intel (x64)
-
iOS XCFramework
Linux:
-
Ubuntu x64 (CPU)
-
Ubuntu arm64 (CPU)
-
Ubuntu s390x (CPU)
-
Ubuntu x64 (Vulkan)
-
Ubuntu arm64 (Vulkan)
-
Ubuntu x64 (ROCm 7.2)
-
Ubuntu x64 (OpenVINO)
Windows:
-
Windows x64 (CPU)
-
Windows arm64 (CPU)
-
Windows x64 (CUDA 12) - CUDA 12.4 DLLs
-
Windows x64 (CUDA 13) - CUDA 13.1 DLLs
-
Windows x64 (Vulkan)
-
Windows x64 (SYCL)
-
Windows x64 (HIP)
openEuler:
-
openEuler x86 (310p)
-
openEuler x86 (910b, ACL Graph)
-
openEuler aarch64 (310p)
-
openEuler aarch64 (910b, ACL Graph)
Sign in to highlight and annotate this article

Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Products

Powering Down Enterprises Tackle AI’s Soaring Energy Costs
Key Takeaways Enterprises are adopting a multi-faceted approach to manage AI’s growing energy consumption, focusing on both technical and operational efficiencies. Hardware innovations like specialized AI accelerators and software optimizations such as model pruning and quantization are crucial for reducing AI workload power demands. Strategic shifts towards cloud and edge computing, combined with AI-driven energy management systems, are enabling dynamic resource allocation and integration of renewable energy sources for sustainable AI. The Energy Imperative of Enterprise AI AI workloads could consume nearly half of all data center power by the end of 2025, forcing enterprises to confront a stark reality: their AI ambitions are driving unprecedented energy costs. From training complex mach




Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!