Reddit Tracks llama.cpp PR #19765: Qwen3-Coder-Next Parser Fix Merged with Tool-Calling and Schema Updates
Original: fixed parser for Qwen3-Coder-Next View original →
What the Reddit post highlighted
The r/LocalLLaMA post titled fixed parser for Qwen3-Coder-Next linked directly to llama.cpp pull request #19765. At capture time, the thread had solid technical engagement (82 upvotes, 36 comments), with discussion centered on prompt-format reliability and parser behavior in local inference workflows.
The linked PR title is common : merge qwen3-coder and nemotron nano 3 parsers. It was opened on February 20, 2026, and merged the same day. According to the PR description, this change is a stop-gap until another larger parser update is merged.
What changed in PR #19765
- Replaces the existing Qwen3-Coder parsing route with a Nemotron Nano 3 PEG parsing variant already present in the codebase.
- Adds parallel tool-calling behavior.
- Fixes JSON schema support issues.
- References fixes for issues #19382, #19430, and #19304, and supersedes #19503 and #19753.
Code-level footprint
GitHub metadata reports 4 changed files, 154 additions, and 602 deletions across two commits. Modified files include common/chat-parser.cpp, common/chat.cpp, common/chat.h, and tests/test-chat.cpp. The deletion-heavy diff suggests consolidation and replacement of parser paths rather than incremental branching.
For local model operators, parser updates like this are high leverage: when chat template parsing drifts from model expectations, tool invocation and structured outputs can fail even if raw generation quality is fine. A narrow parser fix often restores end-to-end reliability without requiring model retraining.
Why it matters for local LLM stacks
Qwen3-Coder-Next has active community adoption, so parser correctness directly affects downstream developer tools, agent loops, and function-calling pipelines. The addition of parallel tool-calling and JSON schema compatibility is especially relevant for users building agentic coding workflows on top of llama.cpp.
This Reddit thread is a useful signal because it surfaced a concrete merged patch, not just a benchmark screenshot. Teams running local inference should treat parser and schema updates as operational dependencies, and regression-test tool-call traces after each runtime upgrade.
Sources: llama.cpp PR #19765, r/LocalLLaMA thread
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LocalLLaMA liked this because it was not another vague 'model feels worse' post. The thread isolated a concrete failure mode: nullable JSON Schema shapes were collapsing into empty type fields, and a small Jinja fix made Gemma 4's tool calling behave normally again.
A LocalLLaMA user built a 768GB RAM system using discontinued Intel Optane Persistent Memory from the secondhand market, running the 1-trillion-parameter Kimi K2.5 model locally at over 4 tokens per second.
A community user achieved 110 tokens/second running Qwen3.6 35B A3B on an RTX 4070 Super 12GB via ik_llama.cpp, a fork with superior CPU offload optimization that significantly outperforms upstream llama.cpp's Multi-Token Prediction implementation.