Users report that the Qwen3.6 27B 8K model occasionally stops processing after generating a tool call, especially when the user steps away. The issue can be resolved by manually pasting the tool call back into the prompt, allowing the model to resume execution. The tool call involves a bash function to find passing tests in a codebase.
media
r/LocalLLaMA
·
55d ago
·
open_models
Tool calling issue in open-source Qwen3.6 27B 8K
from English
Related articles
media
r/LocalLLaMA
·
2h ago
·
1 view
Qwen 3.8 Max release highlights planning, simplification, and experimental design
The full release of Qwen 3.8 Max confirms early impressions that the model is exceptionally fast, highly capable at planning, and skilled at identifying unnecessary complexity in problems.
media
r/LocalLLaMA
·
41d ago
Qwen3.6-27b-mtp-q8 creates A* pathfinding implementation via autonomous testing
The Qwen3.6-27b-mtp-q8 model successfully generated an A* pathfinding implementation for a Java-based test game using Claude Code locally. The process involved nearly 12 hours of iterative development where the model autonomously created and ran a testing suite.
media
r/LocalLLaMA
·
45d ago
·
1 view
Qwen3.6-27B with 3-Critic Harness Matches Frontier Quality
A user tested Qwen3.6-27B (8-bit) alongside GLM5.2 using a coding harness that employs three critics—code review, test review, and Playwright e2e—to validate output quality.
media
r/LocalLLaMA
·
51d ago
·
4 views
Qwen releases 35B-parameter MoE for agent environment simulation
Qwen has launched Qwen-AgentWorld-35B-A3B, a 35B-parameter MoE model with only about 3B active parameters per token. It is trained to simulate responses from MCP, terminal, software engineering, Android, web, and OS GUI environments by predicting next observations after agent actions, enabling efficient agent training and environment simulation without real tool execution.
arxiv
arXiv cs.AI
·
52d ago
·
1 view
CORTIS: Text-Only Adaptation of Spoken Language Models
CORTIS enables task-oriented voice agents to generate structured speech outputs by fine-tuning spoken language models using only text-form task supervision. It outperforms ASR-LLM cascades under acoustic degradation, especially in preserving high-level task semantics, without requiring paired speech-target annotations during training.