Dynamic Reasoning Collapse & Token Degradation on Gemma4 11.9B (Q6_K)
Hi there! Thanks for putting together this gemma4 11.9B fine-tune/quantization. The 262k context length capability on an 11B model is incredibly promising for local-first ecosystems.
I’ve been benchmarking its reasoning, coding, and agentic capabilities using a modified logic puzzle (a River Crossing variant with dynamic constraints). I wanted to share some specific feedback regarding a severe failure mode I encountered during the "thinking" phase, hoping it helps you refine the next iteration.
The Setup
Model: gemma4 (11.9B)
Quantization: Q6_K
Parameters: num_ctx: 131072, temperature: 1.0, top_p: 0.95, top_k: 64
Enabled Capabilities: completion, tools, thinking
The Failure Mode (Token Collapse & Context Hallucination)
The model showed a fantastic agentic instinct initially: instead of just guessing the answer in natural language, it decided to write a Python script (state-space search) to solve the problem logically.
However, under heavy reasoning constraints, the generation completely collapsed:
Syntax Degradation: The output rapidly devolved into corrupted, non-existent syntax (e.g., mixing operators like trip%4!=2==True: pass, if item=="gato"=>69, and nR<-["gato"]), leading to immediate interpreter crashes.
Environment Bleeding / Escape: Once the internal Python logic failed, the model broke its sandbox boundaries. It started blindly throwing hallucinated Windows/Bash terminal commands (ls G:\D\proyectos..., gping, etc.) trying to locate nonexistent directories on the host system, entering an infinite loop of terminal errors.
Actionable Suggestions for Improvement
Given that this is a 11.9B model running at temperature: 1.0, a few areas might need tuning to keep the "thinking" budget stable:
Temperature Sensitivity vs. Quantization: At temperature: 1, the Q6_K quantization seems to cause severe logit drifting during long chain-of-thought (CoT) tokens. Recommending a default temp of 0.7 for reasoning workloads, or implementing a decay factor for the thinking phase, might prevent the syntax from fracturing.
CoT Attention Anchoring: The syntax collapse suggests the model loses its structural anchors during deep thinking blocks. Fine-tuning with stricter syntax enforcement or adding negative logit biases for broken code patterns during the reasoning loop could stabilize code-generation under stress.
Agentic Boundary Training: The bleeding into blind bash/terminal commands shows the model lacks strong alignment regarding its environment limits. A few synthetic training passes enforcing a clean fallback state (e.g., outputting a clean {"error": "..."} JSON instead of guessing terminal paths) would make this an incredibly solid agent.
Looking forward to your next updates! This architecture has massive potential.