Problem Description
Environment & Context
- Working Baseline: FastFlowLM v1.0.1 (serving by lemonade) using previously downloaded model weights
- Regressed Setup: FastFlowLM v1.0.2 (
.deb) freshly installed, using newly downloaded model weights/quantization (qwen3.6-moe:35b-a3b).
Summary
When benchmarking the newly updated model weights under FastFlowLM v1.0.2 against the earlier v1.0.1 setup, the new environment exhibits severe reasoning instability, infinite thinking loops, language drift (switching to German on Hungarian prompts), and token degeneration (///) under complex/constrained prompts.
Key Comparison
| Metric |
Previous (FLM v1.0.1 + Old Weights) |
New (FLM v1.0.2 + New Weights) |
| Total Benchmark Time |
~27 minutes (1,649s) |
~86 minutes (5,164s) |
| Total Tokens Generated |
22,521 tokens |
75,706 tokens |
| Generation Speed |
~13.7 t/s (Consistent) |
~14.2 – 17.6 t/s (Volatile) |
| Loop / Degeneration Rate |
0 / 5 (All completed cleanly) |
2 / 5 (Hit max token limits) |
| Language Adherence |
100% (Hungarian) |
Failed (Drifted to German in Q3) |
Detailed Symptoms
1. Infinite Reasoning Loops (<think> block failure)
On complex reasoning/math prompts, the model under v1.0.2 enters an endless loop inside the internal <think> block. It repeatedly recalculates and re-evaluates the same steps without reaching the </think> closing tag until it exhausts the maximum generation limit (32,640 tokens).
2. Output Degeneration under Constraints
On strict constraint-following tasks (e.g., creative writing without specific letters), the generation breaks down completely. After generating thousands of tokens in a loop, the output degrades into an endless repetition of forward slashes (///) until hitting the token cap.
3. Language Drift / Loss of System Context
On code optimization tasks, the model completely loses target language context during the <think> reasoning phase. Despite the user prompt being entirely in Hungarian, the final generated explanation and section headers unexpectedly output in German (e.g., "Python Implementierung", "Algorithmus-Erklärung").
4. Wasted Compute and Inflated Runtime
Even though the raw generation speed of the new engine was slightly higher (~17.5 t/s vs ~13.7 t/s), the total benchmark runtime exploded by +213% (~86 mins vs ~27 mins) due to tens of thousands of wasted tokens generated inside reasoning loops.
Specific Case Comparison
Expected Behavior
The model/engine under v1.0.2 should:
- Correctly terminate reasoning blocks (
</think>) without looping infinitely.
- Maintain system context and strictly adhere to the prompt's language (no cross-lingual drift).
- Respect strict decoding constraints without falling into character degeneration (
///).
Operating System
Ubuntu 26.04 LTS (Resolute Raccoon)
CPU
AMD Ryzen AI 7 350 w/ Radeon 860M
GPU
AMD Ryzen AI 7 350 w/ Radeon 860M
ROCm Version
7.14.0
ROCm Component
No response
Steps to Reproduce
No response
(Optional for Linux users) Output of /opt/rocm/bin/rocminfo --support
rocminfo --support output
Additional Information
No response
Problem Description
Environment & Context
.deb) freshly installed, using newly downloaded model weights/quantization (qwen3.6-moe:35b-a3b).Summary
When benchmarking the newly updated model weights under FastFlowLM v1.0.2 against the earlier v1.0.1 setup, the new environment exhibits severe reasoning instability, infinite thinking loops, language drift (switching to German on Hungarian prompts), and token degeneration (
///) under complex/constrained prompts.Key Comparison
Detailed Symptoms
1. Infinite Reasoning Loops (
<think>block failure)On complex reasoning/math prompts, the model under v1.0.2 enters an endless loop inside the internal
<think>block. It repeatedly recalculates and re-evaluates the same steps without reaching the</think>closing tag until it exhausts the maximum generation limit (32,640 tokens).2. Output Degeneration under Constraints
On strict constraint-following tasks (e.g., creative writing without specific letters), the generation breaks down completely. After generating thousands of tokens in a loop, the output degrades into an endless repetition of forward slashes (
///) until hitting the token cap.3. Language Drift / Loss of System Context
On code optimization tasks, the model completely loses target language context during the
<think>reasoning phase. Despite the user prompt being entirely in Hungarian, the final generated explanation and section headers unexpectedly output in German (e.g., "Python Implementierung", "Algorithmus-Erklärung").4. Wasted Compute and Inflated Runtime
Even though the raw generation speed of the new engine was slightly higher (~17.5 t/s vs ~13.7 t/s), the total benchmark runtime exploded by +213% (~86 mins vs ~27 mins) due to tens of thousands of wasted tokens generated inside reasoning loops.
Specific Case Comparison
Math/Probability Prompt:
Code Optimization Prompt:
Constraint Following Prompt:
///repetition.Expected Behavior
The model/engine under v1.0.2 should:
</think>) without looping infinitely.///).Operating System
Ubuntu 26.04 LTS (Resolute Raccoon)
CPU
AMD Ryzen AI 7 350 w/ Radeon 860M
GPU
AMD Ryzen AI 7 350 w/ Radeon 860M
ROCm Version
7.14.0
ROCm Component
No response
Steps to Reproduce
No response
(Optional for Linux users) Output of /opt/rocm/bin/rocminfo --support
rocminfo --support output
Additional Information
No response