voidful/Qwen3.5-9B-gemini-3.1-opus-4.6-reasoning

voidful/Qwen3.5-9B-gemini-3.1-opus-4.6-reasoning is a 9B-scale model based on Qwen/Qwen3.5-9B.
This model is designed to improve reasoning-oriented multiple-choice performance while preserving strong general capability.

In our zero-shot evaluation, the model achieves the best overall aggregate performance among the following three models:

• Qwen/Qwen3.5-9B
• DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-INSTRUCT
• voidful/Qwen3.5-9B-gemini-3.1-opus-4.6-reasoning

The largest gains appear on ARC-Challenge, ARC-Easy, and BoolQ.
These results suggest that the model improves structured reasoning and calibrated answer selection.

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Model Summary

• Base model: Qwen/Qwen3.5-9B
• Model type: Causal language model
• Primary focus: Reasoning, multiple-choice QA, and general zero-shot evaluation
• Strengths: ARC, BoolQ, aggregate benchmark performance
• Trade-offs: Slightly weaker than some baselines on HellaSwag and OpenBookQA

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Evaluation Setup

We compare three models under the same zero-shot setting:

• 0-shot
• No few-shot examples
• Same benchmark suite
• Reported with standard error

Compared Models

1. Qwen/Qwen3.5-9B
2. DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-INSTRUCT
3. voidful/Qwen3.5-9B-gemini-3.1-opus-4.6-reasoning

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Main Results

Representative 7-task Average

We use acc_norm when available, and acc otherwise.

Macro Average over All 12 Reported Metrics

These results indicate that voidful/Qwen3.5-9B-gemini-3.1-opus-4.6-reasoning is the strongest overall model in this comparison.

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Benchmark Results

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Key Observations

Strengths

• The model achieves the best overall average score across the compared models.
• The model shows clear improvements on ARC-Challenge and ARC-Easy.
• The model strongly outperforms DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-INSTRUCT on BoolQ.
• The gains are especially visible on reasoning-oriented benchmarks.

Trade-offs

• The model is not the top model on every benchmark.
• The model is slightly behind DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-INSTRUCT on HellaSwag and OpenBookQA.
• The model is close to the baselines on PIQA and Winogrande.

Overall, the model improves the reasoning profile of the base model without uniformly dominating all commonsense tasks.

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Interpretation

The benchmark pattern suggests that this model improves:

• structured answer selection
• reasoning-oriented multiple-choice QA
• calibration on science and reading-style benchmarks

At the same time, the gains are smaller on tasks that rely more heavily on narrative continuation or broad commonsense completion priors.

This behavior is consistent with a model that is optimized more toward reasoning quality than pure completion fluency.

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Limitations

• The evaluation here is limited to a small set of common zero-shot benchmarks.
• Some benchmark differences are small and may fall within the reported standard error.
• The model should not be described as universally better on every task.
• Additional evaluations on instruction following, long-context reasoning, coding, multilingual performance, and open-ended generation are still needed.

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Conclusion

voidful/Qwen3.5-9B-gemini-3.1-opus-4.6-reasoning is a strong 9B reasoning-oriented model built on top of Qwen/Qwen3.5-9B.

In this comparison, it delivers:

• the best overall aggregate benchmark score
• the strongest ARC performance
• strong BoolQ performance
• competitive general capability on other zero-shot commonsense tasks

This makes it a good choice for users who care about reasoning-oriented zero-shot performance in a compact 9B model.

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Raw Results

Qwen/Qwen3.5-9B

DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-INSTRUCT

voidful/Qwen3.5-9B-gemini-3.1-opus-4.6-reasoning

👁 Built with Axolotl

Qwen3.5-9B-gemini-3.1-opus-4.6-reasoning

This model is a fine-tuned version of Qwen/Qwen3.5-9B on the voidful/gemini-3.1-opus-4.6-reasoning-merged dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 80
• gradient_accumulation_steps: 4
• total_train_batch_size: 320
• total_eval_batch_size: 80
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• training_steps: 6

Training results

Framework versions

• Transformers 5.3.0
• Pytorch 2.10.0+cu128
• Datasets 4.5.0
• Tokenizers 0.22.2

Base model

Qwen/Qwen3.5-9B-Base

Qwen/Qwen3.5-9B

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