tomasmcm/sky-t1-coder-32b-flash

This is a merge of pre-trained language models created using mergekit.

I wanted to see if it would be possible to improve on FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview and CoderO1-DeepSeekR1-Coder-32B-Preview by using Sky-T1-32B-Flash as the reasoning model that is merged with Qwen2.5-Coder-32B-Instruct instead of DeepSeek-R1-Distill-Qwen-32B. The idea is to have a strong coder model that can reason but without very long reasoning chains (hence using the Flash model).

GGUF files available at mradermacher/sky-t1-coder-32b-flash-GGUF (thank you!)

LLM Leaderboard (15 Mar 2025)

Rank	Model	Average	IFEval	BBH	MATH	GPQA	MUSR	MMLU-PRO
22	Qwen/Qwen2.5-32B-Instruct	46.60%	83.46%	56.49%	62.54%	11.74%	13.50%	51.85%
39	tomasmcm/sky-t1-coder-32b-flash	44.87%	77.80%	55.47%	54.23%	15.77%	12.81%	53.13%
147	huihuiai/QwQ-32B-Coder-Fusion-9010	41.58%	57.78%	53.02%	53.17%	14.88%	19.52%	51.11%
276	Qwen/Qwen2.5-Coder-32B-Instruct	39.89%	72.65%	52.27%	49.55%	13.20%	13.72%	37.92%
717	Qwen/QwQ-32B-Preview	34.12%	40.35%	53.39%	44.94%	4.25%	9.81%	51.98%
2100	deepseek-ai/DeepSeek-R1-Distill-Qwen-32B	22.96%	41.86%	17.15%	17.07%	4.59%	16.14%	40.96%
3574	Qwen/QwQ-32B	12.21%	39.77%	2.87%	16.09%	1.34%	11.05%	2.18%

Merge Details

Merge Method

This model was merged using the SCE merge method using Qwen/Qwen2.5-Coder-32B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  # Pivot model
  - model: Qwen/Qwen2.5-Coder-32B
  # Target models
  - model: Qwen/Qwen2.5-Coder-32B-Instruct
  - model: NovaSky-AI/Sky-T1-32B-Flash
merge_method: sce
base_model: Qwen/Qwen2.5-Coder-32B
parameters:
  select_topk: 1.0
dtype: bfloat16