Quantization made by Richard Erkhov.

Qwen2.5-14B-Wernicke - GGUF

Model creator: https://huggingface.co/CultriX/
Original model: https://huggingface.co/CultriX/Qwen2.5-14B-Wernicke/

Name	Quant method	Size
Qwen2.5-14B-Wernicke.Q2_K.gguf	Q2_K	5.37GB
Qwen2.5-14B-Wernicke.Q3_K_S.gguf	Q3_K_S	6.2GB
Qwen2.5-14B-Wernicke.Q3_K.gguf	Q3_K	6.84GB
Qwen2.5-14B-Wernicke.Q3_K_M.gguf	Q3_K_M	6.84GB
Qwen2.5-14B-Wernicke.Q3_K_L.gguf	Q3_K_L	7.38GB
Qwen2.5-14B-Wernicke.IQ4_XS.gguf	IQ4_XS	7.62GB
Qwen2.5-14B-Wernicke.Q4_0.gguf	Q4_0	7.93GB
Qwen2.5-14B-Wernicke.IQ4_NL.gguf	IQ4_NL	8.01GB
Qwen2.5-14B-Wernicke.Q4_K_S.gguf	Q4_K_S	7.98GB
Qwen2.5-14B-Wernicke.Q4_K.gguf	Q4_K	8.37GB
Qwen2.5-14B-Wernicke.Q4_K_M.gguf	Q4_K_M	8.37GB
Qwen2.5-14B-Wernicke.Q4_1.gguf	Q4_1	8.75GB
Qwen2.5-14B-Wernicke.Q5_0.gguf	Q5_0	9.56GB
Qwen2.5-14B-Wernicke.Q5_K_S.gguf	Q5_K_S	9.56GB
Qwen2.5-14B-Wernicke.Q5_K.gguf	Q5_K	9.79GB
Qwen2.5-14B-Wernicke.Q5_K_M.gguf	Q5_K_M	9.79GB
Qwen2.5-14B-Wernicke.Q5_1.gguf	Q5_1	10.38GB
Qwen2.5-14B-Wernicke.Q6_K.gguf	Q6_K	11.29GB
Qwen2.5-14B-Wernicke.Q8_0.gguf	Q8_0	14.62GB

Original model description:

library_name: transformers tags: - mergekit - merge base_model: - v000000/Qwen2.5-Lumen-14B - arcee-ai/SuperNova-Medius - Qwen/Qwen2.5-14B - rombodawg/Rombos-LLM-V2.6-Qwen-14b - Qwen/Qwen2.5-14B-Instruct - EVA-UNIT-01/EVA-Qwen2.5-14B-v0.0 model-index: - name: Qwen2.5-14B-Wernicke results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 52.35 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CultriX/Qwen2.5-14B-Wernicke name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 50.64 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CultriX/Qwen2.5-14B-Wernicke name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 30.06 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CultriX/Qwen2.5-14B-Wernicke name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 19.13 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CultriX/Qwen2.5-14B-Wernicke name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 18.25 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CultriX/Qwen2.5-14B-Wernicke name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 49.15 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CultriX/Qwen2.5-14B-Wernicke name: Open LLM Leaderboard

merge

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

Merge Details

Merge Method

This model was merged using the Model Stock merge method using Qwen/Qwen2.5-14B 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:
  - model: EVA-UNIT-01/EVA-Qwen2.5-14B-v0.0
  - model: Qwen/Qwen2.5-14B-Instruct
  - model: arcee-ai/SuperNova-Medius
  - model: rombodawg/Rombos-LLM-V2.6-Qwen-14b
  - model: v000000/Qwen2.5-Lumen-14B
base_model: Qwen/Qwen2.5-14B
merge_method: model_stock
dtype: bfloat16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	36.60
IFEval (0-Shot)	52.35
BBH (3-Shot)	50.64
MATH Lvl 5 (4-Shot)	30.06
GPQA (0-shot)	19.13
MuSR (0-shot)	18.25
MMLU-PRO (5-shot)	49.15