RichardErkhov/jaeyong2_-_Qwen2.5-3B-Instruct-Ja-SFT-gguf

Quantization made by Richard Erkhov.

Qwen2.5-3B-Instruct-Ja-SFT - GGUF

Model creator: https://huggingface.co/jaeyong2/
Original model: https://huggingface.co/jaeyong2/Qwen2.5-3B-Instruct-Ja-SFT/

Name	Quant method	Size
Qwen2.5-3B-Instruct-Ja-SFT.Q2_K.gguf	Q2_K	1.19GB
Qwen2.5-3B-Instruct-Ja-SFT.Q3_K_S.gguf	Q3_K_S	1.35GB
Qwen2.5-3B-Instruct-Ja-SFT.Q3_K.gguf	Q3_K	1.48GB
Qwen2.5-3B-Instruct-Ja-SFT.Q3_K_M.gguf	Q3_K_M	1.48GB
Qwen2.5-3B-Instruct-Ja-SFT.Q3_K_L.gguf	Q3_K_L	1.59GB
Qwen2.5-3B-Instruct-Ja-SFT.IQ4_XS.gguf	IQ4_XS	1.63GB
Qwen2.5-3B-Instruct-Ja-SFT.Q4_0.gguf	Q4_0	1.7GB
Qwen2.5-3B-Instruct-Ja-SFT.IQ4_NL.gguf	IQ4_NL	1.71GB
Qwen2.5-3B-Instruct-Ja-SFT.Q4_K_S.gguf	Q4_K_S	1.71GB
Qwen2.5-3B-Instruct-Ja-SFT.Q4_K.gguf	Q4_K	1.8GB
Qwen2.5-3B-Instruct-Ja-SFT.Q4_K_M.gguf	Q4_K_M	1.8GB
Qwen2.5-3B-Instruct-Ja-SFT.Q4_1.gguf	Q4_1	1.86GB
Qwen2.5-3B-Instruct-Ja-SFT.Q5_0.gguf	Q5_0	2.02GB
Qwen2.5-3B-Instruct-Ja-SFT.Q5_K_S.gguf	Q5_K_S	2.02GB
Qwen2.5-3B-Instruct-Ja-SFT.Q5_K.gguf	Q5_K	2.07GB
Qwen2.5-3B-Instruct-Ja-SFT.Q5_K_M.gguf	Q5_K_M	2.07GB
Qwen2.5-3B-Instruct-Ja-SFT.Q5_1.gguf	Q5_1	2.18GB
Qwen2.5-3B-Instruct-Ja-SFT.Q6_K.gguf	Q6_K	2.36GB
Qwen2.5-3B-Instruct-Ja-SFT.Q8_0.gguf	Q8_0	3.06GB

Original model description:

base_model: - Qwen/Qwen2.5-3B-Instruct language: - ja - en library_name: transformers

Evaluation

llm-jp-eval script(colab)

!git clone https://github.com/llm-jp/llm-jp-eval.git
!cd llm-jp-eval && pip install -e .
!cd llm-jp-eval && python scripts/preprocess_dataset.py --dataset-name all --output-dir ./dataset_dir
!cd llm-jp-eval && python scripts/evaluate_llm.py -cn config.yaml model.pretrained_model_name_or_path=jaeyong2/Qwen2.5-0.5B-Instruct-JaMagpie-Preview tokenizer.pretrained_model_name_or_path=jaeyong2/Qwen2.5-0.5B-Instruct-JaMagpie-Preview dataset_dir=./dataset_dir/1.4.1/evaluation/test

llm-jp-eval	Qwen2.5-3B-Instruct	finetuning-model
AVG	0.4921	0.4895
CG	0.1000	0
EL	0.4770	0.4431
FA	0.1210	0.1246
HE	0.5550	0.5650
MC	0.7133	0.7900
MR	0.5400	0.6100
MT	0.6391	0.5982
NLI	0.6640	0.6640
QA	0.2638	0.3165
RC	0.8481	0.7837

Testing Data, Factors & Metrics

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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