Quantization made by Richard Erkhov.

GEITje-7B - GGUF

Model creator: https://huggingface.co/Rijgersberg/
Original model: https://huggingface.co/Rijgersberg/GEITje-7B/

Name	Quant method	Size
GEITje-7B.Q2_K.gguf	Q2_K	2.53GB
GEITje-7B.IQ3_XS.gguf	IQ3_XS	2.81GB
GEITje-7B.IQ3_S.gguf	IQ3_S	2.96GB
GEITje-7B.Q3_K_S.gguf	Q3_K_S	2.95GB
GEITje-7B.IQ3_M.gguf	IQ3_M	3.06GB
GEITje-7B.Q3_K.gguf	Q3_K	3.28GB
GEITje-7B.Q3_K_M.gguf	Q3_K_M	3.28GB
GEITje-7B.Q3_K_L.gguf	Q3_K_L	3.56GB
GEITje-7B.IQ4_XS.gguf	IQ4_XS	3.67GB
GEITje-7B.Q4_0.gguf	Q4_0	3.83GB
GEITje-7B.IQ4_NL.gguf	IQ4_NL	3.87GB
GEITje-7B.Q4_K_S.gguf	Q4_K_S	3.86GB
GEITje-7B.Q4_K.gguf	Q4_K	4.07GB
GEITje-7B.Q4_K_M.gguf	Q4_K_M	4.07GB
GEITje-7B.Q4_1.gguf	Q4_1	4.24GB
GEITje-7B.Q5_0.gguf	Q5_0	4.65GB
GEITje-7B.Q5_K_S.gguf	Q5_K_S	4.65GB
GEITje-7B.Q5_K.gguf	Q5_K	4.78GB
GEITje-7B.Q5_K_M.gguf	Q5_K_M	4.78GB
GEITje-7B.Q5_1.gguf	Q5_1	5.07GB
GEITje-7B.Q6_K.gguf	Q6_K	5.53GB
GEITje-7B.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - generated_from_trainer - GEITje datasets: - Rijgersberg/GEITje-pretrain-10b model-index: - name: GEITje-v1-7B results: [] language: - nl

GEITje-7B

GEITje is a large open Dutch language model with 7 billion parameters, based on Mistral 7B. It has been further trained on 10 billion tokens of Dutch text. This has improved its Dutch language skills and increased its knowledge of Dutch topics.

Model description

Mistral – Base Model

GEITje is based on Mistral 7B. It's a large open language model with 7 billion parameters, trained by Mistral AI. According to Mistral AI, the 7B model performs better than Llama 2 13B on all (English-language) benchmarks they tested it on. Mistral 7B has been released under the Apache 2.0 open source license.

GEITje – Trained Further on Dutch Texts

GEITje was created by further training Mistral 7B on no less than 10 billion tokens of Dutch text from the Dutch Gigacorpus and the MADLAD-400 web crawling corpus. It is a so-called full-parameter finetune: performed on all parameters. It is not a PEFT or LoRA finetune. Like Mistral, GEITje has a context length of 8,192 tokens.

More info

Read more about GEITje in the 📄 README on GitHub.

Checkpoints

Intermediate checkpoints are available in the checkpoints branch.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 8
total_train_batch_size: 128
total_eval_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 953
training_steps: 9536

Training results

Training Loss	Epoch	Step	Validation Loss
1.6995	0.02	199	1.7673
1.6949	0.04	398	1.6880
1.6377	0.06	597	1.6429
1.6011	0.08	796	1.6384
1.5196	0.1	995	1.6060
1.5158	0.13	1194	1.5832
1.5181	0.15	1393	1.5541
1.4931	0.17	1592	1.5493
1.4972	0.19	1791	1.5407
1.5349	0.21	1990	1.5305
1.5025	0.23	2189	1.5263
1.396	0.25	2388	1.5140
1.4353	0.27	2587	1.5104
1.4307	0.29	2786	1.5003
1.3974	0.31	2985	1.4849
1.404	0.33	3184	1.4771
1.4299	0.35	3383	1.4825
1.4342	0.38	3582	1.4705
1.4341	0.4	3781	1.4643
1.4535	0.42	3980	1.4580
1.4799	0.44	4179	1.4521
1.35	0.46	4378	1.4478
1.4586	0.48	4577	1.4425
1.3685	0.5	4776	1.4368
1.4572	0.52	4975	1.4313
1.3293	0.54	5174	1.4265
1.403	0.56	5373	1.4241
1.3057	0.58	5572	1.4188
1.244	0.61	5771	1.4178
1.3224	0.63	5970	1.4110
1.3238	0.65	6169	1.4083
1.3262	0.67	6368	1.4050
1.3237	0.69	6567	1.4027
1.0453	0.71	6766	1.4005
1.3136	0.73	6965	1.3992
1.3137	0.75	7164	1.3975
1.1587	0.77	7363	1.3964
1.316	0.79	7562	1.3957
1.2738	0.81	7761	1.3951
1.308	0.83	7960	1.3949
1.4049	0.86	8159	1.3946
1.3324	0.88	8358	1.3944
1.3446	0.9	8557	1.3944
1.2489	0.92	8756	1.3943
1.2687	0.94	8955	1.3943
1.3293	0.96	9154	1.3943
1.3045	0.98	9353	1.3943

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.1.1+cu121
Datasets 2.15.0
Tokenizers 0.15.0