JulianS_-_basho_haiku_gpt2-gguf

RichardErkhov/JulianS_-_basho_haiku_gpt2-gguf

JulianS_-_basho_haiku_gpt2-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

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At a glance

Access: Public
Created: Nov 3, 2024
Updated: Nov 3, 2024
SHA: e29c3bed

Model card

From the Hugging Face model README

Quantization made by Richard Erkhov.

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basho_haiku_gpt2 - GGUF

Model creator: https://huggingface.co/JulianS/
Original model: https://huggingface.co/JulianS/basho_haiku_gpt2/

Name	Quant method	Size
basho_haiku_gpt2.Q2_K.gguf	Q2_K	0.08GB
basho_haiku_gpt2.Q3_K_S.gguf	Q3_K_S	0.08GB
basho_haiku_gpt2.Q3_K.gguf	Q3_K	0.09GB
basho_haiku_gpt2.Q3_K_M.gguf	Q3_K_M	0.09GB
basho_haiku_gpt2.Q3_K_L.gguf	Q3_K_L	0.1GB
basho_haiku_gpt2.IQ4_XS.gguf	IQ4_XS	0.1GB
basho_haiku_gpt2.Q4_0.gguf	Q4_0	0.1GB
basho_haiku_gpt2.IQ4_NL.gguf	IQ4_NL	0.1GB
basho_haiku_gpt2.Q4_K_S.gguf	Q4_K_S	0.1GB
basho_haiku_gpt2.Q4_K.gguf	Q4_K	0.11GB
basho_haiku_gpt2.Q4_K_M.gguf	Q4_K_M	0.11GB
basho_haiku_gpt2.Q4_1.gguf	Q4_1	0.11GB
basho_haiku_gpt2.Q5_0.gguf	Q5_0	0.11GB
basho_haiku_gpt2.Q5_K_S.gguf	Q5_K_S	0.11GB
basho_haiku_gpt2.Q5_K.gguf	Q5_K	0.12GB
basho_haiku_gpt2.Q5_K_M.gguf	Q5_K_M	0.12GB
basho_haiku_gpt2.Q5_1.gguf	Q5_1	0.12GB
basho_haiku_gpt2.Q6_K.gguf	Q6_K	0.13GB
basho_haiku_gpt2.Q8_0.gguf	Q8_0	0.17GB

Original model description:

base_model: gpt2 tags:

generated_from_trainer model-index:
name: basho_haiku_gpt2_test results: []

basho_haiku_gpt2

Generate a Haiku in the style of Matsuo Bashō given an initial word prompt!

This model is a fine-tuned version of gpt2 on a Haiku dataset. It achieves the following results on the evaluation set:

Loss: 0.7002

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 1
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss
0.9095	0.11	100	0.6605
0.6638	0.22	200	0.6303
0.6456	0.33	300	0.6163
0.6421	0.45	400	0.6139
0.6421	0.56	500	0.6069
0.6182	0.67	600	0.5944
0.6277	0.78	700	0.5910
0.6409	0.89	800	0.5878
0.6047	1.0	900	0.5807
0.4944	1.11	1000	0.5847
0.4878	1.23	1100	0.5897
0.4706	1.34	1200	0.5947
0.4829	1.45	1300	0.5866
0.4742	1.56	1400	0.5875
0.4555	1.67	1500	0.5884
0.4713	1.78	1600	0.5890
0.4669	1.9	1700	0.5848
0.475	2.01	1800	0.5838
0.3762	2.12	1900	0.6123
0.3703	2.23	2000	0.6172
0.3772	2.34	2100	0.6118
0.3731	2.45	2200	0.6090
0.3662	2.56	2300	0.6151
0.3894	2.68	2400	0.6132
0.3663	2.79	2500	0.6195
0.368	2.9	2600	0.6163
0.3735	3.01	2700	0.6191
0.3006	3.12	2800	0.6518
0.3071	3.23	2900	0.6603
0.2898	3.34	3000	0.6629
0.2986	3.46	3100	0.6648
0.3107	3.57	3200	0.6558
0.3064	3.68	3300	0.6568
0.3052	3.79	3400	0.6633
0.3069	3.9	3500	0.6626
0.2872	4.01	3600	0.6641
0.2711	4.12	3700	0.6848
0.2584	4.24	3800	0.6944
0.2606	4.35	3900	0.7007
0.2538	4.46	4000	0.7029
0.2481	4.57	4100	0.7014
0.2466	4.68	4200	0.7006
0.25	4.79	4300	0.6990
0.2568	4.91	4400	0.7002

Framework versions

Transformers 4.38.2
Pytorch 2.0.1+cu117
Datasets 2.18.0
Tokenizers 0.15.2

Created: Nov 3, 2024
Updated: Nov 3, 2024