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RichardErkhov/JulianS_-_basho_haiku_gpt2-gguf

sourceHugging Faceupdated 2y agoView on Hugging Face

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Quantization made by Richard Erkhov.

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bashohaikugpt2 - GGUF

—Model creator: https://huggingface.co/JulianS/
—Original model: https://huggingface.co/JulianS/bashohaikugpt2/

Name	Quant method	Size
basho_haiku_gpt2.Q2_K.gguf	Q2_K	0.08GB
basho_haiku_gpt2.Q3_K_S.gguf	Q3KS	0.08GB
basho_haiku_gpt2.Q3_K.gguf	Q3_K	0.09GB
basho_haiku_gpt2.Q3_K_M.gguf	Q3KM	0.09GB
basho_haiku_gpt2.Q3_K_L.gguf	Q3KL	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	Q4KS	0.1GB
basho_haiku_gpt2.Q4_K.gguf	Q4_K	0.11GB
basho_haiku_gpt2.Q4_K_M.gguf	Q4KM	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	Q5KS	0.11GB
basho_haiku_gpt2.Q5_K.gguf	Q5_K	0.12GB
basho_haiku_gpt2.Q5_K_M.gguf	Q5KM	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:

—generatedfromtrainer model-index:
—name: bashohaikugpt2_test results: [] ---

bashohaikugpt2

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
—trainbatchsize: 1
—evalbatchsize: 8
—seed: 42
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: 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