gemma-2b-fine-tuned for learning Python Programming easy

This model is a fine-tuned version of google/gemma-2b on a very small dataset of 205 carefully datapoints on Python programming. It achieves the following results on the evaluation set:

• Loss: 1.2177

Model description

This model experiments with fine tuning a large language model for a small task which is teaching Python in simple terms

Intended uses & limitations

The model is intended to be used experimentally, it would require more data points and training to work much better

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 1
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss
1.756	0.19	2	1.6592
1.4272	0.39	4	1.6572
1.6918	0.58	6	1.6529
1.8009	0.77	8	1.6469
1.674	0.96	10	1.6384
1.5397	1.16	12	1.6273
1.6255	1.35	14	1.6131
1.5575	1.54	16	1.5947
1.5248	1.73	18	1.5750
1.5811	1.93	20	1.5545
1.7426	2.12	22	1.5339
1.5397	2.31	24	1.5140
1.421	2.51	26	1.4953
1.3699	2.7	28	1.4778
1.3421	2.89	30	1.4616
1.5048	3.08	32	1.4483
1.3779	3.28	34	1.4362
1.435	3.47	36	1.4247
1.2924	3.66	38	1.4130
1.375	3.86	40	1.4011
1.3808	4.05	42	1.3894
1.3854	4.24	44	1.3776
1.2755	4.43	46	1.3668
1.1832	4.63	48	1.3568
1.4068	4.82	50	1.3473
1.197	5.01	52	1.3383
1.396	5.2	54	1.3300
1.0756	5.4	56	1.3219
1.164	5.59	58	1.3140
1.2238	5.78	60	1.3067
1.2795	5.98	62	1.2999
1.2425	6.17	64	1.2940
1.1914	6.36	66	1.2884
1.2129	6.55	68	1.2832
1.0642	6.75	70	1.2783
1.1238	6.94	72	1.2736
1.0442	7.13	74	1.2692
1.1614	7.33	76	1.2650
1.2674	7.52	78	1.2613
0.973	7.71	80	1.2579
1.1108	7.9	82	1.2551
1.2114	8.1	84	1.2519
0.9327	8.29	86	1.2487
1.0495	8.48	88	1.2459
1.1297	8.67	90	1.2434
1.1777	8.87	92	1.2413
0.9277	9.06	94	1.2394
1.0063	9.25	96	1.2376
1.0652	9.45	98	1.2359
1.0928	9.64	100	1.2342
1.0611	9.83	102	1.2329
0.9749	10.02	104	1.2314
0.9305	10.22	106	1.2300
0.9944	10.41	108	1.2289
1.1229	10.6	110	1.2277
1.1502	10.8	112	1.2269
0.8728	10.99	114	1.2261
0.9504	11.18	116	1.2253
1.0989	11.37	118	1.2242
0.9485	11.57	120	1.2235
1.0335	11.76	122	1.2227
1.0332	11.95	124	1.2222
0.8178	12.14	126	1.2215
1.0058	12.34	128	1.2208
1.034	12.53	130	1.2202
0.9451	12.72	132	1.2197
0.9163	12.92	134	1.2193
1.173	13.11	136	1.2190
1.0758	13.3	138	1.2185
0.9012	13.49	140	1.2184
0.9099	13.69	142	1.2180
1.0	13.88	144	1.2180
1.0032	14.07	146	1.2179
0.991	14.27	148	1.2177
0.8836	14.46	150	1.2177

Framework versions

• PEFT 0.10.0
• Transformers 4.39.3
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2