Quantization made by Richard Erkhov.

sparse_mistral_7b_refined_web_50p_2024-04-13 - GGUF

Model creator: https://huggingface.co/thrunlab/
Original model: https://huggingface.co/thrunlab/sparse_mistral_7b_refined_web_50p_2024-04-13/

Name	Quant method	Size
sparse_mistral_7b_refined_web_50p_2024-04-13.Q2_K.gguf	Q2_K	2.53GB
sparse_mistral_7b_refined_web_50p_2024-04-13.IQ3_XS.gguf	IQ3_XS	2.81GB
sparse_mistral_7b_refined_web_50p_2024-04-13.IQ3_S.gguf	IQ3_S	2.96GB
sparse_mistral_7b_refined_web_50p_2024-04-13.Q3_K_S.gguf	Q3_K_S	2.95GB
sparse_mistral_7b_refined_web_50p_2024-04-13.IQ3_M.gguf	IQ3_M	3.06GB
sparse_mistral_7b_refined_web_50p_2024-04-13.Q3_K.gguf	Q3_K	3.28GB
sparse_mistral_7b_refined_web_50p_2024-04-13.Q3_K_M.gguf	Q3_K_M	3.28GB
sparse_mistral_7b_refined_web_50p_2024-04-13.Q3_K_L.gguf	Q3_K_L	3.56GB
sparse_mistral_7b_refined_web_50p_2024-04-13.IQ4_XS.gguf	IQ4_XS	3.67GB
sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_0.gguf	Q4_0	3.83GB
sparse_mistral_7b_refined_web_50p_2024-04-13.IQ4_NL.gguf	IQ4_NL	3.87GB
sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_K_S.gguf	Q4_K_S	3.86GB
sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_K.gguf	Q4_K	4.07GB
sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_K_M.gguf	Q4_K_M	4.07GB
sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_1.gguf	Q4_1	4.24GB
sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_0.gguf	Q5_0	4.65GB
sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_K_S.gguf	Q5_K_S	4.65GB
sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_K.gguf	Q5_K	4.78GB
sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_K_M.gguf	Q5_K_M	4.78GB
sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_1.gguf	Q5_1	5.07GB
sparse_mistral_7b_refined_web_50p_2024-04-13.Q6_K.gguf	Q6_K	5.53GB

Original model description:

license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags:

generated_from_trainer model-index:
name: sparse_mistral_7b_refined_web_50p_2024-04-13 results: []

sparse_mistral_7b_refined_web_50p_2024-04-13

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.1985

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 1
eval_batch_size: 4
seed: 0
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 8
total_train_batch_size: 32
total_eval_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 2350

Training results

Training Loss	Epoch	Step	Validation Loss
2.3391	0.01	25	2.4196
2.2711	0.02	50	2.3577
2.3054	0.02	75	2.3158
2.2795	0.03	100	2.2966
2.3175	0.04	125	2.2846
2.2388	0.05	150	2.2766
2.1679	0.06	175	2.2705
2.2996	0.06	200	2.2678
2.2788	0.07	225	2.2647
2.2448	0.08	250	2.2637
2.1837	0.09	275	2.2624
2.2089	0.1	300	2.2621
2.2686	0.1	325	2.2601
2.2254	0.11	350	2.2593
2.162	0.12	375	2.2590
2.2687	0.13	400	2.2563
2.2595	0.14	425	2.2571
2.186	0.14	450	2.2564
2.2689	0.15	475	2.2580
2.2472	0.16	500	2.2554
2.2005	0.17	525	2.2553
2.1983	0.18	550	2.2552
2.2388	0.18	575	2.2547
2.1443	0.19	600	2.2555
2.2198	0.2	625	2.2534
2.3008	0.21	650	2.2536
2.179	0.22	675	2.2521
2.2069	0.22	700	2.2531
2.1819	0.23	725	2.2526
2.1218	0.24	750	2.2536
2.1845	0.25	775	2.2515
2.2167	0.26	800	2.2510
2.2252	0.26	825	2.2520
2.1664	0.27	850	2.2519
2.1853	0.28	875	2.2530
2.1499	0.29	900	2.2513
2.2763	0.3	925	2.2517
2.2528	0.3	950	2.2518
2.2505	0.31	975	2.2500
2.1683	0.32	1000	2.2502
2.2177	0.33	1025	2.2501
2.238	0.34	1050	2.2516
2.193	0.34	1075	2.2507
2.2025	0.35	1100	2.2502
2.0944	0.36	1125	2.2512
2.2272	0.37	1150	2.2508
2.2264	0.38	1175	2.2500
2.1837	0.38	1200	2.2507
2.1444	0.39	1225	2.2489
2.2464	0.4	1250	2.2499
2.1388	0.41	1275	2.2508
2.193	0.42	1300	2.2492
2.2376	0.42	1325	2.2506
2.2212	0.43	1350	2.2478
2.2002	0.44	1375	2.2488
2.2729	0.45	1400	2.2484
2.2329	0.46	1425	2.2473
2.1919	0.46	1450	2.2481
2.2102	0.47	1475	2.2475
2.1466	0.48	1500	2.2473
2.1819	0.49	1525	2.2478
2.2558	0.5	1550	2.2468
2.2137	0.5	1575	2.2463
2.2288	0.51	1600	2.2466
2.1479	0.52	1625	2.2468
2.1726	0.53	1650	2.2471
2.1805	0.54	1675	2.2454
2.1505	0.54	1700	2.2470
2.1337	0.55	1725	2.2465
2.2413	0.56	1750	2.2460
2.152	0.57	1775	2.2478
2.2669	0.58	1800	2.2471
2.2925	0.58	1825	2.2465
2.222	0.59	1850	2.2457
2.1308	0.6	1875	2.2466
2.201	0.61	1900	2.2456
2.2247	0.62	1925	2.2460
2.2426	0.62	1950	2.2463
2.2312	0.63	1975	2.2465
2.2679	0.64	2000	2.2464
2.1928	0.65	2025	2.2463
2.2087	0.66	2050	2.2455
2.1792	0.66	2075	2.2470
2.252	0.67	2100	2.2468
2.2018	0.68	2125	2.2456
2.2006	0.69	2150	2.2451
2.2076	0.7	2175	2.2449
2.2436	0.7	2200	2.2460
2.2156	0.71	2225	2.2477
2.1348	0.72	2250	2.2455
2.1338	0.73	2275	2.2450
2.2147	0.74	2300	2.2455
2.2766	0.74	2325	2.2444
2.204	0.75	2350	2.2458

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.15.0
Tokenizers 0.15.0