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stojchet/
lr_small_e5_sft

Name: lr_small_e5_sft
Availability: InStock

generated_from_trainer

generator

deepseek-ai/deepseek-coder-1.3b-base

adapter

other

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lr_small_e5_sft

This model is a fine-tuned version of deepseek-ai/deepseek-coder-1.3b-base on the generator dataset. It achieves the following results on the evaluation set:

Loss: 1.2372

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: 1.41e-07
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 128
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
1.2662	0.0128	1	1.2401
1.2266	0.0256	2	1.2401
1.2594	0.0384	3	1.2401
1.1592	0.0512	4	1.2400
1.2268	0.064	5	1.2400
1.2151	0.0768	6	1.2400
1.2202	0.0896	7	1.2400
1.2694	0.1024	8	1.2400
1.2341	0.1152	9	1.2400
1.2519	0.128	10	1.2399
1.2733	0.1408	11	1.2399
1.2018	0.1536	12	1.2399
1.1774	0.1664	13	1.2399
1.2955	0.1792	14	1.2399
1.2297	0.192	15	1.2399
1.2685	0.2048	16	1.2399
1.2485	0.2176	17	1.2398
1.2604	0.2304	18	1.2398
1.2048	0.2432	19	1.2398
1.2037	0.256	20	1.2398
1.2399	0.2688	21	1.2398
1.212	0.2816	22	1.2398
1.2505	0.2944	23	1.2398
1.2966	0.3072	24	1.2397
1.2109	0.32	25	1.2397
1.2595	0.3328	26	1.2397
1.2283	0.3456	27	1.2397
1.2027	0.3584	28	1.2397
1.2004	0.3712	29	1.2397
1.189	0.384	30	1.2397
1.1946	0.3968	31	1.2396
1.2234	0.4096	32	1.2396
1.269	0.4224	33	1.2396
1.2421	0.4352	34	1.2396
1.2031	0.448	35	1.2396
1.1914	0.4608	36	1.2396
1.2188	0.4736	37	1.2396
1.2434	0.4864	38	1.2395
1.276	0.4992	39	1.2395
1.212	0.512	40	1.2395
1.2414	0.5248	41	1.2395
1.2359	0.5376	42	1.2395
1.1813	0.5504	43	1.2395
1.2172	0.5632	44	1.2395
1.2052	0.576	45	1.2395
1.2451	0.5888	46	1.2394
1.1902	0.6016	47	1.2394
1.2322	0.6144	48	1.2394
1.2201	0.6272	49	1.2394
1.2086	0.64	50	1.2394
1.2439	0.6528	51	1.2394
1.1928	0.6656	52	1.2394
1.2412	0.6784	53	1.2394
1.2058	0.6912	54	1.2393
1.1883	0.704	55	1.2393
1.2065	0.7168	56	1.2393
1.2334	0.7296	57	1.2393
1.2276	0.7424	58	1.2393
1.2009	0.7552	59	1.2393
1.245	0.768	60	1.2393
1.2835	0.7808	61	1.2393
1.2671	0.7936	62	1.2392
1.201	0.8064	63	1.2392
1.2489	0.8192	64	1.2392
1.2038	0.832	65	1.2392
1.1625	0.8448	66	1.2392
1.2959	0.8576	67	1.2392
1.1787	0.8704	68	1.2392
1.1992	0.8832	69	1.2392
1.1789	0.896	70	1.2391
1.2455	0.9088	71	1.2391
1.269	0.9216	72	1.2391
1.2519	0.9344	73	1.2391
1.1986	0.9472	74	1.2391
1.2427	0.96	75	1.2391
1.2743	0.9728	76	1.2391
1.2139	0.9856	77	1.2391
1.2297	0.9984	78	1.2390
1.2004	1.0112	79	1.2390
1.1837	1.024	80	1.2390
1.1996	1.0368	81	1.2390
1.2621	1.0496	82	1.2390
1.2061	1.0624	83	1.2390
1.1926	1.0752	84	1.2390
1.2024	1.088	85	1.2390
1.2435	1.1008	86	1.2390
1.2531	1.1136	87	1.2389
1.1794	1.1264	88	1.2389
1.2244	1.1392	89	1.2389
1.2612	1.152	90	1.2389
1.187	1.1648	91	1.2389
1.1657	1.1776	92	1.2389
1.2265	1.1904	93	1.2389
1.2354	1.2032	94	1.2389
1.2089	1.216	95	1.2388
1.2879	1.2288	96	1.2388
1.1864	1.2416	97	1.2388
1.1877	1.2544	98	1.2388
1.2365	1.2672	99	1.2388
1.2211	1.28	100	1.2388
1.1654	1.2928	101	1.2388
1.2635	1.3056	102	1.2388
1.2163	1.3184	103	1.2388
1.2451	1.3312	104	1.2388
1.1779	1.3440	105	1.2387
1.2307	1.3568	106	1.2387
1.216	1.3696	107	1.2387
1.2072	1.3824	108	1.2387
1.2682	1.3952	109	1.2387
1.2594	1.408	110	1.2387
1.2478	1.4208	111	1.2387
1.2661	1.4336	112	1.2387
1.2112	1.4464	113	1.2387
1.2198	1.4592	114	1.2386
1.2139	1.472	115	1.2386
1.2387	1.4848	116	1.2386
1.2283	1.4976	117	1.2386
1.2096	1.5104	118	1.2386
1.1933	1.5232	119	1.2386
1.2256	1.536	120	1.2386
1.2189	1.5488	121	1.2386
1.2513	1.5616	122	1.2386
1.2174	1.5744	123	1.2386
1.1715	1.5872	124	1.2386
1.2519	1.6	125	1.2385
1.3025	1.6128	126	1.2385
1.275	1.6256	127	1.2385
1.2159	1.6384	128	1.2385
1.2654	1.6512	129	1.2385
1.2578	1.6640	130	1.2385
1.2109	1.6768	131	1.2385
1.2051	1.6896	132	1.2385
1.2345	1.7024	133	1.2385
1.2252	1.7152	134	1.2385
1.2907	1.728	135	1.2384
1.2581	1.7408	136	1.2384
1.2019	1.7536	137	1.2384
1.1398	1.7664	138	1.2384
1.248	1.7792	139	1.2384
1.2405	1.792	140	1.2384
1.2377	1.8048	141	1.2384
1.2202	1.8176	142	1.2384
1.2258	1.8304	143	1.2384
1.2795	1.8432	144	1.2384
1.2337	1.8560	145	1.2383
1.2606	1.8688	146	1.2383
1.2036	1.8816	147	1.2383
1.2198	1.8944	148	1.2383
1.1932	1.9072	149	1.2383
1.2388	1.92	150	1.2383
1.2139	1.9328	151	1.2383
1.1596	1.9456	152	1.2383
1.2878	1.9584	153	1.2383
1.2157	1.9712	154	1.2383
1.2824	1.984	155	1.2382
1.2283	1.9968	156	1.2382
1.2511	2.0096	157	1.2382
1.2028	2.0224	158	1.2382
1.2694	2.0352	159	1.2382
1.2099	2.048	160	1.2382
1.1883	2.0608	161	1.2382
1.2332	2.0736	162	1.2382
1.2156	2.0864	163	1.2382
1.2147	2.0992	164	1.2382
1.1711	2.112	165	1.2381
1.2415	2.1248	166	1.2381
1.2761	2.1376	167	1.2381
1.2508	2.1504	168	1.2381
1.2206	2.1632	169	1.2381
1.2453	2.176	170	1.2381
1.2355	2.1888	171	1.2381
1.2047	2.2016	172	1.2381
1.2247	2.2144	173	1.2381
1.2012	2.2272	174	1.2381
1.1953	2.24	175	1.2381
1.2563	2.2528	176	1.2381
1.2429	2.2656	177	1.2380
1.1839	2.2784	178	1.2380
1.1571	2.2912	179	1.2380
1.2277	2.304	180	1.2380
1.2133	2.3168	181	1.2380
1.232	2.3296	182	1.2380
1.2567	2.3424	183	1.2380
1.2277	2.3552	184	1.2380
1.2397	2.368	185	1.2380
1.1894	2.3808	186	1.2380
1.1736	2.3936	187	1.2380
1.2767	2.4064	188	1.2380
1.2343	2.4192	189	1.2380
1.2082	2.432	190	1.2380
1.1952	2.4448	191	1.2379
1.2408	2.4576	192	1.2379
1.2013	2.4704	193	1.2379
1.231	2.4832	194	1.2379
1.24	2.496	195	1.2379
1.2435	2.5088	196	1.2379
1.2484	2.5216	197	1.2379
1.2466	2.5344	198	1.2379
1.2019	2.5472	199	1.2379
1.1954	2.56	200	1.2379
1.1974	2.5728	201	1.2379
1.2271	2.5856	202	1.2379
1.2532	2.5984	203	1.2379
1.1606	2.6112	204	1.2379
1.2247	2.624	205	1.2378
1.2458	2.6368	206	1.2378
1.2675	2.6496	207	1.2378
1.2415	2.6624	208	1.2378
1.227	2.6752	209	1.2378
1.1998	2.6880	210	1.2378
1.242	2.7008	211	1.2378
1.2265	2.7136	212	1.2378
1.2242	2.7264	213	1.2378
1.2559	2.7392	214	1.2378
1.2286	2.752	215	1.2378
1.2285	2.7648	216	1.2378
1.2311	2.7776	217	1.2378
1.268	2.7904	218	1.2378
1.2513	2.8032	219	1.2377
1.2834	2.816	220	1.2377
1.2535	2.8288	221	1.2377
1.2214	2.8416	222	1.2377
1.2127	2.8544	223	1.2377
1.1854	2.8672	224	1.2377
1.2651	2.88	225	1.2377
1.2434	2.8928	226	1.2377
1.1727	2.9056	227	1.2377
1.237	2.9184	228	1.2377
1.182	2.9312	229	1.2377
1.1925	2.944	230	1.2377
1.2101	2.9568	231	1.2377
1.2566	2.9696	232	1.2377
1.2056	2.9824	233	1.2377
1.2068	2.9952	234	1.2377
1.2142	3.008	235	1.2377
1.2174	3.0208	236	1.2376
1.2084	3.0336	237	1.2376
1.2535	3.0464	238	1.2376
1.2019	3.0592	239	1.2376
1.2401	3.072	240	1.2376
1.187	3.0848	241	1.2376
1.2852	3.0976	242	1.2376
1.2061	3.1104	243	1.2376
1.1962	3.1232	244	1.2376
1.197	3.136	245	1.2376
1.2596	3.1488	246	1.2376
1.2476	3.1616	247	1.2376
1.163	3.1744	248	1.2376
1.2619	3.1872	249	1.2376
1.2432	3.2	250	1.2376
1.2739	3.2128	251	1.2376
1.2259	3.2256	252	1.2376
1.2601	3.2384	253	1.2376
1.223	3.2512	254	1.2375
1.2189	3.2640	255	1.2375
1.2332	3.2768	256	1.2375
1.2355	3.2896	257	1.2375
1.2167	3.3024	258	1.2375
1.2379	3.3152	259	1.2375
1.2399	3.328	260	1.2375
1.2689	3.3408	261	1.2375
1.1815	3.3536	262	1.2375
1.2063	3.3664	263	1.2375
1.2283	3.3792	264	1.2375
1.2725	3.392	265	1.2375
1.2147	3.4048	266	1.2375
1.1906	3.4176	267	1.2375
1.2483	3.4304	268	1.2375
1.2373	3.4432	269	1.2375
1.2347	3.456	270	1.2375
1.2671	3.4688	271	1.2375
1.2533	3.4816	272	1.2375
1.1967	3.4944	273	1.2375
1.2325	3.5072	274	1.2375
1.1707	3.52	275	1.2374
1.2044	3.5328	276	1.2374
1.2309	3.5456	277	1.2374
1.2419	3.5584	278	1.2374
1.2066	3.5712	279	1.2374
1.19	3.584	280	1.2374
1.2569	3.5968	281	1.2374
1.2009	3.6096	282	1.2374
1.2628	3.6224	283	1.2374
1.2042	3.6352	284	1.2374
1.1863	3.648	285	1.2374
1.242	3.6608	286	1.2374
1.2452	3.6736	287	1.2374
1.2264	3.6864	288	1.2374
1.2975	3.6992	289	1.2374
1.2291	3.7120	290	1.2374
1.2091	3.7248	291	1.2374
1.2253	3.7376	292	1.2374
1.2382	3.7504	293	1.2374
1.2249	3.7632	294	1.2374
1.2276	3.776	295	1.2374
1.2609	3.7888	296	1.2374
1.2503	3.8016	297	1.2374
1.1788	3.8144	298	1.2374
1.2189	3.8272	299	1.2374
1.1886	3.84	300	1.2373
1.2337	3.8528	301	1.2373
1.2084	3.8656	302	1.2373
1.2366	3.8784	303	1.2373
1.183	3.8912	304	1.2373
1.2253	3.904	305	1.2373
1.2123	3.9168	306	1.2373
1.1668	3.9296	307	1.2373
1.1947	3.9424	308	1.2373
1.2131	3.9552	309	1.2373
1.2225	3.968	310	1.2373
1.2312	3.9808	311	1.2373
1.1971	3.9936	312	1.2373
1.2206	4.0064	313	1.2373
1.1943	4.0192	314	1.2373
1.251	4.032	315	1.2373
1.217	4.0448	316	1.2373
1.2485	4.0576	317	1.2373
1.1592	4.0704	318	1.2373
1.2168	4.0832	319	1.2373
1.2029	4.096	320	1.2373
1.2057	4.1088	321	1.2373
1.2338	4.1216	322	1.2373
1.2558	4.1344	323	1.2373
1.2744	4.1472	324	1.2373
1.2222	4.16	325	1.2373
1.2182	4.1728	326	1.2373
1.2555	4.1856	327	1.2373
1.238	4.1984	328	1.2373
1.2253	4.2112	329	1.2373
1.2597	4.224	330	1.2373
1.1844	4.2368	331	1.2373
1.2253	4.2496	332	1.2373
1.2254	4.2624	333	1.2373
1.23	4.2752	334	1.2373
1.283	4.288	335	1.2373
1.2561	4.3008	336	1.2372
1.2368	4.3136	337	1.2372
1.2204	4.3264	338	1.2372
1.204	4.3392	339	1.2372
1.2165	4.352	340	1.2372
1.2591	4.3648	341	1.2372
1.2301	4.3776	342	1.2372
1.2	4.3904	343	1.2372
1.228	4.4032	344	1.2372
1.2481	4.416	345	1.2372
1.2043	4.4288	346	1.2372
1.2609	4.4416	347	1.2372
1.2163	4.4544	348	1.2372
1.257	4.4672	349	1.2372
1.2073	4.48	350	1.2372
1.2602	4.4928	351	1.2372
1.2639	4.5056	352	1.2372
1.2102	4.5184	353	1.2372
1.2104	4.5312	354	1.2372
1.1943	4.5440	355	1.2372
1.223	4.5568	356	1.2372
1.2168	4.5696	357	1.2372
1.2054	4.5824	358	1.2372
1.2042	4.5952	359	1.2372
1.1875	4.608	360	1.2372
1.188	4.6208	361	1.2372
1.2374	4.6336	362	1.2372
1.2418	4.6464	363	1.2372
1.2363	4.6592	364	1.2372
1.2319	4.672	365	1.2372
1.2349	4.6848	366	1.2372
1.2357	4.6976	367	1.2372
1.1544	4.7104	368	1.2372
1.1897	4.7232	369	1.2372
1.2462	4.736	370	1.2372
1.1635	4.7488	371	1.2372
1.2841	4.7616	372	1.2372
1.2488	4.7744	373	1.2372
1.2466	4.7872	374	1.2372
1.23	4.8	375	1.2372
1.199	4.8128	376	1.2372
1.2095	4.8256	377	1.2372
1.1816	4.8384	378	1.2372
1.3457	4.8512	379	1.2372
1.2052	4.864	380	1.2372
1.233	4.8768	381	1.2372
1.2699	4.8896	382	1.2372
1.2544	4.9024	383	1.2372
1.221	4.9152	384	1.2372
1.2299	4.928	385	1.2372
1.1672	4.9408	386	1.2372
1.1694	4.9536	387	1.2372
1.2394	4.9664	388	1.2372
1.2328	4.9792	389	1.2372
1.1979	4.992	390	1.2372

Framework versions

PEFT 0.10.0
Transformers 4.43.0.dev0
Pytorch 2.2.2+cu121
Datasets 2.19.2
Tokenizers 0.19.1

stojchet/lr_small_e5_sft

lr_small_e5_sft

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

stojchet/
lr_small_e5_sft