diff --git a/__pycache__/train.cpython-312.pyc b/__pycache__/train.cpython-312.pyc
index ff62a17..fa6b907 100644
Binary files a/__pycache__/train.cpython-312.pyc and b/__pycache__/train.cpython-312.pyc differ
diff --git a/results.tsv b/results.tsv
index 7a0b97a..0bc3f69 100644
--- a/results.tsv
+++ b/results.tsv
@@ -1,500 +1,2001 @@
 step	lambda	train_loss	train_ppl	eval_ppl	eval_bpb	lr	time_s	best_ppl	q_neg1	q_zero	q_pos1
-1	0.0005	2.138573	8.49	-	-	1.00e-07	5.6	inf	-	-	-
-2	0.0010	2.562353	12.97	-	-	2.00e-07	5.8	inf	-	-	-
-3	0.0015	2.507907	12.28	-	-	3.00e-07	6.0	inf	-	-	-
-4	0.0020	1.832102	6.25	-	-	4.00e-07	6.2	inf	-	-	-
-5	0.0025	2.430138	11.36	-	-	5.00e-07	6.3	inf	-	-	-
-6	0.0030	1.799932	6.05	-	-	6.00e-07	6.5	inf	-	-	-
-7	0.0035	2.397582	11.00	-	-	7.00e-07	6.7	inf	-	-	-
-8	0.0040	2.210238	9.12	-	-	8.00e-07	6.9	inf	-	-	-
-9	0.0045	2.177710	8.83	-	-	9.00e-07	7.1	inf	-	-	-
-10	0.0050	2.095311	8.13	-	-	1.00e-06	7.3	inf	-	-	-
-11	0.0055	2.305612	10.03	-	-	1.10e-06	7.4	inf	-	-	-
-12	0.0060	2.072741	7.95	-	-	1.20e-06	7.6	inf	-	-	-
-13	0.0065	2.392291	10.94	-	-	1.30e-06	7.8	inf	-	-	-
-14	0.0070	2.352060	10.51	-	-	1.40e-06	8.0	inf	-	-	-
-15	0.0075	2.213432	9.15	-	-	1.50e-06	8.2	inf	-	-	-
-16	0.0080	2.151543	8.60	-	-	1.60e-06	8.4	inf	-	-	-
-17	0.0085	2.268387	9.66	-	-	1.70e-06	8.5	inf	-	-	-
-18	0.0090	2.036884	7.67	-	-	1.80e-06	8.7	inf	-	-	-
-19	0.0095	2.287216	9.85	-	-	1.90e-06	8.9	inf	-	-	-
-20	0.0100	2.472313	11.85	-	-	2.00e-06	9.1	inf	-	-	-
-21	0.0105	2.042691	7.71	-	-	2.10e-06	9.3	inf	-	-	-
-22	0.0110	2.230279	9.30	-	-	2.20e-06	9.5	inf	-	-	-
-23	0.0115	2.216335	9.17	-	-	2.30e-06	9.6	inf	-	-	-
-24	0.0120	2.185955	8.90	-	-	2.40e-06	9.8	inf	-	-	-
-25	0.0125	2.420405	11.25	-	-	2.50e-06	10.0	inf	-	-	-
-26	0.0130	1.879047	6.55	-	-	2.60e-06	10.2	inf	-	-	-
-27	0.0135	2.157828	8.65	-	-	2.70e-06	10.4	inf	-	-	-
-28	0.0140	2.038403	7.68	-	-	2.80e-06	10.6	inf	-	-	-
-29	0.0145	2.083694	8.03	-	-	2.90e-06	10.8	inf	-	-	-
-30	0.0150	1.899912	6.69	-	-	3.00e-06	10.9	inf	-	-	-
-31	0.0155	2.181333	8.86	-	-	3.10e-06	11.1	inf	-	-	-
-32	0.0160	1.903187	6.71	-	-	3.20e-06	11.3	inf	-	-	-
-33	0.0165	2.257179	9.56	-	-	3.30e-06	11.5	inf	-	-	-
-34	0.0170	2.008779	7.45	-	-	3.40e-06	11.7	inf	-	-	-
-35	0.0175	2.922477	18.59	-	-	3.50e-06	11.9	inf	-	-	-
-36	0.0180	2.388071	10.89	-	-	3.60e-06	12.0	inf	-	-	-
-37	0.0185	2.291557	9.89	-	-	3.70e-06	12.2	inf	-	-	-
-38	0.0190	2.242863	9.42	-	-	3.80e-06	12.4	inf	-	-	-
-39	0.0195	2.517748	12.40	-	-	3.90e-06	12.6	inf	-	-	-
-40	0.0200	2.573046	13.11	-	-	4.00e-06	12.8	inf	-	-	-
-41	0.0205	2.360054	10.59	-	-	4.10e-06	13.0	inf	-	-	-
-42	0.0210	2.409387	11.13	-	-	4.20e-06	13.1	inf	-	-	-
-43	0.0215	2.677930	14.55	-	-	4.30e-06	13.3	inf	-	-	-
-44	0.0220	2.416625	11.21	-	-	4.40e-06	13.5	inf	-	-	-
-45	0.0225	2.345797	10.44	-	-	4.50e-06	13.7	inf	-	-	-
-46	0.0230	2.501760	12.20	-	-	4.60e-06	13.9	inf	-	-	-
-47	0.0235	2.668490	14.42	-	-	4.70e-06	14.1	inf	-	-	-
-48	0.0240	2.690314	14.74	-	-	4.80e-06	14.3	inf	-	-	-
-49	0.0245	2.478399	11.92	-	-	4.90e-06	14.4	inf	-	-	-
-50	0.0250	2.552420	12.84	-	-	5.00e-06	14.6	inf	-	-	-
-51	0.0255	2.561868	12.96	-	-	5.10e-06	14.8	inf	-	-	-
-52	0.0260	2.713401	15.08	-	-	5.20e-06	15.0	inf	-	-	-
-53	0.0265	2.017108	7.52	-	-	5.30e-06	15.2	inf	-	-	-
-54	0.0270	2.423203	11.28	-	-	5.40e-06	15.4	inf	-	-	-
-55	0.0275	2.463993	11.75	-	-	5.50e-06	15.6	inf	-	-	-
-56	0.0280	2.304244	10.02	-	-	5.60e-06	15.7	inf	-	-	-
-57	0.0285	2.465939	11.77	-	-	5.70e-06	15.9	inf	-	-	-
-58	0.0290	2.597343	13.43	-	-	5.80e-06	16.1	inf	-	-	-
-59	0.0295	2.380273	10.81	-	-	5.90e-06	16.3	inf	-	-	-
-60	0.0300	2.546273	12.76	-	-	6.00e-06	16.5	inf	-	-	-
-61	0.0305	2.363519	10.63	-	-	6.10e-06	16.7	inf	-	-	-
-62	0.0310	2.500315	12.19	-	-	6.20e-06	16.8	inf	-	-	-
-63	0.0315	2.661282	14.31	-	-	6.30e-06	17.0	inf	-	-	-
-64	0.0320	2.550792	12.82	-	-	6.40e-06	17.2	inf	-	-	-
-65	0.0325	2.782503	16.16	-	-	6.50e-06	17.4	inf	-	-	-
-66	0.0330	2.566261	13.02	-	-	6.60e-06	17.6	inf	-	-	-
-67	0.0335	2.422227	11.27	-	-	6.70e-06	17.8	inf	-	-	-
-68	0.0340	2.309755	10.07	-	-	6.80e-06	18.0	inf	-	-	-
-69	0.0345	2.421411	11.26	-	-	6.90e-06	18.1	inf	-	-	-
-70	0.0350	2.337055	10.35	-	-	7.00e-06	18.3	inf	-	-	-
-71	0.0355	2.441586	11.49	-	-	7.10e-06	18.5	inf	-	-	-
-72	0.0360	2.007472	7.44	-	-	7.20e-06	18.7	inf	-	-	-
-73	0.0365	2.359654	10.59	-	-	7.30e-06	18.9	inf	-	-	-
-74	0.0370	2.119129	8.32	-	-	7.40e-06	19.1	inf	-	-	-
-75	0.0375	2.542888	12.72	-	-	7.50e-06	19.3	inf	-	-	-
-76	0.0380	2.760894	15.81	-	-	7.60e-06	19.4	inf	-	-	-
-77	0.0385	2.843285	17.17	-	-	7.70e-06	19.6	inf	-	-	-
-78	0.0390	2.126482	8.39	-	-	7.80e-06	19.8	inf	-	-	-
-79	0.0395	2.277030	9.75	-	-	7.90e-06	20.0	inf	-	-	-
-80	0.0400	2.148972	8.58	-	-	8.00e-06	20.2	inf	-	-	-
-81	0.0405	2.100555	8.17	-	-	8.10e-06	20.4	inf	-	-	-
-82	0.0410	2.352795	10.51	-	-	8.20e-06	20.5	inf	-	-	-
-83	0.0415	2.117337	8.31	-	-	8.30e-06	20.7	inf	-	-	-
-84	0.0420	2.242581	9.42	-	-	8.40e-06	20.9	inf	-	-	-
-85	0.0425	2.714520	15.10	-	-	8.50e-06	21.1	inf	-	-	-
-86	0.0430	2.178751	8.84	-	-	8.60e-06	21.3	inf	-	-	-
-87	0.0435	2.817117	16.73	-	-	8.70e-06	21.5	inf	-	-	-
-88	0.0440	2.150729	8.59	-	-	8.80e-06	21.7	inf	-	-	-
-89	0.0445	2.320984	10.19	-	-	8.90e-06	21.8	inf	-	-	-
-90	0.0450	2.471253	11.84	-	-	9.00e-06	22.0	inf	-	-	-
-91	0.0455	2.643612	14.06	-	-	9.10e-06	22.2	inf	-	-	-
-92	0.0460	2.418486	11.23	-	-	9.20e-06	22.4	inf	-	-	-
-93	0.0465	2.503345	12.22	-	-	9.30e-06	22.6	inf	-	-	-
-94	0.0470	2.722399	15.22	-	-	9.40e-06	22.8	inf	-	-	-
-95	0.0475	2.174699	8.80	-	-	9.50e-06	22.9	inf	-	-	-
-96	0.0480	2.444904	11.53	-	-	9.60e-06	23.1	inf	-	-	-
-97	0.0485	2.241285	9.41	-	-	9.70e-06	23.3	inf	-	-	-
-98	0.0490	2.337248	10.35	-	-	9.80e-06	23.5	inf	-	-	-
-99	0.0495	2.346853	10.45	-	-	9.90e-06	23.7	inf	-	-	-
-100	0.0500	2.563068	12.98	37.18	5.2166	1.00e-05	42.7	37.18	0.0776	0.8447	0.0777
-101	0.0505	2.400863	11.03	-	-	1.00e-05	42.8	37.18	-	-	-
-102	0.0510	2.334385	10.32	-	-	1.00e-05	43.0	37.18	-	-	-
-103	0.0515	2.483816	11.99	-	-	1.00e-05	43.2	37.18	-	-	-
-104	0.0520	2.465145	11.77	-	-	1.00e-05	43.4	37.18	-	-	-
-105	0.0525	2.272185	9.70	-	-	1.00e-05	43.6	37.18	-	-	-
-106	0.0530	2.716387	15.13	-	-	1.00e-05	43.7	37.18	-	-	-
-107	0.0535	2.234694	9.34	-	-	1.00e-05	43.9	37.18	-	-	-
-108	0.0540	2.467305	11.79	-	-	1.00e-05	44.1	37.18	-	-	-
-109	0.0545	2.573664	13.11	-	-	1.00e-05	44.3	37.18	-	-	-
-110	0.0550	2.311522	10.09	-	-	1.00e-05	44.5	37.18	-	-	-
-111	0.0555	2.575843	13.14	-	-	1.00e-05	44.7	37.18	-	-	-
-112	0.0560	2.357507	10.56	-	-	1.00e-05	44.9	37.18	-	-	-
-113	0.0565	2.393637	10.95	-	-	1.00e-05	45.1	37.18	-	-	-
-114	0.0570	2.452413	11.62	-	-	1.00e-05	45.2	37.18	-	-	-
-115	0.0575	2.481568	11.96	-	-	1.00e-05	45.4	37.18	-	-	-
-116	0.0580	2.478903	11.93	-	-	1.00e-05	45.6	37.18	-	-	-
-117	0.0585	2.586261	13.28	-	-	1.00e-05	45.8	37.18	-	-	-
-118	0.0590	2.220206	9.21	-	-	1.00e-05	46.0	37.18	-	-	-
-119	0.0595	2.351057	10.50	-	-	1.00e-05	46.2	37.18	-	-	-
-120	0.0600	2.517451	12.40	-	-	1.00e-05	46.4	37.18	-	-	-
-121	0.0605	2.438140	11.45	-	-	1.00e-05	46.5	37.18	-	-	-
-122	0.0610	2.345435	10.44	-	-	1.00e-05	46.7	37.18	-	-	-
-123	0.0615	2.145411	8.55	-	-	1.00e-05	46.9	37.18	-	-	-
-124	0.0620	2.266543	9.65	-	-	1.00e-05	47.1	37.18	-	-	-
-125	0.0625	2.328250	10.26	-	-	1.00e-05	47.3	37.18	-	-	-
-126	0.0630	2.468649	11.81	-	-	1.00e-05	47.5	37.18	-	-	-
-127	0.0635	2.387323	10.88	-	-	1.00e-05	47.7	37.18	-	-	-
-128	0.0640	2.322604	10.20	-	-	1.00e-05	47.9	37.18	-	-	-
-129	0.0645	1.999412	7.38	-	-	1.00e-05	48.0	37.18	-	-	-
-130	0.0650	2.683703	14.64	-	-	1.00e-05	48.2	37.18	-	-	-
-131	0.0655	2.137464	8.48	-	-	1.00e-05	48.4	37.18	-	-	-
-132	0.0660	2.442773	11.50	-	-	1.00e-05	48.6	37.18	-	-	-
-133	0.0665	2.397190	10.99	-	-	1.00e-05	48.8	37.18	-	-	-
-134	0.0670	2.459330	11.70	-	-	1.00e-05	49.0	37.18	-	-	-
-135	0.0675	2.123795	8.36	-	-	1.00e-05	49.2	37.18	-	-	-
-136	0.0680	2.585186	13.27	-	-	1.00e-05	49.3	37.18	-	-	-
-137	0.0685	2.402543	11.05	-	-	1.00e-05	49.5	37.18	-	-	-
-138	0.0690	2.537756	12.65	-	-	1.00e-05	49.7	37.18	-	-	-
-139	0.0695	2.380743	10.81	-	-	1.00e-05	49.9	37.18	-	-	-
-140	0.0700	2.148084	8.57	-	-	1.00e-05	50.1	37.18	-	-	-
-141	0.0705	2.358355	10.57	-	-	1.00e-05	50.3	37.18	-	-	-
-142	0.0710	2.066343	7.90	-	-	1.00e-05	50.5	37.18	-	-	-
-143	0.0715	2.082654	8.03	-	-	1.00e-05	50.7	37.18	-	-	-
-144	0.0720	2.374614	10.75	-	-	1.00e-05	50.8	37.18	-	-	-
-145	0.0725	2.237202	9.37	-	-	1.00e-05	51.0	37.18	-	-	-
-146	0.0730	2.460353	11.71	-	-	1.00e-05	51.2	37.18	-	-	-
-147	0.0735	2.548951	12.79	-	-	1.00e-05	51.4	37.18	-	-	-
-148	0.0740	2.021847	7.55	-	-	1.00e-05	51.6	37.18	-	-	-
-149	0.0745	2.395192	10.97	-	-	1.00e-05	51.8	37.18	-	-	-
-150	0.0750	2.295523	9.93	-	-	1.00e-05	52.0	37.18	-	-	-
-151	0.0755	2.184924	8.89	-	-	1.00e-05	52.1	37.18	-	-	-
-152	0.0760	2.114951	8.29	-	-	1.00e-05	52.3	37.18	-	-	-
-153	0.0765	2.459696	11.70	-	-	1.00e-05	52.5	37.18	-	-	-
-154	0.0770	2.511945	12.33	-	-	1.00e-05	52.7	37.18	-	-	-
-155	0.0775	2.054245	7.80	-	-	1.00e-05	52.9	37.18	-	-	-
-156	0.0780	2.295519	9.93	-	-	1.00e-05	53.1	37.18	-	-	-
-157	0.0785	2.459937	11.70	-	-	1.00e-05	53.3	37.18	-	-	-
-158	0.0790	2.352649	10.51	-	-	1.00e-05	53.5	37.18	-	-	-
-159	0.0795	2.416194	11.20	-	-	1.00e-05	53.6	37.18	-	-	-
-160	0.0800	2.334132	10.32	-	-	1.00e-05	53.8	37.18	-	-	-
-161	0.0805	2.004742	7.42	-	-	1.00e-05	54.0	37.18	-	-	-
-162	0.0810	2.077888	7.99	-	-	1.00e-05	54.2	37.18	-	-	-
-163	0.0815	2.119277	8.33	-	-	1.00e-05	54.4	37.18	-	-	-
-164	0.0820	2.363844	10.63	-	-	1.00e-05	54.6	37.18	-	-	-
-165	0.0825	2.208542	9.10	-	-	1.00e-05	54.8	37.18	-	-	-
-166	0.0830	2.323340	10.21	-	-	1.00e-05	54.9	37.18	-	-	-
-167	0.0835	2.145709	8.55	-	-	1.00e-05	55.1	37.18	-	-	-
-168	0.0840	2.760295	15.80	-	-	1.00e-05	55.3	37.18	-	-	-
-169	0.0845	2.297471	9.95	-	-	1.00e-05	55.5	37.18	-	-	-
-170	0.0850	1.907239	6.73	-	-	1.00e-05	55.7	37.18	-	-	-
-171	0.0855	2.206612	9.08	-	-	1.00e-05	55.9	37.18	-	-	-
-172	0.0860	2.437925	11.45	-	-	1.00e-05	56.1	37.18	-	-	-
-173	0.0865	2.502681	12.22	-	-	1.00e-05	56.3	37.18	-	-	-
-174	0.0870	2.337579	10.36	-	-	1.00e-05	56.4	37.18	-	-	-
-175	0.0875	2.429755	11.36	-	-	1.00e-05	56.6	37.18	-	-	-
-176	0.0880	2.438525	11.46	-	-	1.00e-05	56.8	37.18	-	-	-
-177	0.0885	2.297938	9.95	-	-	1.00e-05	57.0	37.18	-	-	-
-178	0.0890	2.352155	10.51	-	-	1.00e-05	57.2	37.18	-	-	-
-179	0.0895	2.144866	8.54	-	-	1.00e-05	57.4	37.18	-	-	-
-180	0.0900	2.491884	12.08	-	-	1.00e-05	57.6	37.18	-	-	-
-181	0.0905	2.553859	12.86	-	-	1.00e-05	57.7	37.18	-	-	-
-182	0.0910	2.103667	8.20	-	-	1.00e-05	57.9	37.18	-	-	-
-183	0.0915	2.129826	8.41	-	-	1.00e-05	58.1	37.18	-	-	-
-184	0.0920	2.417928	11.22	-	-	1.00e-05	58.3	37.18	-	-	-
-185	0.0925	2.040138	7.69	-	-	1.00e-05	58.5	37.18	-	-	-
-186	0.0930	2.180190	8.85	-	-	1.00e-05	58.7	37.18	-	-	-
-187	0.0935	2.367704	10.67	-	-	1.00e-05	58.9	37.18	-	-	-
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-373	0.1865	2.650488	14.16	-	-	1.00e-05	131.2	37.18	-	-	-
-374	0.1870	2.137973	8.48	-	-	1.00e-05	131.4	37.18	-	-	-
-375	0.1875	2.781675	16.15	-	-	1.00e-05	131.6	37.18	-	-	-
-376	0.1880	2.273114	9.71	-	-	1.00e-05	131.8	37.18	-	-	-
-377	0.1885	2.462258	11.73	-	-	1.00e-05	132.0	37.18	-	-	-
-378	0.1890	2.863773	17.53	-	-	1.00e-05	132.1	37.18	-	-	-
-379	0.1895	2.584777	13.26	-	-	1.00e-05	132.3	37.18	-	-	-
-380	0.1900	2.302516	10.00	-	-	1.00e-05	132.5	37.18	-	-	-
-381	0.1905	2.261381	9.60	-	-	1.00e-05	132.7	37.18	-	-	-
-382	0.1910	2.456542	11.66	-	-	1.00e-05	132.9	37.18	-	-	-
-383	0.1915	2.496755	12.14	-	-	1.00e-05	133.1	37.18	-	-	-
-384	0.1920	2.586002	13.28	-	-	1.00e-05	133.3	37.18	-	-	-
-385	0.1925	2.400292	11.03	-	-	1.00e-05	133.4	37.18	-	-	-
-386	0.1930	2.700379	14.89	-	-	1.00e-05	133.6	37.18	-	-	-
-387	0.1935	2.507515	12.27	-	-	1.00e-05	133.8	37.18	-	-	-
-388	0.1940	2.521346	12.45	-	-	1.00e-05	134.0	37.18	-	-	-
-389	0.1945	2.189584	8.93	-	-	1.00e-05	134.2	37.18	-	-	-
-390	0.1950	2.475131	11.88	-	-	1.00e-05	134.4	37.18	-	-	-
-391	0.1955	2.449794	11.59	-	-	1.00e-05	134.6	37.18	-	-	-
-392	0.1960	2.440052	11.47	-	-	1.00e-05	134.8	37.18	-	-	-
-393	0.1965	2.481871	11.96	-	-	1.00e-05	134.9	37.18	-	-	-
-394	0.1970	2.413207	11.17	-	-	1.00e-05	135.1	37.18	-	-	-
-395	0.1975	2.676141	14.53	-	-	1.00e-05	135.3	37.18	-	-	-
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-397	0.1985	2.516792	12.39	-	-	1.00e-05	135.7	37.18	-	-	-
-398	0.1990	2.497608	12.15	-	-	1.00e-05	135.9	37.18	-	-	-
-399	0.1995	2.172814	8.78	-	-	1.00e-05	136.1	37.18	-	-	-
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diff --git a/train.py b/train.py
index b6a4e8e..68b4c6b 100644
--- a/train.py
+++ b/train.py
@@ -45,7 +45,10 @@ DEFAULTS = dict(
     quant_warmup_steps=2000,  # lambda warmup over N steps
     activation_bits=16,   # 16 = no activation quant (use 8 for INT8)
     threshold=0.5,        # deadzone threshold (Bonsai: 0.5)
-    soft_quant=True,       # use tanh proxy for smooth gradients
+    soft_quant=False,     # use tanh proxy for smooth gradients
+    warmup_schedule="plateau",  # linear or plateau
+    plateau_steps=500,    # steps per plateau level
+    plateau_max=0.8,      # max lambda for plateau warmup
     # Data
     train_dataset="roneneldan/TinyStories",
     eval_data_path=str(Path(__file__).parent / "data" / "wikitext_eval.json"),
@@ -577,7 +580,15 @@ def train(args):
         step_start = time.time()
 
         # ---- Lambda warmup ----
-        lambda_ = min(step / args.quant_warmup_steps, 1.0)
+        if args.warmup_schedule == "plateau":
+            # Plateau warmup: hold lambda at each level for plateau_steps
+            levels = int(args.plateau_max / 0.05)  # 0.05 increments
+            plateau_size = args.plateau_steps
+            level = min(step // plateau_size, levels)
+            lambda_ = min(level * 0.05, args.plateau_max)
+        else:
+            # Linear warmup
+            lambda_ = min(step / args.quant_warmup_steps, 1.0)
         set_lambda(model, lambda_)
 
         # ---- LR warmup ----
@@ -702,6 +713,9 @@ def main():
     parser.add_argument("--threshold", type=float, default=DEFAULTS["threshold"], dest="threshold")
     parser.add_argument("--soft-quant", action='store_true', default=DEFAULTS["soft_quant"], dest="soft_quant")
     parser.add_argument("--no-soft-quant", action='store_false', dest="soft_quant")
+    parser.add_argument("--warmup-schedule", default=DEFAULTS["warmup_schedule"], dest="warmup_schedule")
+    parser.add_argument("--plateau-steps", type=int, default=DEFAULTS["plateau_steps"], dest="plateau_steps")
+    parser.add_argument("--plateau-max", type=float, default=DEFAULTS["plateau_max"], dest="plateau_max")
     parser.add_argument("--train-dataset", default=DEFAULTS["train_dataset"], dest="train_dataset")
     parser.add_argument("--eval-data-path", default=DEFAULTS["eval_data_path"], dest="eval_data_path")
     parser.add_argument("--log-file", default=DEFAULTS["log_file"])