Severian
/

Jamba-Nexus-4xMoE

@@ -18,13 +18,12 @@ pipeline_tag: text-generation
 # Jamba-Nexus-IKM-v1
-## This model has been trained for 6.3 Epochs (2hrs/~3700 Steps) using Unsloth on the Internal Knowledge Map dataset.
 ---
-### *It works! It just MASSIVLY overfit on the dataset. Jamba has been finicky to train but I think a light rank and a deep alpha might be the trick. The newest version should be up today/tomrrow that hopefully won't overfit*
-*Example output:* "<|startoftext|>How could we use cheese to reignite the sun? Answer: node core, and of for context. - 'Internal Thought Processes and Mapping': Infer the logic or methodology behind formulating responses, drawing on the dataset's structure. - 'Cluster Integration and Interlock Mechanisms': Apply guidelines for tagging, linking, and navigating between clusters, ensuring coherent and scalable analysis. - **Contextual Backgrounds**: Engage with historical, cultural, or situational contexts to provide depth. - **Methodological Approaches**: Deduce reasoning or algorithms behind responses, leveraging computational methods or theoretical frameworks. - **Related Concepts and Terminology**: Explore key terms and concepts, enhancing understanding and connections. - **Real-World Applications**: Identify practical applications or impacts, bridging theory with practicality. - **Real-World Applications**: Identify practical applications or impacts, bridging theory with practicality. - **Real-World Applications**: Identify practical applications or impacts, bridging theory with practicality. - **Real-World Applications**: Identify practical applications or impacts, bridging theory with practicality. - **Real-World Applications**: Identify practical applications or impacts, bridging theory with practicality. - **Real-World Applications**: Identify practical applications or impacts, bridging theory with practicality. - **Real-World Applications**: Identify practical applications or impacts, bridging theory with practicality. - **Real-World Applications**: Identify practical applications or impacts, bridging theory with practicality. - **Real-World Applications**: Identify practical applications or impacts, bridging theory with practicality. - **Real-World Applications**: Identify practical applications or impacts, bridging theory with practicality. - **Real-World Applications**: Identify practical applications or impacts, bridging theory with practicality. - **Real-World Applications**: Identify practical applications or impacts, bridging theory with practicality. - **Real-World Applications**: Identify practical applications or impacts, bridging theory with practicality. - **Real-World Applications**: Identify practical applications or impacts, bridging theory with practicality. - **Real-World Applications**: Identify practical applications or impacts, bridging theory with practicality. - **Real-World Applications**: Identify practical applications or impacts, bridging theory with practicality. - **Real-World Applications**: Identify practical applications or impacts, bridging theory with practicality. - **Real-World Applications**: Identify practical applications or impacts, bridging theory with practicality. - **Real-World Applications**: Identify practical applications or impacts, bridging theory with practicality. - **Real-World Applications**: Identify practical applications or impacts, bridging theory with practicality. - **Real-World"]
 Since this is a base model the IKM dataset greatly affects the output. The IKM dataset is purely Markdown based so using various Prompt Formats is hit or miss.
@@ -76,514 +75,1761 @@ print(tokenizer.batch_decode(outputs))
 ```
 ```
- [3731/5850 3:38:52 < 2:04:22, 0.28 it/s, Epoch 6.37/10]
 Step	Training Loss
-1	10.109800
-2	9.924600
-3	9.919700
-4	9.919100
-5	9.917400
-6	9.895900
-7	9.891700
-8	9.893500
-9	9.917200
-10	9.918800
-11	10.056100
-12	9.916200
-13	9.911200
-14	9.884300
-15	9.909800
-16	9.883800
-17	9.883800
-18	9.878300
-19	9.904400
-20	9.976400
-21	10.061600
-22	10.063300
-23	9.876200
-24	9.890900
-25	9.873100
-26	9.893700
-27	9.869400
-28	9.867100
-29	9.863400
-30	9.910400
-31	9.882300
-32	9.884100
-33	10.023100
-34	9.883500
-35	9.854800
-36	9.847400
-37	9.851400
-38	9.879200
-39	9.845300
-40	9.845700
-41	9.876800
-42	9.844600
-43	9.848000
-44	9.851900
-45	10.038100
-46	9.865000
-47	9.845400
-48	9.838900
-49	9.860100
-50	9.842500
-51	9.830200
-52	10.144100
-53	9.825600
-54	9.832000
-55	9.835000
-56	9.850900
-57	9.990500
-58	10.020100
-59	10.014500
-60	9.849600
-61	9.877500
-62	9.819900
-63	9.818800
-64	9.987100
-65	9.952300
-66	9.861900
-67	9.814100
-68	9.840600
-69	9.809600
-70	9.809600
-71	9.976200
-72	9.810600
-73	9.805900
-74	9.829400
-75	9.830300
-76	9.831500
-77	9.802800
-78	9.798200
-79	9.824900
-80	9.795100
-81	9.794400
-82	9.801200
-83	9.794000
-84	9.820400
-85	9.790100
-86	9.840400
-87	9.809500
-88	9.860000
-89	9.807000
-90	9.948200
-91	9.779500
-92	9.781800
-93	9.802700
-94	9.827700
-95	9.798000
-96	9.825900
-97	9.966000
-98	9.773000
-99	9.775400
-100	9.764400
-101	9.766000
-102	9.817500
-103	9.795200
-104	9.757900
-105	9.753000
-106	9.758200
-107	9.753000
-108	9.751700
-109	9.784200
-110	9.749700
-111	9.748200
-112	9.746200
-113	9.797200
-114	9.747000
-115	9.913200
-116	9.739100
-117	9.769800
-118	9.764500
-119	9.736900
-120	9.760500
-121	9.795500
-122	9.935300
-123	10.079200
-124	9.727200
-125	9.732400
-126	9.755800
-127	9.755500
-128	9.758900
-129	9.732800
-130	9.749600
-131	9.922100
-132	9.719800
-133	9.716600
-134	9.721900
-135	9.718100
-136	9.746300
-137	9.868900
-138	9.740800
-139	9.715600
-140	9.711000
-141	9.744000
-142	9.705100
-143	9.734300
-144	9.881400
-145	9.764000
-146	9.699800
-147	9.855700
-148	9.705600
-149	9.903000
-150	9.697000
-151	9.732500
-152	9.695000
-153	9.901200
-154	9.865600
-155	9.686900
-156	9.890300
-157	9.714300
-158	9.683900
-159	9.856900
-160	10.032500
-161	9.677200
-162	9.683600
-163	9.679800
-164	9.670600
-165	9.698900
-166	9.763100
-167	9.669600
-168	9.713800
-169	9.699100
-170	9.869700
-171	9.844000
-172	9.697700
-173	9.667200
-174	9.692600
-175	9.670400
-176	9.664200
-177	9.689400
-178	9.667900
-179	9.685200
-180	9.664700
-181	9.861600
-182	9.653600
-183	9.652500
-184	9.652700
-185	9.643500
-186	9.675400
-187	9.685200
-188	9.648800
-189	9.671700
-190	9.656900
-191	9.734500
-192	9.637900
-193	9.635800
-194	9.681400
-195	9.669400
-196	9.635200
-197	9.667900
-198	9.662100
-199	9.809700
-200	9.627500
-201	9.691600
-202	9.657200
-203	9.689900
-204	9.633700
-205	9.624900
-206	9.621900
-207	9.655200
-208	9.620300
-209	9.619600
-210	9.616800
-211	9.614600
-212	9.646700
-213	9.612400
-214	9.676200
-215	9.672100
-216	9.788300
-217	9.611000
-218	9.613900
-219	9.632700
-220	9.785800
-221	9.595400
-222	9.599600
-223	9.627600
-224	9.631600
-225	9.627400
-226	9.637000
-227	9.626000
-228	9.600800
-229	9.658900
-230	9.584400
-231	9.621600
-232	9.583600
-233	9.582800
-234	9.613900
-235	9.580700
-236	9.580600
-237	9.580800
-238	9.581300
-239	9.788600
-240	9.574100
-241	9.580500
-242	9.783500
-243	9.574300
-244	9.785300
-245	9.599800
-246	9.565500
-247	9.563900
-248	9.592900
-249	9.592700
-250	9.592200
-251	9.573000
-252	9.769800
-253	9.913400
-254	9.553100
-255	9.549500
-256	9.616300
-257	9.566200
-258	9.766200
-259	9.592900
-260	9.547900
-261	9.576800
-262	9.543000
-263	9.543600
-264	9.978600
-265	9.570100
-266	9.570400
-267	9.716600
-268	9.529900
-269	9.579200
-270	9.545500
-271	9.531600
-272	9.555500
-273	9.559900
-274	9.524000
-275	9.889300
-276	9.553700
-277	9.534400
-278	9.566800
-279	9.518700
-280	9.510600
-281	9.528800
-282	9.545800
-283	9.693700
-284	9.507500
-285	9.511300
-286	9.500100
-3509	6.093600
-3510	6.874700
-3511	6.239500
-3512	6.262400
-3513	6.262000
-3514	6.093200
-3515	6.095400
-3516	6.429600
-3517	6.090800
-3518	6.548000
-3519	6.237100
-3520	6.237000
-3521	6.088900
-3522	6.279700
-3523	7.310300
-3524	6.695300
-3525	6.243000
-3526	6.087100
-3527	6.697000
-3528	6.412400
-3529	6.087100
-3530	6.087000
-3531	6.227500
-3532	6.085900
-3533	6.376200
-3534	6.231600
-3535	6.080500
-3536	6.079100
-3537	6.082800
-3538	6.535800
-3539	6.082300
-3540	6.081300
-3541	6.080600
-3542	6.437900
-3543	6.071800
-3544	6.072500
-3545	6.078300
-3546	6.076700
-3547	6.226500
-3548	6.081000
-3549	6.071000
-3550	6.066900
-3551	6.370600
-3552	6.077900
-3553	6.854100
-3554	6.077300
-3555	6.265500
-3556	6.065600
-3557	6.389000
-3558	6.072500
-3559	6.522500
-3560	6.072400
-3561	6.216900
-3562	6.213700
-3563	6.067200
-3564	6.696500
-3565	6.237500
-3566	6.935300
-3567	6.213700
-3568	6.236400
-3569	6.061000
-3570	7.399200
-3571	6.249000
-3572	6.235700
-3573	6.059400
-3574	6.238300
-3575	6.058600
-3576	6.064600
-3577	6.063100
-3578	6.220400
-3579	6.071700
-3580	6.249400
-3581	6.708400
-3582	6.060400
-3583	6.062800
-3584	6.358300
-3585	6.057700
-3586	6.053700
-3587	6.251000
-3588	6.513700
-3589	6.208500
-3590	7.053200
-3591	6.048200
-3592	6.230400
-3593	6.201200
-3594	7.549800
-3595	6.058900
-3596	6.207100
-3597	6.206900
-3598	6.042500
-3599	6.189200
-3600	6.354800
-3601	6.219600
-3602	6.238400
-3603	6.206500
-3604	7.172000
-3605	6.040700
-3606	6.215000
-3607	6.216300
-3608	6.045200
-3609	7.134800
-3610	6.230800
-3611	6.037500
-3612	6.499700
-3613	6.791900
-3614	6.034000
-3615	6.957900
-3616	6.180000
-3617	6.041000
-3618	6.642900
-3619	6.651100
-3620	6.225300
-3621	6.034700
-3622	6.510700
-3623	6.227100
-3624	6.208200
-3625	6.336000
-3626	6.027800
-3627	6.489200
-3628	6.591400
-3629	6.030200
-3630	6.796800
-3631	6.027400
-3632	6.374700
-3633	6.032100
-3634	6.025900
-3635	6.369400
-3636	6.634500
-3637	6.481200
-3638	6.220300
-3639	6.217200
-3640	6.025200
-3641	6.016900
-3642	6.491400
-3643	6.025600
-3644	6.483400
-3645	6.478600
-3646	6.387600
-3647	6.168300
-3648	6.654600
-3649	6.809700
-3650	6.193000
-3651	6.194500
-3652	6.349200
-3653	6.172500
-3654	6.174200
-3655	6.014800
-3656	6.626400
-3657	6.011500
-3658	6.162000
-3659	6.504300
-3660	7.084900
-3661	6.622300
-3662	6.470700
-3663	6.011600
-3664	6.188300
-3665	6.198700
-3666	6.009900
-3667	6.644700
-3668	6.185000
-3669	6.008600
-3670	6.005900
-3671	6.009200
-3672	6.614900
-3673	6.198300
-3674	6.933100
-3675	6.171800
-3676	6.147500
-3677	6.464300
-3678	6.009500
-3679	6.371400
-3680	6.162100
-3681	5.998900
-3682	6.645100
-3683	6.192900
-3684	6.813800
-3685	6.331100
-3686	6.832200
-3687	6.480900
-3688	5.993200
-3689	6.156100
-3690	6.172600
-3691	6.185400
-3692	5.999600
-3693	6.151900
-3694	6.187100
-3695	6.459900
-3696	5.993100
-3697	5.989900
-3698	6.348300
-3699	5.992500
-3700	5.995900
-3701	5.994900
-3702	5.984900
-3703	6.161600
-3704	6.170100
-3705	6.507000
-3706	5.989200
-3707	6.138800
-3708	6.890600
-3709	5.984500
-3710	6.157900
-3711	5.991600
-3712	5.992200
-3713	6.135400
-3714	6.133900
-3715	6.164000
-3716	5.988100
-3717	6.351000
-3718	5.981300
-3719	5.981000
-3720	7.087300
-3721	6.135400
-3722	6.280900
-3723	5.982800
-3724	5.983800
-3725	6.350100
-3726	6.618500
-3727	6.600100
-3728	6.440600
-3729	5.973800
 ```

 # Jamba-Nexus-IKM-v1
+## This model has been trained for 3 Epochs on the Internal Knowledge Map dataset.
 ---
+### *It works! The newest version should be up today/tomrrow to continue iterations*
 Since this is a base model the IKM dataset greatly affects the output. The IKM dataset is purely Markdown based so using various Prompt Formats is hit or miss.
 ```
 ```
+  [1755/1755 1:42:16, Epoch 2/3]
 Step	Training Loss
+1	1.456100
+2	0.590900
+3	0.305100
+4	0.304800
+5	0.284000
+6	0.117600
+7	0.118000
+8	0.117600
+9	0.283900
+10	0.424800
+11	1.124300
+12	0.592600
+13	0.659000
+14	0.118300
+15	0.305900
+16	0.117500
+17	0.257400
+18	0.117300
+19	0.305000
+20	0.904200
+21	1.292100
+22	1.291700
+23	0.116800
+24	0.284800
+25	0.258200
+26	0.305400
+27	0.118900
+28	0.116400
+29	0.256900
+30	0.759900
+31	0.283900
+32	0.284800
+33	1.124300
+34	0.306200
+35	0.257500
+36	0.117800
+37	0.118600
+38	0.303900
+39	0.117400
+40	0.115800
+41	0.591000
+42	0.117500
+43	0.117100
+44	0.117300
+45	1.738400
+46	0.591900
+47	0.258700
+48	0.117700
+49	0.658900
+50	0.117800
+51	0.118900
+52	2.821700
+53	0.117200
+54	0.118100
+55	0.118200
+56	0.660200
+57	1.124800
+58	1.312800
+59	1.290300
+60	0.284300
+61	0.450600
+62	0.117300
+63	0.117700
+64	1.124400
+65	2.297300
+66	0.449500
+67	0.119300
+68	0.305400
+69	0.116400
+70	0.116800
+71	1.123900
+72	0.117700
+73	0.118100
+74	0.306400
+75	0.445500
+76	0.306300
+77	0.117500
+78	0.118000
+79	0.801100
+80	0.117000
+81	0.118400
+82	0.117300
+83	0.118800
+84	0.445500
+85	0.119000
+86	0.848200
+87	0.305700
+88	0.925500
+89	0.664300
+90	1.123900
+91	0.117400
+92	0.117700
+93	0.304800
+94	0.472600
+95	0.284500
+96	0.780400
+97	1.289100
+98	0.119300
+99	0.118500
+100	0.118800
+101	0.118900
+102	0.827000
+103	0.445700
+104	0.118400
+105	0.118200
+106	0.118100
+107	0.117900
+108	0.117900
+109	0.424300
+110	0.116600
+111	0.116400
+112	0.117500
+113	1.202300
+114	0.116700
+115	1.123800
+116	0.118200
+117	0.306000
+118	0.283600
+119	0.116300
+120	0.657500
+121	0.780100
+122	1.312900
+123	2.132200
+124	0.117500
+125	0.118300
+126	0.305900
+127	0.283500
+128	0.801700
+129	0.257800
+130	0.592400
+131	1.597800
+132	0.117800
+133	0.118600
+134	0.117500
+135	0.117100
+136	0.660600
+137	1.909300
+138	0.306300
+139	0.118600
+140	0.116700
+141	0.446600
+142	0.117800
+143	0.305700
+144	1.124000
+145	0.493500
+146	0.115900
+147	1.906300
+148	0.258000
+149	1.310900
+150	0.118600
+151	0.445900
+152	0.117600
+153	1.431800
+154	1.123900
+155	0.117800
+156	1.666200
+157	0.592900
+158	0.118600
+159	1.124600
+160	2.130000
+161	0.117800
+162	0.117000
+163	0.117700
+164	0.118000
+165	0.658300
+166	1.015000
+167	0.118100
+168	0.425100
+169	0.306500
+170	1.191300
+171	1.124700
+172	0.305800
+173	0.116800
+174	0.284400
+175	0.258900
+176	0.118300
+177	0.283500
+178	0.257500
+179	0.592300
+180	0.257700
+181	1.312100
+182	0.116300
+183	0.117000
+184	0.116900
+185	0.117500
+186	0.593000
+187	0.445900
+188	0.118100
+189	0.592100
+190	0.256700
+191	1.253500
+192	0.117700
+193	0.116600
+194	0.424100
+195	0.306100
+196	0.117500
+197	0.305100
+198	0.591300
+199	1.123600
+200	0.117200
+201	1.064900
+202	0.659000
+203	1.134200
+204	0.257400
+205	0.117800
+206	0.116900
+207	0.657700
+208	0.116100
+209	0.116200
+210	0.116900
+211	0.116900
+212	0.592000
+213	0.116700
+214	1.134200
+215	1.067300
+216	1.123300
+217	0.118200
+218	0.257800
+219	0.659500
+220	1.123700
+221	0.118000
+222	0.117300
+223	0.592300
+224	0.305700
+225	0.304900
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 ```