--- library_name: transformers pipeline_tag: text-generation license: mit datasets: - Severian/Internal-Knowledge-Map --- # New Fixed Version with extended training being uploaded right now! ## GGUF Q8 Version: https://huggingface.co/Severian/Nexus-IKM-Mistral-7B-GGUF **If you'd like to train your own version, here is the full notebook to recreate the training on Unsloth yourself (https://colab.research.google.com/drive/1828t77iO2nLRXVfB8HoI11eFu-79-Oe7?usp=sharing). You'll just have to drop in the train.jsonl from the Dataset repo (https://huggingface.co/datasets/Severian/Internal-Knowledge-Map) into your Colab directory and rename it dataset.jsonl** This model is the second trained with experimental 'Internal Knowledge Map' dataset. Developed with an aim to go beyond the scope of usual data processing capabilities, this model gets trained to build comprehensive understanding and reasoning in a wide range of knowledge domains with elaborate guidelines. It bases its reasoning on a specially selected dataset emphasizing the interrelations of the diverse disciplines which aim to synthesize, integrate, and apply complex information in ways that mimic humanly abstract reasoning and creative thought processes. At the very core of the development of this model is the desire to make sure that LLMs engage in a kind of cognitive activity not limited to memory but actually taking on abstract reasoning, problem-solving, and generation of new insights. To achieve this, 'Nexus-IKM-Mistral-7B' has been fine-tuned until convergance at ~15 Epochs on this unique dataset, which resulted in the model demonstrating greater capability for giving rise to insights and problem-solving in complex, multi-disciplinary settings. This involves improved ability in drawing links between different pieces of knowledge, reasoning through complex scenarios, and proposing innovative solutions that cut across various domains, including science, technology, environmental studies, and humanities. Test this out and see if you find anything interesting or intriguing. I will keep iterating more versions but this one seems like a fun and useful way to start. # Training Snaphot ``` Step Training Loss 1 3.223000 2 3.221300 3 3.215900 4 3.210600 5 3.203000 6 3.193500 7 3.184000 8 3.173400 9 3.162400 10 3.151500 11 3.140500 12 3.128800 13 3.117600 14 3.106700 15 3.095500 16 3.084700 17 3.073700 18 3.062700 19 3.052300 20 3.041800 201 1.273200 202 1.257600 203 1.241900 204 1.226100 205 1.210800 206 1.195500 207 1.180800 208 1.166000 209 1.151200 210 1.136900 211 1.122000 212 1.106600 213 1.091200 214 1.075200 215 1.059200 216 1.042900 217 1.026600 218 1.010300 219 0.994200 416 0.041700 417 0.041700 418 0.041600 419 0.041600 420 0.041600 421 0.041600 422 0.041500 423 0.041500 424 0.041500 425 0.041400 426 0.041400 427 0.041400 668 0.035200 669 0.035100 670 0.035100 671 0.035100 672 0.035100 673 0.035000 674 0.035000 675 0.035000 676 0.035000 677 0.034900 678 0.034900 679 0.034900 680 0.034800 681 0.034800 682 0.034800 683 0.034800 684 0.034800 685 0.034700 686 0.034700 1209 0.006600 1210 0.006500 1211 0.006300 1212 0.006200 1213 0.006100 1214 0.006000 1215 0.005800 1216 0.005700 1217 0.005600 1218 0.005500 1219 0.005400 1220 0.005300 1221 0.005100 1222 0.004900 1223 0.004800 1224 0.004700 1225 0.004600 1226 0.004500 1227 0.004400 1228 0.004300 1229 0.004200 1230 0.004000 1231 0.003900 1232 0.003800 1233 0.003700 1234 0.003500 1235 0.003400 1236 0.003300 1237 0.003200 1238 0.003000 1239 0.003000 1240 0.002900 1241 0.002800 1242 0.002700 1243 0.002600 1244 0.002500 1245 0.002400 1246 0.002300 1247 0.002200 1248 0.002100 1249 0.002000 1250 0.001900 1251 0.001800 1252 0.001800 1253 0.001700 1254 0.001600 1255 0.001600 1256 0.001500 1257 0.001400 1258 0.001300 1259 0.001300 1260 0.001200 1261 0.001200 1262 0.001100 1263 0.001100 1264 0.001000 1265 0.001000 1266 0.000900 1267 0.000900 1268 0.000800 1269 0.000800 1270 0.000800 1271 0.000800 1272 0.000700 1273 0.000700 1274 0.000700 1275 0.000600 1276 0.000600 1277 0.000600 1278 0.000600 1279 0.000500 1280 0.000500 1281 0.000500 1282 0.000500 1283 0.000500 1284 0.000500 1285 0.000500 1286 0.000400 1287 0.000400 1288 0.000400 1289 0.000400 1290 0.000400 1291 0.000400 1292 0.000400 1293 0.000400 1294 0.000400 1295 0.000400 1296 0.000400 1297 0.000300 1298 0.000300 ```