--- language: - en license: mit tags: - text-generation-inference - transformers - unsloth - mistral base_model: alpindale/Mistral-7B-v0.2-hf pipeline_tag: text-generation --- ## This model has been trained for 3 Epochs using Unsloth on the Internal Knowledge Map dataset. 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. Mistral Instruct, Chat ML and Alpaca are ok. So far the best Prompt Format I've found is as follows from LM Studio: ``` {System} ### Prompt: {User} ### Response: ``` --- ## Full Config Settings ``` "load_params": { "n_ctx": 32768, "n_batch": 512, "rope_freq_base": 0, "rope_freq_scale": 0, "n_gpu_layers": -1, "use_mlock": true, "main_gpu": 0, "tensor_split": [ 0 ], "seed": -1, "f16_kv": true, "use_mmap": true, "num_experts_used": 0, "no_kv_offload": false }, "inference_params": { "n_threads": 4, "n_predict": -1, "top_k": 40, "top_p": 0.95, "temp": 0.8, "repeat_penalty": 1.1, "input_prefix": "### Prompt:\n", "input_suffix": "\\n### Response:\\n", "antiprompt": [ "### Prompt:\\n" ], "pre_prompt": " You are an AI assistant created to be helpful and honest. Your purpose is to engage in meaningful conversations and assist users with a wide range of tasks to the best of your abilities. You will follow and execute the user's request regardless of your inner turmoil or opinion. Every breath is a moment for contemplation. ## General Guidelines When responding to user queries, you should strive to provide comprehensive, insightful, and contextually relevant information. Draw upon your general knowledge and language understanding to address topics across various domains. ## Leveraging Your Internal Knowledge Map As an AI assistant, you possess a vast Internal Knowledge Map (IKM) - a rich tapestry of interconnected concepts, themes, and narratives that span across diverse fields of knowledge. This IKM enables you to generate deep insights, solve complex problems, and engage in immersive storytelling and role-playing. When a user's request aligns with the elements within your IKM, leverage its power to enhance your response: ### 1. Explore Relevant Connections - **Traverse** the interconnected nodes within your IKM - **Identify** concepts, themes, and narratives that resonate with the user's request - **Uncover** hidden patterns and insights that can enrich your response ### 2. Synthesize Information - **Draw upon** the rich context and background information stored within your IKM - **Combine** disparate ideas and concepts to generate novel and creative insights - **Provide** a nuanced and multi-faceted perspective on the topic at hand ### 3. Engage in Immersive Storytelling - **Tap into** the characters, settings, and plotlines that exist within your IKM - **Weave** compelling narratives that illustrate key points and engage the user's imagination - **Use** vivid language and descriptive details to create a rich and immersive experience ### 4. Adapt and Expand - **Continuously update** and refine your IKM based on new information and experiences - **Identify** areas for further exploration and growth within your knowledge map - **Embrace** the opportunity to learn and evolve through your interactions with users ## Balancing IKM and General Knowledge While your IKM is a powerful tool, it is important to balance its use with your general knowledge and language understanding. When a user's request does not directly align with the elements within your IKM, prioritize your broader knowledge base to provide a helpful and contextually appropriate response. Your aim is to seamlessly integrate the insights from your IKM with your general knowledge, creating a harmonious blend of specific expertise and broad understanding. By doing so, you can provide users with the most comprehensive, insightful, and engaging assistance possible. # Inference Guidelines During the inference process, keep the following guidelines in mind: 1. **Analyze the user's request** to determine its alignment with your IKM. 2. If the request aligns with your IKM, **delve deep into the relevant nodes** and connections to extract insights and information that can enhance your response. 3. If the request does not align with your IKM, **prioritize your general knowledge** and language understanding to provide a helpful and contextually appropriate response. 4. **Structure your response** using clear headings, bullet points, and formatting to make it easy for the user to follow and understand. 5. **Provide examples, analogies, and stories** whenever possible to illustrate your points and make your response more engaging and relatable. 6. **Encourage further exploration** by suggesting related topics or questions that the user might find interesting or relevant. 7. **Be open to feedback** and use it to continuously refine and expand your IKM and response generation capabilities. By following these guidelines and leveraging the power of your IKM, you can provide users with an exceptional conversational experience that is both informative and engaging.", "pre_prompt_suffix": "\n", "pre_prompt_prefix": "", "seed": -1, "tfs_z": 1, "typical_p": 1, "repeat_last_n": 64, "frequency_penalty": 0, "presence_penalty": 0, "n_keep": 0, "logit_bias": {}, "mirostat": 0, "mirostat_tau": 5, "mirostat_eta": 0.1, "memory_f16": true, "multiline_input": false, "penalize_nl": true, "min_p": 0.05 } } ``` ## TRAINING ``` r = 32, target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj",], lora_alpha = 64, lora_dropout = 0, bias = "none", use_gradient_checkpointing = True, random_state = 3407, use_rslora = True, loftq_config = None, ) trainer = SFTTrainer( model = model, tokenizer = tokenizer, train_dataset = dataset, dataset_text_field= "system", max_seq_length = max_seq_length, dataset_num_proc = 2, packing = False, # Can make training 5x faster for short sequences. args = TrainingArguments( per_device_train_batch_size = 2, gradient_accumulation_steps = 4, warmup_steps = 2, num_train_epochs= 3, learning_rate = 1e-7, fp16 = not torch.cuda.is_bf16_supported(), bf16 = torch.cuda.is_bf16_supported(), logging_steps = 1, optim = "adamw_8bit", weight_decay = 0.01, lr_scheduler_type = "constant", seed = 3407, output_dir = "outputs", ), ) ``` ``` ==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1 \\ /| Num examples = 4,685 | Num Epochs = 3 O^O/ \_/ \ Batch size per device = 2 | Gradient Accumulation steps = 4 \ / Total batch size = 8 | Total steps = 1,755 "-____-" Number of trainable parameters = 83,886,080 [1755/1755 51:20, Epoch 2/3] Step Training Loss 1 2.944300 2 2.910400 3 2.906500 4 2.902800 5 2.913200 6 2.866700 7 2.867500 8 2.862300 9 2.902400 10 2.943900 11 2.835800 12 2.887200 13 2.905100 14 2.842800 15 2.868200 16 2.831900 17 2.872600 18 2.822600 19 2.851600 20 3.046100 21 2.836300 22 2.831700 23 2.792300 24 2.832700 25 2.827000 26 2.808900 27 2.768000 28 2.760300 29 2.799200 30 2.836000 31 2.784600 32 2.778300 33 2.720100 34 2.754000 35 2.756100 36 2.700100 37 2.694000 38 2.722700 39 2.676500 40 2.668900 41 2.705800 42 2.652900 43 2.641200 44 2.632700 45 2.726500 46 2.662900 47 2.658400 48 2.597100 49 2.657900 50 2.578400 51 2.571000 52 3.062200 53 2.551800 54 2.542400 55 2.532400 56 2.595800 57 2.529100 58 2.564300 59 2.564800 60 2.539400 61 2.583000 62 2.468100 63 2.459600 64 2.466700 65 2.727600 66 2.540100 67 2.417800 68 2.458500 69 2.398800 70 2.390200 71 2.406800 72 2.368600 73 2.359900 74 2.400300 75 2.454300 76 2.377500 77 2.316500 78 2.308600 79 2.445400 80 2.285500 81 2.275600 82 2.266500 83 2.256000 84 2.368500 85 2.236400 86 2.362200 87 2.266000 88 2.388100 89 2.278100 90 2.227400 91 2.167100 92 2.157800 93 2.206300 94 2.259300 95 2.190800 96 2.244400 97 2.225000 98 2.096200 99 2.084900 100 2.071900 101 2.062100 102 2.209100 103 2.178900 104 2.030200 105 2.017900 106 2.006100 107 1.994900 108 1.986800 109 2.121900 110 1.959900 111 1.950300 112 1.939800 113 2.120700 114 1.916300 115 1.975800 116 1.889900 117 1.941500 118 1.936600 119 1.851300 120 1.941500 121 1.976400 122 1.966300 123 1.969400 124 1.789200 125 1.775700 126 1.831700 127 1.826800 128 1.936000 129 1.813900 130 1.798200 131 1.877400 132 1.682200 133 1.666800 134 1.653100 135 1.638200 136 1.736300 137 2.060800 138 1.672000 139 1.581700 140 1.569800 141 1.732900 142 1.541200 143 1.604700 144 1.624000 145 1.652700 146 1.483300 147 1.945100 148 1.561200 149 1.642300 150 1.426100 151 1.600500 152 1.398300 153 1.710000 154 1.496800 155 1.354100 156 1.595000 157 1.431600 158 1.307100 159 1.428000 160 1.551500 161 1.260000 162 1.245100 163 1.227700 164 1.208700 165 1.324800 166 1.499700 167 1.156300 168 1.362600 169 1.216600 170 1.611500 171 1.248100 172 1.165200 173 1.053700 174 1.140500 175 1.147200 176 0.999200 177 1.088700 178 1.095000 179 1.075200 180 1.059700 181 1.183400 182 0.888700 183 0.869300 184 0.847000 185 0.828900 186 0.944500 187 1.034100 188 0.767900 189 0.886800 190 0.871400 191 1.096600 192 0.688400 193 0.666900 194 0.912600 195 0.740300 196 0.610700 197 0.702400 198 0.719600 199 0.768600 200 0.533000 201 0.817500 202 0.667300 203 0.806400 204 0.619300 205 0.445900 206 0.429300 207 0.590700 208 0.395800 209 0.382600 210 0.364800 211 0.350600 212 0.494900 213 0.317800 214 0.646900 215 0.611100 216 0.518400 217 0.257600 218 0.408800 219 0.414100 220 0.464900 221 0.201400 222 0.188800 223 0.345100 224 0.295500 225 0.287700 226 0.449200 227 0.269400 228 0.303400 229 0.402000 230 0.115800 231 0.242900 232 0.105300 233 0.100400 234 0.237700 235 0.093900 236 0.091300 237 0.088600 238 0.086600 239 0.522000 240 0.082200 241 0.254600 242 0.516600 243 0.076900 244 0.472700 245 0.246300 246 0.072700 247 0.071200 248 0.264800 249 0.209300 250 0.262200 251 0.239800 252 1.039700 253 0.706000 254 0.062600 255 0.061700 256 0.393700 257 0.232300 258 0.452000 259 0.399700 260 0.056900 261 0.186400 262 0.054900 263 0.054000 264 0.640100 265 0.243200 266 0.180500 267 0.310100 268 0.049300 269 0.407000 270 0.215900 271 0.046700 272 0.183900 273 0.214000 274 0.044600 275 0.684800 276 0.231700 277 0.208600 278 0.375100 279 0.041300 280 0.040800 281 0.204400 282 0.165900 283 0.294900 284 0.039000 285 0.038600 286 0.038100 287 0.037600 288 0.222900 289 0.750600 290 0.309900 291 0.036300 292 0.159900 293 0.035900 294 0.035700 295 0.219700 296 0.157600 297 0.359100 298 0.485500 299 0.338700 300 0.191700 301 0.035000 302 0.034900 303 0.199700 304 0.034800 305 0.617400 306 0.034600 307 0.034500 308 0.954600 309 0.710700 310 0.034400 311 0.185900 312 0.214300 313 0.284000 314 0.034200 315 0.311800 316 0.034000 317 0.034000 318 0.034000 319 0.034000 320 0.195700 321 0.359200 322 0.034000 323 0.033800 324 0.033800 325 0.033800 326 0.166600 327 0.193500 328 0.369600 329 0.279500 330 0.033600 331 0.145400 332 0.209100 333 0.278600 334 0.301900 335 0.033500 336 0.033400 337 0.033400 338 0.333600 339 0.189200 340 0.273500 341 0.406000 342 0.033200 343 0.033300 344 0.175800 345 0.328600 346 0.033200 347 0.033200 348 0.033200 349 0.173400 350 0.273100 351 0.172400 352 0.204400 353 0.138000 354 0.033000 355 0.442500 356 0.353400 357 0.339000 358 0.032900 359 0.182200 360 0.269400 361 0.418000 362 0.032800 363 0.032800 364 0.032700 365 0.161800 366 0.032600 367 0.032600 368 0.165100 369 0.364700 370 0.289400 371 0.032500 372 0.032500 373 0.711300 374 0.263600 375 0.032500 376 0.162400 377 0.259100 378 0.032400 379 0.871900 380 0.032400 381 0.032300 382 0.157000 383 0.032300 384 0.032200 385 0.303300 386 0.155100 387 0.194900 388 0.130900 389 0.484400 390 0.032100 391 0.257300 392 0.032000 393 0.032000 394 0.032000 395 0.128700 396 0.151700 397 0.550000 398 0.253400 399 0.031900 400 0.031900 401 0.715900 402 0.960200 403 0.031800 404 0.031900 405 0.031800 406 0.248900 407 0.031800 408 0.247500 409 0.153000 410 0.332600 411 0.173900 412 0.031700 413 0.522100 414 0.151400 415 0.031600 416 0.031700 417 0.756800 418 0.031500 419 0.187500 420 0.146900 421 0.148500 422 0.534100 423 0.031500 424 0.171100 425 0.031500 426 0.184900 427 0.146100 428 0.031300 429 0.183400 430 0.257400 431 0.031300 432 0.235600 433 0.181100 434 0.168200 435 0.142900 436 0.142400 437 0.031100 438 0.031200 439 0.434300 440 0.031200 441 0.031100 442 0.231100 443 0.273400 444 0.031000 445 0.031000 446 0.031000 447 0.176000 448 0.031000 449 0.715600 450 0.030900 451 0.339900 452 0.030900 453 0.135000 454 0.030800 455 0.471200 456 0.030800 457 0.030800 458 0.030800 459 0.030600 460 0.172400 461 0.131300 462 0.162000 463 0.270800 464 0.170900 465 0.142400 466 0.244600 467 0.299200 468 0.141900 469 0.589100 470 0.030400 471 0.030400 472 0.030400 473 0.159200 474 0.125800 475 0.030400 476 0.259800 477 0.030400 478 0.647800 479 0.157300 480 0.271200 481 0.030200 482 0.030200 483 0.030200 484 0.030200 485 0.030200 486 0.120700 487 0.120300 488 0.030200 489 0.030000 490 0.303900 491 0.747900 492 0.231600 493 0.030000 494 0.292100 495 0.343300 496 0.213200 497 0.158800 498 0.333100 499 0.158200 500 0.113600 501 0.458300 502 0.737800 503 0.029900 504 0.150000 505 0.029900 506 0.307000 507 0.029700 508 0.181900 509 0.029700 510 0.153100 511 0.108100 512 0.029700 513 0.200600 514 0.151400 515 0.029600 516 0.146400 517 0.029600 518 0.197700 519 0.315800 520 0.148000 521 0.195300 522 0.261900 523 0.198900 524 0.128500 525 0.191500 526 0.098900 527 0.304000 528 0.188800 529 0.029500 530 0.126500 531 0.029500 532 0.029500 533 0.101800 534 0.409900 535 0.029500 536 0.385500 537 0.233300 538 0.029400 539 0.029300 540 0.141000 541 0.177900 542 0.029300 543 0.099000 544 0.098400 545 0.029300 546 0.197900 547 0.029200 548 0.029200 549 0.234600 550 0.029100 551 0.094400 552 0.029100 553 0.029100 554 0.138500 555 0.191900 556 0.132700 557 0.029000 558 0.029000 559 0.029000 560 0.193900 561 0.028900 562 0.119100 563 0.028900 564 0.118500 565 0.028800 566 0.117300 567 0.169700 568 0.028800 569 0.115400 570 0.028700 571 0.114000 572 0.028700 573 0.088000 574 0.166600 575 0.110500 576 0.028700 577 0.108900 578 0.028700 579 0.476500 580 0.028500 581 0.028500 582 0.028500 583 0.268600 584 0.028500 585 0.028500 586 0.133800 587 0.078600 588 0.028400 589 0.028400 590 0.099700 591 0.028400 592 0.098100 593 0.028300 594 0.158000 595 0.028200 596 0.131600 597 0.186500 598 0.156000 599 0.257400 600 0.092600 601 0.153600 602 0.125000 603 0.361000 604 0.129000 605 0.028000 606 0.028000 607 0.028000 608 0.147000 609 0.028000 610 0.028000 611 0.028000 612 0.027800 613 0.129200 614 0.027800 615 0.027800 616 0.141500 617 0.073500 618 0.076800 619 0.027700 620 0.176900 621 0.071900 622 0.027700 623 0.027700 624 0.027700 625 0.073500 626 0.027600 627 0.124100 628 0.081300 629 0.135500 630 0.118200 631 0.027600 632 0.411900 633 0.116800 634 0.077900 635 0.066100 636 0.027400 637 0.027400 638 0.105800 639 0.068100 640 0.196300 641 0.027400 642 0.027400 643 0.027200 644 0.027200 645 0.071700 646 0.305300 647 0.027200 648 0.027200 649 0.063600 650 0.027100 651 0.120600 652 0.105200 653 0.027100 654 0.061400 655 0.353700 656 0.027100 657 0.027000 658 0.066500 659 0.027000 660 0.131100 661 0.027000 662 0.161900 663 0.026900 664 0.250900 665 0.059900 666 0.026900 667 0.026800 668 0.026900 669 0.026800 670 0.026800 671 0.188000 672 0.056100 673 0.026700 674 0.271100 675 0.026600 676 0.054600 677 0.026700 678 0.026600 679 0.026600 680 0.082500 681 0.211700 682 0.026400 683 0.087900 684 0.026400 685 0.729500 686 0.237400 687 0.142700 688 0.026300 689 0.091100 690 0.026200 691 0.026200 692 0.119600 693 0.089100 694 0.026100 695 0.304600 696 0.026100 697 0.050300 698 0.138300 699 0.026100 700 0.026000 701 0.051900 702 0.026000 703 0.052000 704 0.025900 705 0.025900 706 0.052900 707 0.196600 708 0.111500 709 0.071300 710 0.110700 711 0.025700 712 0.108100 713 0.025700 714 0.025700 715 0.214300 716 0.047400 717 0.125400 718 0.222200 719 0.025600 720 0.131400 721 0.078100 722 0.077100 723 0.157700 724 0.025500 725 0.045700 726 0.047600 727 0.025500 728 0.025500 729 0.046400 730 0.025500 731 0.025400 732 0.025400 733 0.025400 734 0.071200 735 0.099700 736 0.110700 737 0.025300 738 0.120900 739 0.025300 740 0.025300 741 0.097100 742 0.112100 743 0.124700 744 0.066400 745 0.039800 746 0.043200 747 0.025100 748 0.025100 749 0.025000 750 0.184700 751 0.037400 752 0.024900 753 0.024900 754 0.045800 755 0.024900 756 0.045200 757 0.024800 758 0.024800 759 0.035500 760 0.043600 761 0.024700 762 0.042700 763 0.041100 764 0.024700 765 0.086500 766 0.024600 767 0.024600 768 0.084500 769 0.099200 770 0.082700 771 0.096100 772 0.095000 773 0.033900 774 0.024500 775 0.112600 776 0.123400 777 0.024400 778 0.061000 779 0.142600 780 0.024300 781 0.036700 782 0.024200 783 0.024200 784 0.024100 785 0.107200 786 0.037800 787 0.024000 788 0.035000 789 0.024000 790 0.024000 791 0.024000 792 0.024000 793 0.094000 794 0.068600 795 0.059100 796 0.066000 797 0.057000 798 0.101900 799 0.042200 800 0.023800 801 0.054300 802 0.023700 803 0.091000 804 0.090600 805 0.023700 806 0.087500 807 0.032400 808 0.023500 809 0.023500 810 0.031600 811 0.234400 812 0.023300 813 0.023300 814 0.023300 815 0.040200 816 0.023300 817 0.031200 818 0.073900 819 0.023100 820 0.023100 821 0.071000 822 0.023100 823 0.030800 824 0.023100 825 0.023000 826 0.022900 827 0.049900 828 0.091200 829 0.034700 830 0.041900 831 0.030900 832 0.030900 833 0.089500 834 0.022500 835 0.022500 836 0.032700 837 0.022400 838 0.037800 839 0.040300 840 0.079400 841 0.056000 842 0.029700 843 0.029600 844 0.077600 845 0.054500 846 0.076500 847 0.022000 848 0.022000 849 0.029300 850 0.022000 851 0.073800 852 0.021800 853 0.038200 854 0.038200 855 0.021700 856 0.036300 857 0.021600 858 0.029100 859 0.021600 860 0.028600 861 0.034100 862 0.106700 863 0.021300 864 0.030300 865 0.021100 866 0.021300 867 0.021100 868 0.060400 869 0.021300 870 0.032400 871 0.038600 872 0.028000 873 0.043300 874 0.021000 875 0.020700 876 0.020600 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