Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) sparse_mistral_7b_refined_web_50p_2024-04-13 - GGUF - Model creator: https://huggingface.co/thrunlab/ - Original model: https://huggingface.co/thrunlab/sparse_mistral_7b_refined_web_50p_2024-04-13/ | Name | Quant method | Size | | ---- | ---- | ---- | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q2_K.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q2_K.gguf) | Q2_K | 2.53GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.IQ3_XS.gguf) | IQ3_XS | 2.81GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.IQ3_S.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.IQ3_S.gguf) | IQ3_S | 2.96GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q3_K_S.gguf) | Q3_K_S | 2.95GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.IQ3_M.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.IQ3_M.gguf) | IQ3_M | 3.06GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q3_K.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q3_K.gguf) | Q3_K | 3.28GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q3_K_M.gguf) | Q3_K_M | 3.28GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q3_K_L.gguf) | Q3_K_L | 3.56GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.IQ4_XS.gguf) | IQ4_XS | 3.67GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_0.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_0.gguf) | Q4_0 | 3.83GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.IQ4_NL.gguf) | IQ4_NL | 3.87GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_K_S.gguf) | Q4_K_S | 3.86GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_K.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_K.gguf) | Q4_K | 4.07GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_K_M.gguf) | Q4_K_M | 4.07GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_1.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_1.gguf) | Q4_1 | 4.24GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_0.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_0.gguf) | Q5_0 | 4.65GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_K_S.gguf) | Q5_K_S | 4.65GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_K.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_K.gguf) | Q5_K | 4.78GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_K_M.gguf) | Q5_K_M | 4.78GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_1.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_1.gguf) | Q5_1 | 5.07GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q6_K.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q6_K.gguf) | Q6_K | 5.53GB | Original model description: --- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - generated_from_trainer model-index: - name: sparse_mistral_7b_refined_web_50p_2024-04-13 results: [] --- # sparse_mistral_7b_refined_web_50p_2024-04-13 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1985 ## 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: 1e-05 - train_batch_size: 1 - eval_batch_size: 4 - seed: 0 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 2350 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.3391 | 0.01 | 25 | 2.4196 | | 2.2711 | 0.02 | 50 | 2.3577 | | 2.3054 | 0.02 | 75 | 2.3158 | | 2.2795 | 0.03 | 100 | 2.2966 | | 2.3175 | 0.04 | 125 | 2.2846 | | 2.2388 | 0.05 | 150 | 2.2766 | | 2.1679 | 0.06 | 175 | 2.2705 | | 2.2996 | 0.06 | 200 | 2.2678 | | 2.2788 | 0.07 | 225 | 2.2647 | | 2.2448 | 0.08 | 250 | 2.2637 | | 2.1837 | 0.09 | 275 | 2.2624 | | 2.2089 | 0.1 | 300 | 2.2621 | | 2.2686 | 0.1 | 325 | 2.2601 | | 2.2254 | 0.11 | 350 | 2.2593 | | 2.162 | 0.12 | 375 | 2.2590 | | 2.2687 | 0.13 | 400 | 2.2563 | | 2.2595 | 0.14 | 425 | 2.2571 | | 2.186 | 0.14 | 450 | 2.2564 | | 2.2689 | 0.15 | 475 | 2.2580 | | 2.2472 | 0.16 | 500 | 2.2554 | | 2.2005 | 0.17 | 525 | 2.2553 | | 2.1983 | 0.18 | 550 | 2.2552 | | 2.2388 | 0.18 | 575 | 2.2547 | | 2.1443 | 0.19 | 600 | 2.2555 | | 2.2198 | 0.2 | 625 | 2.2534 | | 2.3008 | 0.21 | 650 | 2.2536 | | 2.179 | 0.22 | 675 | 2.2521 | | 2.2069 | 0.22 | 700 | 2.2531 | | 2.1819 | 0.23 | 725 | 2.2526 | | 2.1218 | 0.24 | 750 | 2.2536 | | 2.1845 | 0.25 | 775 | 2.2515 | | 2.2167 | 0.26 | 800 | 2.2510 | | 2.2252 | 0.26 | 825 | 2.2520 | | 2.1664 | 0.27 | 850 | 2.2519 | | 2.1853 | 0.28 | 875 | 2.2530 | | 2.1499 | 0.29 | 900 | 2.2513 | | 2.2763 | 0.3 | 925 | 2.2517 | | 2.2528 | 0.3 | 950 | 2.2518 | | 2.2505 | 0.31 | 975 | 2.2500 | | 2.1683 | 0.32 | 1000 | 2.2502 | | 2.2177 | 0.33 | 1025 | 2.2501 | | 2.238 | 0.34 | 1050 | 2.2516 | | 2.193 | 0.34 | 1075 | 2.2507 | | 2.2025 | 0.35 | 1100 | 2.2502 | | 2.0944 | 0.36 | 1125 | 2.2512 | | 2.2272 | 0.37 | 1150 | 2.2508 | | 2.2264 | 0.38 | 1175 | 2.2500 | | 2.1837 | 0.38 | 1200 | 2.2507 | | 2.1444 | 0.39 | 1225 | 2.2489 | | 2.2464 | 0.4 | 1250 | 2.2499 | | 2.1388 | 0.41 | 1275 | 2.2508 | | 2.193 | 0.42 | 1300 | 2.2492 | | 2.2376 | 0.42 | 1325 | 2.2506 | | 2.2212 | 0.43 | 1350 | 2.2478 | | 2.2002 | 0.44 | 1375 | 2.2488 | | 2.2729 | 0.45 | 1400 | 2.2484 | | 2.2329 | 0.46 | 1425 | 2.2473 | | 2.1919 | 0.46 | 1450 | 2.2481 | | 2.2102 | 0.47 | 1475 | 2.2475 | | 2.1466 | 0.48 | 1500 | 2.2473 | | 2.1819 | 0.49 | 1525 | 2.2478 | | 2.2558 | 0.5 | 1550 | 2.2468 | | 2.2137 | 0.5 | 1575 | 2.2463 | | 2.2288 | 0.51 | 1600 | 2.2466 | | 2.1479 | 0.52 | 1625 | 2.2468 | | 2.1726 | 0.53 | 1650 | 2.2471 | | 2.1805 | 0.54 | 1675 | 2.2454 | | 2.1505 | 0.54 | 1700 | 2.2470 | | 2.1337 | 0.55 | 1725 | 2.2465 | | 2.2413 | 0.56 | 1750 | 2.2460 | | 2.152 | 0.57 | 1775 | 2.2478 | | 2.2669 | 0.58 | 1800 | 2.2471 | | 2.2925 | 0.58 | 1825 | 2.2465 | | 2.222 | 0.59 | 1850 | 2.2457 | | 2.1308 | 0.6 | 1875 | 2.2466 | | 2.201 | 0.61 | 1900 | 2.2456 | | 2.2247 | 0.62 | 1925 | 2.2460 | | 2.2426 | 0.62 | 1950 | 2.2463 | | 2.2312 | 0.63 | 1975 | 2.2465 | | 2.2679 | 0.64 | 2000 | 2.2464 | | 2.1928 | 0.65 | 2025 | 2.2463 | | 2.2087 | 0.66 | 2050 | 2.2455 | | 2.1792 | 0.66 | 2075 | 2.2470 | | 2.252 | 0.67 | 2100 | 2.2468 | | 2.2018 | 0.68 | 2125 | 2.2456 | | 2.2006 | 0.69 | 2150 | 2.2451 | | 2.2076 | 0.7 | 2175 | 2.2449 | | 2.2436 | 0.7 | 2200 | 2.2460 | | 2.2156 | 0.71 | 2225 | 2.2477 | | 2.1348 | 0.72 | 2250 | 2.2455 | | 2.1338 | 0.73 | 2275 | 2.2450 | | 2.2147 | 0.74 | 2300 | 2.2455 | | 2.2766 | 0.74 | 2325 | 2.2444 | | 2.204 | 0.75 | 2350 | 2.2458 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0