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sparse_mistral_7b_refined_web_50p_2024-04-13

Text Generation
Transformers
Safetensors
sparse_llama
Generated from Trainer
custom_code
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vxbrandon commited on
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End of training

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  1. README.md +16 -6
  2. model-00001-of-00003.safetensors +1 -1
  3. model-00002-of-00003.safetensors +1 -1
  4. model-00003-of-00003.safetensors +1 -1
README.md CHANGED
@@ -15,7 +15,7 @@ should probably proofread and complete it, then remove this comment. -->
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  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.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.2015
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  ## Model description
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@@ -45,7 +45,7 @@ The following hyperparameters were used during training:
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  - total_eval_batch_size: 16
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - training_steps: 1600
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  ### Training results
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@@ -111,10 +111,20 @@ The following hyperparameters were used during training:
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  | 2.1919 | 0.46 | 1450 | 2.2481 |
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  | 2.2102 | 0.47 | 1475 | 2.2475 |
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  | 2.1466 | 0.48 | 1500 | 2.2473 |
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- | 2.1818 | 0.49 | 1525 | 2.2462 |
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- | 2.2549 | 0.5 | 1550 | 2.2470 |
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- | 2.2137 | 0.5 | 1575 | 2.2449 |
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- | 2.2276 | 0.51 | 1600 | 2.2481 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  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.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.2004
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  ## Model description
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  - total_eval_batch_size: 16
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - training_steps: 1850
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  ### Training results
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  | 2.1919 | 0.46 | 1450 | 2.2481 |
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  | 2.2102 | 0.47 | 1475 | 2.2475 |
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  | 2.1466 | 0.48 | 1500 | 2.2473 |
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+ | 2.1819 | 0.49 | 1525 | 2.2478 |
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+ | 2.2558 | 0.5 | 1550 | 2.2468 |
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+ | 2.2137 | 0.5 | 1575 | 2.2463 |
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+ | 2.2288 | 0.51 | 1600 | 2.2466 |
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+ | 2.1479 | 0.52 | 1625 | 2.2468 |
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+ | 2.1726 | 0.53 | 1650 | 2.2471 |
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+ | 2.1805 | 0.54 | 1675 | 2.2454 |
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+ | 2.1505 | 0.54 | 1700 | 2.2470 |
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+ | 2.1337 | 0.55 | 1725 | 2.2465 |
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+ | 2.2413 | 0.56 | 1750 | 2.2460 |
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+ | 2.1528 | 0.57 | 1775 | 2.2460 |
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+ | 2.2645 | 0.58 | 1800 | 2.2473 |
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+ | 2.2913 | 0.58 | 1825 | 2.2473 |
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+ | 2.2214 | 0.59 | 1850 | 2.2471 |
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  ### Framework versions
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