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--- |
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license: apache-2.0 |
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base_model: mistralai/Mistral-7B-Instruct-v0.1 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: Mistral-7B-Instruct-v0.1-LC-PI-.5-noSW |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Mistral-7B-Instruct-v0.1-LC-PI-.5-noSW |
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This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8995 |
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## Model description |
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This model is a fine-tuning of Mistral-7B-Instruct-v0.1. |
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This FT was done with full attention (removing the 4k SWA). |
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This FT was using a Position Interpolation factor of 0.5 (Linear RoPE scaling). |
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Please note that the RoPE scaling factor should be determined by L'/L where L is the pre-training max context length and L' is the new max context length. In our case, we are just making experiments (and for us we would have had L'/L = 7200/8096 > 1 which did not require any PI scaling). |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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Data is a 9k sample from the RedPajama datset. The context is <=7200 with a decreasing exponential distribution of scale 1500. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 20 |
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- training_steps: 300 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 2.1056 | 0.18 | 50 | 1.9680 | |
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| 2.1266 | 0.36 | 100 | 1.9213 | |
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| 1.978 | 0.55 | 150 | 1.9084 | |
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| 1.8576 | 0.73 | 200 | 1.9022 | |
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| 2.0311 | 0.91 | 250 | 1.8999 | |
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| 1.9197 | 1.09 | 300 | 1.8995 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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