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stojchet
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cae853d7940781dc7e9d9554f584df5f

PEFT
Safetensors
trl
sft
Generated from Trainer
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  ---
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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- ## Training Details
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- ### Training Data
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- ### Training Procedure
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- #### Preprocessing [optional]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- ## Evaluation
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- #### Summary
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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  ---
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+ base_model: deepseek-ai/deepseek-coder-1.3b-base
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+ datasets:
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+ - generator
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+ library_name: peft
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+ license: other
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+ tags:
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+ - trl
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+ - sft
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+ - generated_from_trainer
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+ model-index:
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+ - name: cae853d7940781dc7e9d9554f584df5f
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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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+
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+ [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/stojchets/huggingface/runs/cae853d7940781dc7e9d9554f584df5f)
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+ # cae853d7940781dc7e9d9554f584df5f
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+
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+ This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-base](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) on the generator dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.1733
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1.41e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - gradient_accumulation_steps: 16
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+ - total_train_batch_size: 128
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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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+ - num_epochs: 3
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:------:|:----:|:---------------:|
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+ | 1.2662 | 0.0128 | 1 | 1.2383 |
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+ | 1.2249 | 0.0256 | 2 | 1.2367 |
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+ | 1.2567 | 0.0384 | 3 | 1.2352 |
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+ | 1.155 | 0.0512 | 4 | 1.2338 |
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+ | 1.2201 | 0.064 | 5 | 1.2324 |
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+ | 1.2077 | 0.0768 | 6 | 1.2310 |
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+ | 1.2095 | 0.0896 | 7 | 1.2296 |
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+ | 1.2579 | 0.1024 | 8 | 1.2283 |
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+ | 1.2189 | 0.1152 | 9 | 1.2271 |
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+ | 1.2382 | 0.128 | 10 | 1.2258 |
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+ | 1.2605 | 0.1408 | 11 | 1.2248 |
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+ | 1.1883 | 0.1536 | 12 | 1.2239 |
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+ | 1.1614 | 0.1664 | 13 | 1.2230 |
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+ | 1.2769 | 0.1792 | 14 | 1.2220 |
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+ | 1.2099 | 0.192 | 15 | 1.2211 |
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+ | 1.2414 | 0.2048 | 16 | 1.2201 |
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+ | 1.2291 | 0.2176 | 17 | 1.2192 |
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+ | 1.2381 | 0.2304 | 18 | 1.2184 |
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+ | 1.1776 | 0.2432 | 19 | 1.2175 |
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+ | 1.1788 | 0.256 | 20 | 1.2167 |
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+ | 1.2061 | 0.2688 | 21 | 1.2159 |
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+ | 1.1856 | 0.2816 | 22 | 1.2150 |
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+ | 1.2252 | 0.2944 | 23 | 1.2142 |
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+ | 1.2646 | 0.3072 | 24 | 1.2134 |
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+ | 1.1888 | 0.32 | 25 | 1.2126 |
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+ | 1.228 | 0.3328 | 26 | 1.2118 |
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+ | 1.1969 | 0.3456 | 27 | 1.2111 |
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+ | 1.1779 | 0.3584 | 28 | 1.2103 |
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+ | 1.1726 | 0.3712 | 29 | 1.2096 |
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+ | 1.1582 | 0.384 | 30 | 1.2089 |
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+ | 1.1643 | 0.3968 | 31 | 1.2083 |
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+ | 1.1878 | 0.4096 | 32 | 1.2076 |
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+ | 1.2315 | 0.4224 | 33 | 1.2070 |
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+ | 1.2022 | 0.4352 | 34 | 1.2063 |
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+ | 1.1669 | 0.448 | 35 | 1.2057 |
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+ | 1.1609 | 0.4608 | 36 | 1.2051 |
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+ | 1.1888 | 0.4736 | 37 | 1.2045 |
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+ | 1.2044 | 0.4864 | 38 | 1.2039 |
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+ | 1.2389 | 0.4992 | 39 | 1.2033 |
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+ | 1.1755 | 0.512 | 40 | 1.2027 |
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+ | 1.1997 | 0.5248 | 41 | 1.2021 |
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+ | 1.1997 | 0.5376 | 42 | 1.2015 |
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+ | 1.1511 | 0.5504 | 43 | 1.2009 |
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+ | 1.1689 | 0.5632 | 44 | 1.2004 |
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+ | 1.1654 | 0.576 | 45 | 1.1998 |
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+ | 1.2018 | 0.5888 | 46 | 1.1993 |
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+ | 1.1503 | 0.6016 | 47 | 1.1988 |
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+ | 1.1835 | 0.6144 | 48 | 1.1983 |
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+ | 1.1831 | 0.6272 | 49 | 1.1977 |
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+ | 1.1629 | 0.64 | 50 | 1.1972 |
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+ | 1.2002 | 0.6528 | 51 | 1.1967 |
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+ | 1.1467 | 0.6656 | 52 | 1.1963 |
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+ | 1.193 | 0.6784 | 53 | 1.1959 |
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+ | 1.1652 | 0.6912 | 54 | 1.1955 |
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+ | 1.1446 | 0.704 | 55 | 1.1950 |
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+ | 1.1657 | 0.7168 | 56 | 1.1946 |
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+ | 1.1865 | 0.7296 | 57 | 1.1941 |
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+ | 1.1803 | 0.7424 | 58 | 1.1936 |
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+ | 1.1562 | 0.7552 | 59 | 1.1931 |
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+ | 1.1881 | 0.768 | 60 | 1.1926 |
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+ | 1.2279 | 0.7808 | 61 | 1.1921 |
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+ | 1.2158 | 0.7936 | 62 | 1.1915 |
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+ | 1.1586 | 0.8064 | 63 | 1.1910 |
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+ | 1.2019 | 0.8192 | 64 | 1.1906 |
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+ | 1.155 | 0.832 | 65 | 1.1901 |
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+ | 1.1142 | 0.8448 | 66 | 1.1897 |
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+ | 1.2389 | 0.8576 | 67 | 1.1894 |
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+ | 1.1259 | 0.8704 | 68 | 1.1889 |
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+ | 1.1568 | 0.8832 | 69 | 1.1886 |
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+ | 1.1306 | 0.896 | 70 | 1.1882 |
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+ | 1.1814 | 0.9088 | 71 | 1.1877 |
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+ | 1.2137 | 0.9216 | 72 | 1.1873 |
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+ | 1.1884 | 0.9344 | 73 | 1.1868 |
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+ | 1.1446 | 0.9472 | 74 | 1.1863 |
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+ | 1.1979 | 0.96 | 75 | 1.1858 |
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+ | 1.2137 | 0.9728 | 76 | 1.1854 |
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+ | 1.1541 | 0.9856 | 77 | 1.1851 |
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+ | 1.1775 | 0.9984 | 78 | 1.1847 |
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+ | 1.1489 | 1.0112 | 79 | 1.1844 |
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+ | 1.131 | 1.024 | 80 | 1.1841 |
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+ | 1.1427 | 1.0368 | 81 | 1.1837 |
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+ | 1.2006 | 1.0496 | 82 | 1.1833 |
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+ | 1.1473 | 1.0624 | 83 | 1.1830 |
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+ | 1.1315 | 1.0752 | 84 | 1.1826 |
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+ | 1.1497 | 1.088 | 85 | 1.1823 |
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+ | 1.1845 | 1.1008 | 86 | 1.1820 |
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+ | 1.1845 | 1.1136 | 87 | 1.1817 |
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+ | 1.1167 | 1.1264 | 88 | 1.1814 |
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+ | 1.1639 | 1.1392 | 89 | 1.1811 |
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+ | 1.1952 | 1.152 | 90 | 1.1808 |
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+ | 1.1327 | 1.1648 | 91 | 1.1805 |
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+ | 1.0937 | 1.1776 | 92 | 1.1802 |
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+ | 1.1549 | 1.1904 | 93 | 1.1799 |
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+ | 1.1704 | 1.2032 | 94 | 1.1797 |
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+ | 1.1479 | 1.216 | 95 | 1.1794 |
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+ | 1.2221 | 1.2288 | 96 | 1.1792 |
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+ | 1.1193 | 1.2416 | 97 | 1.1789 |
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+ | 1.1259 | 1.2544 | 98 | 1.1786 |
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+ | 1.1816 | 1.2672 | 99 | 1.1784 |
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+ | 1.1566 | 1.28 | 100 | 1.1782 |
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+ | 1.1093 | 1.2928 | 101 | 1.1780 |
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+ | 1.1985 | 1.3056 | 102 | 1.1779 |
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+ | 1.1553 | 1.3184 | 103 | 1.1778 |
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+ | 1.1772 | 1.3312 | 104 | 1.1776 |
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+ | 1.1154 | 1.3440 | 105 | 1.1775 |
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+ | 1.1666 | 1.3568 | 106 | 1.1774 |
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+ | 1.1494 | 1.3696 | 107 | 1.1772 |
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+ | 1.1508 | 1.3824 | 108 | 1.1771 |
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+ | 1.201 | 1.3952 | 109 | 1.1770 |
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+ | 1.1919 | 1.408 | 110 | 1.1769 |
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+ | 1.1885 | 1.4208 | 111 | 1.1768 |
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+ | 1.2055 | 1.4336 | 112 | 1.1767 |
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+ | 1.1522 | 1.4464 | 113 | 1.1766 |
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+ | 1.1565 | 1.4592 | 114 | 1.1765 |
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+ | 1.1551 | 1.472 | 115 | 1.1764 |
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+ | 1.17 | 1.4848 | 116 | 1.1763 |
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+ | 1.1631 | 1.4976 | 117 | 1.1762 |
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+ | 1.1396 | 1.5104 | 118 | 1.1761 |
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+ | 1.1355 | 1.5232 | 119 | 1.1760 |
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+ | 1.1606 | 1.536 | 120 | 1.1760 |
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+ | 1.1594 | 1.5488 | 121 | 1.1759 |
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+ | 1.1783 | 1.5616 | 122 | 1.1758 |
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+ | 1.1592 | 1.5744 | 123 | 1.1758 |
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+ | 1.1159 | 1.5872 | 124 | 1.1757 |
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+ | 1.1807 | 1.6 | 125 | 1.1756 |
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+ | 1.2294 | 1.6128 | 126 | 1.1756 |
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+ | 1.1922 | 1.6256 | 127 | 1.1755 |
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+ | 1.1532 | 1.6384 | 128 | 1.1755 |
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+ | 1.1956 | 1.6512 | 129 | 1.1754 |
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+ | 1.1954 | 1.6640 | 130 | 1.1754 |
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+ | 1.1479 | 1.6768 | 131 | 1.1753 |
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+ | 1.1398 | 1.6896 | 132 | 1.1753 |
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+ | 1.1724 | 1.7024 | 133 | 1.1752 |
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+ | 1.1397 | 1.7152 | 134 | 1.1752 |
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+ | 1.2162 | 1.728 | 135 | 1.1751 |
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+ | 1.1854 | 1.7408 | 136 | 1.1751 |
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+ | 1.1411 | 1.7536 | 137 | 1.1751 |
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+ | 1.0747 | 1.7664 | 138 | 1.1750 |
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+ | 1.1727 | 1.7792 | 139 | 1.1750 |
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+ | 1.1701 | 1.792 | 140 | 1.1750 |
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+ | 1.1688 | 1.8048 | 141 | 1.1750 |
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+ | 1.1545 | 1.8176 | 142 | 1.1750 |
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+ | 1.1512 | 1.8304 | 143 | 1.1749 |
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+ | 1.203 | 1.8432 | 144 | 1.1749 |
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+ | 1.1665 | 1.8560 | 145 | 1.1749 |
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+ | 1.186 | 1.8688 | 146 | 1.1748 |
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+ | 1.1283 | 1.8816 | 147 | 1.1748 |
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+ | 1.1555 | 1.8944 | 148 | 1.1748 |
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+ | 1.1243 | 1.9072 | 149 | 1.1748 |
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+ | 1.1767 | 1.92 | 150 | 1.1747 |
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+ | 1.1505 | 1.9328 | 151 | 1.1747 |
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+ | 1.1012 | 1.9456 | 152 | 1.1747 |
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+ | 1.2098 | 1.9584 | 153 | 1.1747 |
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+ | 1.1476 | 1.9712 | 154 | 1.1746 |
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+ | 1.2055 | 1.984 | 155 | 1.1746 |
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+ | 1.1539 | 1.9968 | 156 | 1.1746 |
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+ | 1.176 | 2.0096 | 157 | 1.1745 |
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+ | 1.1357 | 2.0224 | 158 | 1.1745 |
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+ | 1.1943 | 2.0352 | 159 | 1.1745 |
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+ | 1.1447 | 2.048 | 160 | 1.1744 |
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+ | 1.123 | 2.0608 | 161 | 1.1744 |
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+ | 1.1638 | 2.0736 | 162 | 1.1744 |
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+ | 1.1551 | 2.0864 | 163 | 1.1744 |
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+ | 1.1409 | 2.0992 | 164 | 1.1743 |
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+ | 1.1071 | 2.112 | 165 | 1.1743 |
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+ | 1.1705 | 2.1248 | 166 | 1.1743 |
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+ | 1.2038 | 2.1376 | 167 | 1.1742 |
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+ | 1.1734 | 2.1504 | 168 | 1.1742 |
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+ | 1.1538 | 2.1632 | 169 | 1.1742 |
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+ | 1.179 | 2.176 | 170 | 1.1742 |
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+ | 1.1614 | 2.1888 | 171 | 1.1741 |
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+ | 1.1397 | 2.2016 | 172 | 1.1741 |
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+ | 1.1569 | 2.2144 | 173 | 1.1741 |
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+ | 1.1379 | 2.2272 | 174 | 1.1740 |
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+ | 1.1304 | 2.24 | 175 | 1.1740 |
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+ | 1.1855 | 2.2528 | 176 | 1.1740 |
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+ | 1.1763 | 2.2656 | 177 | 1.1740 |
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+ | 1.1194 | 2.2784 | 178 | 1.1739 |
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+ | 1.0971 | 2.2912 | 179 | 1.1739 |
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+ | 1.1566 | 2.304 | 180 | 1.1739 |
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+ | 1.1421 | 2.3168 | 181 | 1.1739 |
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+ | 1.1645 | 2.3296 | 182 | 1.1738 |
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+ | 1.1782 | 2.3424 | 183 | 1.1738 |
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+ | 1.1514 | 2.3552 | 184 | 1.1738 |
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+ | 1.175 | 2.368 | 185 | 1.1738 |
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+ | 1.1279 | 2.3808 | 186 | 1.1738 |
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+ | 1.1158 | 2.3936 | 187 | 1.1738 |
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+ | 1.202 | 2.4064 | 188 | 1.1737 |
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+ | 1.164 | 2.4192 | 189 | 1.1737 |
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+ | 1.1431 | 2.432 | 190 | 1.1737 |
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+ | 1.1271 | 2.4448 | 191 | 1.1737 |
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+ | 1.1746 | 2.4576 | 192 | 1.1736 |
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+ | 1.1126 | 2.4704 | 193 | 1.1736 |
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+ | 1.1652 | 2.4832 | 194 | 1.1736 |
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+ | 1.1692 | 2.496 | 195 | 1.1736 |
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+ | 1.1764 | 2.5088 | 196 | 1.1736 |
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+ | 1.1905 | 2.5216 | 197 | 1.1736 |
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+ | 1.1679 | 2.5344 | 198 | 1.1735 |
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+ | 1.1324 | 2.5472 | 199 | 1.1735 |
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+ | 1.124 | 2.56 | 200 | 1.1735 |
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+ | 1.1296 | 2.5728 | 201 | 1.1735 |
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+ | 1.1498 | 2.5856 | 202 | 1.1735 |
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+ | 1.1845 | 2.5984 | 203 | 1.1735 |
260
+ | 1.0965 | 2.6112 | 204 | 1.1735 |
261
+ | 1.1511 | 2.624 | 205 | 1.1735 |
262
+ | 1.1703 | 2.6368 | 206 | 1.1734 |
263
+ | 1.1948 | 2.6496 | 207 | 1.1734 |
264
+ | 1.1688 | 2.6624 | 208 | 1.1734 |
265
+ | 1.1528 | 2.6752 | 209 | 1.1734 |
266
+ | 1.1261 | 2.6880 | 210 | 1.1734 |
267
+ | 1.1662 | 2.7008 | 211 | 1.1734 |
268
+ | 1.1596 | 2.7136 | 212 | 1.1734 |
269
+ | 1.1474 | 2.7264 | 213 | 1.1734 |
270
+ | 1.1813 | 2.7392 | 214 | 1.1734 |
271
+ | 1.1624 | 2.752 | 215 | 1.1734 |
272
+ | 1.1604 | 2.7648 | 216 | 1.1734 |
273
+ | 1.1596 | 2.7776 | 217 | 1.1734 |
274
+ | 1.2008 | 2.7904 | 218 | 1.1734 |
275
+ | 1.1813 | 2.8032 | 219 | 1.1734 |
276
+ | 1.2147 | 2.816 | 220 | 1.1734 |
277
+ | 1.1821 | 2.8288 | 221 | 1.1734 |
278
+ | 1.1476 | 2.8416 | 222 | 1.1734 |
279
+ | 1.1416 | 2.8544 | 223 | 1.1734 |
280
+ | 1.1228 | 2.8672 | 224 | 1.1733 |
281
+ | 1.1908 | 2.88 | 225 | 1.1733 |
282
+ | 1.1666 | 2.8928 | 226 | 1.1733 |
283
+ | 1.0962 | 2.9056 | 227 | 1.1733 |
284
+ | 1.1721 | 2.9184 | 228 | 1.1733 |
285
+ | 1.1158 | 2.9312 | 229 | 1.1733 |
286
+ | 1.1282 | 2.944 | 230 | 1.1733 |
287
+ | 1.1401 | 2.9568 | 231 | 1.1733 |
288
+ | 1.1897 | 2.9696 | 232 | 1.1733 |
289
+ | 1.1395 | 2.9824 | 233 | 1.1733 |
290
+ | 1.141 | 2.9952 | 234 | 1.1733 |
291
+
292
+
293
+ ### Framework versions
294
+
295
+ - PEFT 0.10.0
296
+ - Transformers 4.43.0.dev0
297
+ - Pytorch 2.2.2+cu121
298
+ - Datasets 2.19.2
299
+ - Tokenizers 0.19.1