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austindavis
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chess-gpt2-uci-12x12x768

Text Generation
Transformers
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Inference Endpoints
Model card Files Community
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README.md CHANGED
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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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-
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- <!-- Provide a quick summary of what the model is/does. -->
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-
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-
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-
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- ## Model Details
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-
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- ### Model Description
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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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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- ## Bias, Risks, and Limitations
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- ### Recommendations
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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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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-
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- #### Preprocessing [optional]
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- [More Information Needed]
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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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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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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-
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ## Glossary [optional]
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- ## More Information [optional]
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  ---
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: gpt2-lichess-uci-202306-12x12
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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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+ # gpt2-lichess-uci-202306-12x12
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+
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+ This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.8365
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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: 0.0002
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+ - train_batch_size: 6
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+ - eval_batch_size: 8
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+ - seed: 42
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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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+ - num_epochs: 1
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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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+ | 1.3438 | 0.0036 | 15000 | 1.3198 |
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+ | 1.2309 | 0.0072 | 30000 | 1.2102 |
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+ | 1.1738 | 0.0108 | 45000 | 1.1595 |
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+ | 1.1384 | 0.0144 | 60000 | 1.1267 |
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+ | 1.115 | 0.0180 | 75000 | 1.0988 |
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+ | 1.0937 | 0.0216 | 90000 | 1.0820 |
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+ | 1.0786 | 0.0253 | 105000 | 1.0670 |
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+ | 1.0649 | 0.0289 | 120000 | 1.0541 |
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+ | 1.0546 | 0.0325 | 135000 | 1.0429 |
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+ | 1.0446 | 0.0361 | 150000 | 1.0323 |
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+ | 1.0388 | 0.0397 | 165000 | 1.0247 |
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+ | 1.029 | 0.0433 | 180000 | 1.0184 |
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+ | 1.022 | 0.0469 | 195000 | 1.0127 |
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+ | 1.0144 | 0.0505 | 210000 | 1.0072 |
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+ | 1.01 | 0.0541 | 225000 | 1.0008 |
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+ | 1.0069 | 0.0577 | 240000 | 0.9964 |
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+ | 1.0033 | 0.0613 | 255000 | 0.9928 |
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+ | 0.9988 | 0.0649 | 270000 | 0.9867 |
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+ | 0.9948 | 0.0685 | 285000 | 0.9836 |
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+ | 0.9892 | 0.0722 | 300000 | 0.9803 |
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+ | 0.9864 | 0.0758 | 315000 | 0.9764 |
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+ | 0.9798 | 0.0794 | 330000 | 0.9733 |
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+ | 0.9804 | 0.0830 | 345000 | 0.9713 |
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+ | 0.9776 | 0.0866 | 360000 | 0.9684 |
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+ | 0.9754 | 0.0902 | 375000 | 0.9656 |
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+ | 0.9736 | 0.0938 | 390000 | 0.9639 |
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+ | 0.9687 | 0.0974 | 405000 | 0.9609 |
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+ | 0.9715 | 0.1010 | 420000 | 0.9592 |
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+ | 0.9644 | 0.1046 | 435000 | 0.9581 |
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+ | 0.9629 | 0.1082 | 450000 | 0.9550 |
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+ | 0.9609 | 0.1118 | 465000 | 0.9544 |
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+ | 0.9589 | 0.1154 | 480000 | 0.9511 |
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+ | 0.9585 | 0.1190 | 495000 | 0.9493 |
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+ | 0.9573 | 0.1227 | 510000 | 0.9491 |
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+ | 0.9533 | 0.1263 | 525000 | 0.9473 |
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+ | 0.9558 | 0.1299 | 540000 | 0.9453 |
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+ | 0.9498 | 0.1335 | 555000 | 0.9434 |
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+ | 0.9487 | 0.1371 | 570000 | 0.9415 |
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+ | 0.9492 | 0.1407 | 585000 | 0.9411 |
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+ | 0.9475 | 0.1443 | 600000 | 0.9390 |
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+ | 0.9443 | 0.1479 | 615000 | 0.9387 |
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+ | 0.9458 | 0.1515 | 630000 | 0.9372 |
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+ | 0.9441 | 0.1551 | 645000 | 0.9358 |
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+ | 0.9438 | 0.1587 | 660000 | 0.9350 |
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+ | 0.9423 | 0.1623 | 675000 | 0.9341 |
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+ | 0.9383 | 0.1659 | 690000 | 0.9321 |
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+ | 0.9395 | 0.1696 | 705000 | 0.9314 |
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+ | 0.9379 | 0.1732 | 720000 | 0.9301 |
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+ | 0.9382 | 0.1768 | 735000 | 0.9289 |
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+ | 0.9374 | 0.1804 | 750000 | 0.9284 |
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+ | 0.9353 | 0.1840 | 765000 | 0.9278 |
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+ | 0.9325 | 0.1876 | 780000 | 0.9270 |
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+ | 0.932 | 0.1912 | 795000 | 0.9255 |
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+ | 0.9289 | 0.1948 | 810000 | 0.9250 |
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+ | 0.9324 | 0.1984 | 825000 | 0.9233 |
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+ | 0.9303 | 0.2020 | 840000 | 0.9230 |
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+ | 0.9297 | 0.2056 | 855000 | 0.9224 |
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+ | 0.9279 | 0.2092 | 870000 | 0.9223 |
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+ | 0.9298 | 0.2128 | 885000 | 0.9202 |
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+ | 0.9252 | 0.2165 | 900000 | 0.9195 |
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+ | 0.9259 | 0.2201 | 915000 | 0.9194 |
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+ | 0.9255 | 0.2237 | 930000 | 0.9186 |
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+ | 0.9231 | 0.2273 | 945000 | 0.9181 |
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+ | 0.9258 | 0.2309 | 960000 | 0.9165 |
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+ | 0.9214 | 0.2345 | 975000 | 0.9162 |
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+ | 0.924 | 0.2381 | 990000 | 0.9161 |
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+ | 0.9245 | 0.2417 | 1005000 | 0.9146 |
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+ | 0.919 | 0.2453 | 1020000 | 0.9147 |
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+ | 0.9202 | 0.2489 | 1035000 | 0.9133 |
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+ | 0.92 | 0.2525 | 1050000 | 0.9125 |
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+ | 0.9161 | 0.2561 | 1065000 | 0.9128 |
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+ | 0.9174 | 0.2597 | 1080000 | 0.9123 |
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+ | 0.9176 | 0.2634 | 1095000 | 0.9107 |
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+ | 0.9177 | 0.2670 | 1110000 | 0.9097 |
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+ | 0.9178 | 0.2706 | 1125000 | 0.9098 |
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+ | 0.9144 | 0.2742 | 1140000 | 0.9085 |
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+ | 0.916 | 0.2778 | 1155000 | 0.9091 |
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+ | 0.913 | 0.2814 | 1170000 | 0.9088 |
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+ | 0.9135 | 0.2850 | 1185000 | 0.9075 |
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+ | 0.9145 | 0.2886 | 1200000 | 0.9059 |
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+ | 0.9113 | 0.2922 | 1215000 | 0.9064 |
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+ | 0.9121 | 0.2958 | 1230000 | 0.9060 |
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+ | 0.9108 | 0.2994 | 1245000 | 0.9052 |
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+ | 0.909 | 0.3030 | 1260000 | 0.9038 |
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+ | 0.9127 | 0.3066 | 1275000 | 0.9040 |
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+ | 0.9104 | 0.3103 | 1290000 | 0.9029 |
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+ | 0.9097 | 0.3139 | 1305000 | 0.9036 |
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+ | 0.9086 | 0.3175 | 1320000 | 0.9023 |
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+ | 0.9078 | 0.3211 | 1335000 | 0.9016 |
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+ | 0.9077 | 0.3247 | 1350000 | 0.9019 |
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+ | 0.9058 | 0.3283 | 1365000 | 0.9010 |
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+ | 0.9053 | 0.3319 | 1380000 | 0.9005 |
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+ | 0.9069 | 0.3355 | 1395000 | 0.8995 |
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+ | 0.9057 | 0.3391 | 1410000 | 0.8997 |
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+ | 0.9037 | 0.3427 | 1425000 | 0.8988 |
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+ | 0.9038 | 0.3463 | 1440000 | 0.8987 |
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+ | 0.9029 | 0.3499 | 1455000 | 0.8979 |
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+ | 0.9012 | 0.3535 | 1470000 | 0.8975 |
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+ | 0.9003 | 0.3571 | 1485000 | 0.8977 |
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+ | 0.9019 | 0.3608 | 1500000 | 0.8966 |
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+ | 0.9018 | 0.3644 | 1515000 | 0.8964 |
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+ | 0.9008 | 0.3680 | 1530000 | 0.8957 |
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+ | 0.8997 | 0.3716 | 1545000 | 0.8951 |
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+ | 0.8996 | 0.3752 | 1560000 | 0.8950 |
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+ | 0.9 | 0.3788 | 1575000 | 0.8941 |
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+ | 0.8989 | 0.3824 | 1590000 | 0.8934 |
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+ | 0.8962 | 0.3860 | 1605000 | 0.8938 |
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+ | 0.8962 | 0.3896 | 1620000 | 0.8936 |
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+ | 0.8972 | 0.3932 | 1635000 | 0.8926 |
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+ | 0.8985 | 0.3968 | 1650000 | 0.8915 |
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+ | 0.8959 | 0.4004 | 1665000 | 0.8919 |
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+ | 0.8966 | 0.4040 | 1680000 | 0.8911 |
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+ | 0.8952 | 0.4077 | 1695000 | 0.8908 |
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+ | 0.8949 | 0.4113 | 1710000 | 0.8897 |
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+ | 0.8943 | 0.4149 | 1725000 | 0.8895 |
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+ | 0.8921 | 0.4185 | 1740000 | 0.8892 |
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+ | 0.8924 | 0.4221 | 1755000 | 0.8887 |
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+ | 0.8926 | 0.4257 | 1770000 | 0.8880 |
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+ | 0.8902 | 0.4293 | 1785000 | 0.8876 |
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+ | 0.8931 | 0.4329 | 1800000 | 0.8871 |
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+ | 0.8909 | 0.4365 | 1815000 | 0.8871 |
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+ | 0.8904 | 0.4401 | 1830000 | 0.8863 |
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+ | 0.8892 | 0.4437 | 1845000 | 0.8861 |
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+ | 0.8886 | 0.4473 | 1860000 | 0.8854 |
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+ | 0.8894 | 0.4509 | 1875000 | 0.8855 |
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+ | 0.8908 | 0.4546 | 1890000 | 0.8852 |
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+ | 0.8898 | 0.4582 | 1905000 | 0.8848 |
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+ | 0.886 | 0.4618 | 1920000 | 0.8842 |
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+ | 0.8855 | 0.4654 | 1935000 | 0.8830 |
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+ | 0.8866 | 0.4690 | 1950000 | 0.8825 |
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+ | 0.8848 | 0.4726 | 1965000 | 0.8823 |
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+ | 0.8835 | 0.4762 | 1980000 | 0.8825 |
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+ | 0.8869 | 0.4798 | 1995000 | 0.8814 |
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+ | 0.885 | 0.4834 | 2010000 | 0.8814 |
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+ | 0.8869 | 0.4870 | 2025000 | 0.8812 |
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+ | 0.8842 | 0.4906 | 2040000 | 0.8804 |
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+ | 0.8865 | 0.4942 | 2055000 | 0.8804 |
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+ | 0.8837 | 0.4978 | 2070000 | 0.8799 |
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+ | 0.8799 | 0.5015 | 2085000 | 0.8798 |
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+ | 0.8824 | 0.5051 | 2100000 | 0.8784 |
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+ | 0.8821 | 0.5087 | 2115000 | 0.8786 |
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+ | 0.8802 | 0.5123 | 2130000 | 0.8779 |
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+ | 0.8811 | 0.5159 | 2145000 | 0.8778 |
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+ | 0.8805 | 0.5195 | 2160000 | 0.8774 |
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+ | 0.8837 | 0.5231 | 2175000 | 0.8765 |
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+ | 0.88 | 0.5267 | 2190000 | 0.8764 |
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+ | 0.8794 | 0.5303 | 2205000 | 0.8760 |
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+ | 0.8805 | 0.5339 | 2220000 | 0.8754 |
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+ | 0.8768 | 0.5375 | 2235000 | 0.8757 |
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+ | 0.8763 | 0.5411 | 2250000 | 0.8743 |
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+ | 0.8762 | 0.5447 | 2265000 | 0.8746 |
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+ | 0.8786 | 0.5484 | 2280000 | 0.8748 |
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+ | 0.8772 | 0.5520 | 2295000 | 0.8735 |
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+ | 0.8758 | 0.5556 | 2310000 | 0.8728 |
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+ | 0.874 | 0.5592 | 2325000 | 0.8724 |
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+ | 0.8752 | 0.5628 | 2340000 | 0.8722 |
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+ | 0.8748 | 0.5664 | 2355000 | 0.8712 |
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+ | 0.8749 | 0.5700 | 2370000 | 0.8714 |
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+ | 0.874 | 0.5736 | 2385000 | 0.8707 |
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+ | 0.8728 | 0.5772 | 2400000 | 0.8702 |
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+ | 0.8742 | 0.5808 | 2415000 | 0.8701 |
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+ | 0.8683 | 0.5844 | 2430000 | 0.8696 |
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+ | 0.8693 | 0.5880 | 2445000 | 0.8695 |
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+ | 0.8717 | 0.5916 | 2460000 | 0.8684 |
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+ | 0.8697 | 0.5952 | 2475000 | 0.8682 |
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+ | 0.8734 | 0.5989 | 2490000 | 0.8675 |
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+ | 0.87 | 0.6025 | 2505000 | 0.8672 |
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+ | 0.8714 | 0.6061 | 2520000 | 0.8669 |
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+ | 0.8671 | 0.6097 | 2535000 | 0.8661 |
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+ | 0.8681 | 0.6133 | 2550000 | 0.8661 |
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+ | 0.8689 | 0.6169 | 2565000 | 0.8658 |
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+ | 0.8655 | 0.6205 | 2580000 | 0.8652 |
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+ | 0.8667 | 0.6241 | 2595000 | 0.8646 |
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+ | 0.8676 | 0.6277 | 2610000 | 0.8647 |
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+ | 0.8671 | 0.6313 | 2625000 | 0.8638 |
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+ | 0.8665 | 0.6349 | 2640000 | 0.8632 |
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+ | 0.865 | 0.6385 | 2655000 | 0.8632 |
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+ | 0.8648 | 0.6421 | 2670000 | 0.8627 |
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+ | 0.8646 | 0.6458 | 2685000 | 0.8616 |
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+ | 0.8621 | 0.6494 | 2700000 | 0.8619 |
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+ | 0.8631 | 0.6530 | 2715000 | 0.8621 |
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+ | 0.8623 | 0.6566 | 2730000 | 0.8609 |
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+ | 0.8623 | 0.6602 | 2745000 | 0.8606 |
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+ | 0.8625 | 0.6638 | 2760000 | 0.8602 |
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+ | 0.8637 | 0.6674 | 2775000 | 0.8595 |
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+ | 0.8625 | 0.6710 | 2790000 | 0.8594 |
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+ | 0.8627 | 0.6746 | 2805000 | 0.8587 |
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+ | 0.8606 | 0.6782 | 2820000 | 0.8584 |
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+ | 0.8607 | 0.6818 | 2835000 | 0.8578 |
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+ | 0.8609 | 0.6854 | 2850000 | 0.8575 |
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+ | 0.8577 | 0.6890 | 2865000 | 0.8570 |
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+ | 0.8567 | 0.6927 | 2880000 | 0.8567 |
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+ | 0.8587 | 0.6963 | 2895000 | 0.8560 |
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+ | 0.8558 | 0.6999 | 2910000 | 0.8561 |
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+ | 0.8539 | 0.7035 | 2925000 | 0.8556 |
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+ | 0.8576 | 0.7071 | 2940000 | 0.8553 |
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+ | 0.8555 | 0.7107 | 2955000 | 0.8546 |
244
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245
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246
+ | 0.8544 | 0.7215 | 3000000 | 0.8540 |
247
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248
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249
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250
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251
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252
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253
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254
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255
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256
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257
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259
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260
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266
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267
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268
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269
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270
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271
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272
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273
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274
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275
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276
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277
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278
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279
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280
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281
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282
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283
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284
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285
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286
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287
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288
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289
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290
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291
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292
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293
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294
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295
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296
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297
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298
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299
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300
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301
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302
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303
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304
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306
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307
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308
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309
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310
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311
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312
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313
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314
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315
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316
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317
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318
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319
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+
325
+
326
+ ### Framework versions
327
+
328
+ - Transformers 4.40.1
329
+ - Pytorch 2.3.0
330
+ - Datasets 2.19.1
331
+ - Tokenizers 0.19.1
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