83 lines
2.4 KiB
Markdown
83 lines
2.4 KiB
Markdown
---
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license: llama2
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tags:
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- generated_from_trainer
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datasets:
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- AshtonIsNotHere/nlp_pp_code_dataset
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metrics:
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- accuracy
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model-index:
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- name: CodeLlama_7B_nlp_pp
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results:
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- task:
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name: Causal Language Modeling
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type: text-generation
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dataset:
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name: AshtonIsNotHere/nlp_pp_code_dataset
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type: AshtonIsNotHere/nlp_pp_code_dataset
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split: test
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8968056729128353
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---
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# CodeLlama_7B_nlp_pp
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This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the AshtonIsNotHere/nlp_pp_code_dataset dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4129
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- Accuracy: 0.8968
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## Model description
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This model has been fine-tuned for code completion on a dataset of NLP++ code.
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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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Dataset consists of a combination of scraped NLP++ code and NLP++ code examples from the [VisualText website](https://visualtext.org/help/).
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## Training procedure
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This model is trained in a multinode, multi-gpu setup with DeepSpeed Z3. For more information on the training setup, check out the [GitHub repo](https://github.com/ashtonomy/nlp_pp_code_completion).
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.00012
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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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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 16
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- total_eval_batch_size: 4
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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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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 7.0
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| No log | 1.0 | 61 | 0.5100 | 0.8726 |
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| No log | 1.99 | 122 | 0.4129 | 0.8968 |
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| No log | 2.99 | 183 | 0.4166 | 0.9072 |
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| No log | 4.0 | 245 | 0.4595 | 0.9090 |
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| No log | 5.0 | 306 | 0.5181 | 0.9093 |
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| No log | 5.99 | 367 | 0.5553 | 0.9090 |
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| No log | 6.97 | 427 | 0.5603 | 0.9089 |
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### Framework versions
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- Transformers 4.30.2
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- Pytorch 2.0.1+cu117
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- Datasets 2.13.0
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- Tokenizers 0.13.3 |