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--- |
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license: llama2 |
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base_model: codellama/CodeLlama-7b-hf |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: codellama2-finetuned-codex-py |
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results: [] |
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datasets: |
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- iamtarun/python_code_instructions_18k_alpaca |
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language: |
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- en |
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pipeline_tag: text-generation |
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--- |
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# codellama2-finetuned-codex-py |
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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 [iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca) dataset. |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Example Use Cases: |
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``` |
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from transformers import AutoTokenizer |
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from transformers import pipeline |
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import torch |
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tokenizer = AutoTokenizer.from_pretrained("damerajee/codellama2-finetuned-alpaca-18k-fin") |
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pipe = pipeline( |
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"text-generation", |
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model="damerajee/codellama2-finetuned-alpaca-18k-fin", |
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torch_dtype=torch.float16, |
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device_map="auto", |
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) |
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text = "write a function that takes in print out each individual characters in a string" |
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sequences = pipe( |
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text, |
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do_sample=True, |
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temperature=0.1, |
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top_p=0.7, |
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num_return_sequences=1, |
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eos_token_id=tokenizer.eos_token_id, |
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max_length=70, |
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) |
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for seq in sequences: |
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print(f"Result: {seq['generated_text']}") |
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``` |
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## Training and evaluation data |
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| Step | Training Loss | |
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|------|---------------| |
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| 10 | 0.792200 | |
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| 20 | 0.416100 | |
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| 30 | 0.348600 | |
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| 40 | 0.323200 | |
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| 50 | 0.316300 | |
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| 60 | 0.317500 | |
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| 70 | 0.333600 | |
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| 80 | 0.329500 | |
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| 90 | 0.333400 | |
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| 100 | 0.309900 | |
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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: 0.0002 |
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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: 4 |
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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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- training_steps: 100 |
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- mixed_precision_training: Native AMP |
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### Training results |
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### Framework versions |
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- Transformers 4.36.0.dev0 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.0 |