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--- |
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license: mit |
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base_model: microsoft/phi-2 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: phi-2-pl-v_0_1 |
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results: [] |
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--- |
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# phi-2-pl-v_0_1 |
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This model is based on [microsoft/phi-2](https://huggingface.co/microsoft/phi-2). It was trained from scratch on the 20231201 Polish Wikipedia dump. |
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## Model description |
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The model was trained for a context length of 2048 tokens. |
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## Intended uses & limitations |
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The model is intended for research purposes only. It may generate fictitious, incorrect, unethical, or biased texts. At its current state, it is not suitable for production purposes. |
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Example: |
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``` |
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tokenizer = AutoTokenizer.from_pretrained( |
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model_name, trust_remote_code=True, use_fast=True |
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) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, vocab_size=len(tokenizer), attn_implementation="flash_attention_2", |
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trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto" |
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) |
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model.eval() |
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generation_config = GenerationConfig.from_pretrained( |
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model_name, do_sample=False, repetition_penalty=1.5, |
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min_new_tokens=1, max_new_tokens=128 |
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) |
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test_input = tokenizer("Wrocław to polski miasto. Wrocław jest ", return_tensors='pt').to(torch.device('cuda')) |
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test_output = model.generate(**test_input, generation_config=generation_config) |
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test_preds = tokenizer.batch_decode(sequences=test_output, skip_special_tokens=True, clean_up_tokenization_spaces=True) |
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print(test_preds) |
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``` |
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## Training and evaluation data |
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The 20231201 Polish Wikipedia dump. |
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## Training procedure |
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### Training environment |
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- GPU: 1 x A100X (80GB) |
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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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- num_devices: 1 |
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- train_batch_size: 8 |
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- gradient_accumulation_steps: 1 |
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-07 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1 |
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- precision: bf16 |
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- seed: 42 |
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### Training results |
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- runtime: 1mo 3d 9h 40m 16s |
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- train_loss: 2.983 |
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### Framework versions |
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- Transformers 4.37.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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