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--- |
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library_name: transformers |
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base_model: None |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: trial2 |
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results: [] |
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license: apache-2.0 |
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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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## mistral-2b-base |
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Welcome to my model card! |
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This Model feature is ... |
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- trained by japanese |
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- trained in two stages: patch level and token level |
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- Suppression of unknown word generation by using byte fallback in SentencePiece tokenizer and conversion to huggingface Tokenizers format |
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- Use of Mistral 2B |
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Yukkuri shite ittene! |
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<!-- ## Intended uses & limitations |
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More information needed |
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--> |
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## How to use the model |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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model_path = "ce-lery/mistral-2b-base" |
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torch.set_float32_matmul_precision('high') |
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device = "cuda" |
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if (device != "cuda" and device != "cpu"): |
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device = "cpu" |
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tokenizer = AutoTokenizer.from_pretrained(model_path,use_fast=False) |
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model = AutoModelForCausalLM.from_pretrained(model_path, |
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trust_remote_code=True, |
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).to(device) |
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prompt = "自然言語処理とは、" |
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inputs = tokenizer(prompt, |
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add_special_tokens=True, |
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return_tensors="pt").to(model.device) |
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with torch.no_grad(): |
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outputs = model.generate( |
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inputs["input_ids"], |
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max_new_tokens=4096, |
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do_sample=True, |
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early_stopping=False, |
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top_p=0.95, |
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top_k=50, |
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temperature=0.7, |
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no_repeat_ngram_size=2, |
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num_beams=3 |
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) |
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print(outputs.tolist()[0]) |
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outputs_txt = tokenizer.decode(outputs[0]) |
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print(outputs_txt) |
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``` |
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## Training and evaluation data |
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40B token. The contents are following. |
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- Wikipedia |
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- Wikibooks |
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- Wikiversity |
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- CC-100 |
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- OSCAR2109 |
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- mC4 (head 150GB) |
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## Training procedure |
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Please refer [ce-lery/mistral-2b-recipe](https://github.com/ce-lery/mistral-2b-recipe). |
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The Guide for this repository is published [here](). It is written in Japanese. |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 128 |
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- total_train_batch_size: 256 |
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- optimizer: Use adamw_bnb_8bit with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine_with_min_lr |
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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 1.0 |
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### Training results |
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Please refer [here](https://huggingface.co./ce-lery/mistral-2b-base/tensorboard). |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.4.0a0+f70bd71a48.nv24.06 |
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- Datasets 2.20.0 |
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- Tokenizers 0.20.3 |
