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--- |
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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: checkpoints-mistral-300M-FA2 |
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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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# japanese-mistral-300m-base |
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## Overview |
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Welcome to my model card! |
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This Model feature is ... |
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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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- Pretrained by wikipedia dataset and cc100 dataset |
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- Use of [Mistral 300M](confing.json) |
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Yukkuri shite ittene! |
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## How to use the model |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer |
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import torch |
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MODEL_NAME = "ce-lery/japanese-mistral-300m-base" |
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torch.set_float32_matmul_precision('high') |
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DEVICE = "cuda" |
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if torch.cuda.is_available(): |
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print("cuda") |
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DEVICE = "cuda" |
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else: |
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print("cpu") |
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DEVICE = "cpu" |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME,use_fast=False) |
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model = AutoModelForCausalLM.from_pretrained( |
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MODEL_NAME, |
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trust_remote_code=True, |
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).to(DEVICE) |
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# streamer = TextStreamer(tokenizer) |
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prompt = "大規模言語モデルとは、" |
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inputs = tokenizer(prompt, add_special_tokens=False,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=256, |
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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=0.9, |
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# streamer=streamer, |
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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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## Receipe |
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If you want to restruct this model, you can refer [this Github repository](https://github.com/ce-lery/japanese-mistral-300m-recipe). |
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I wrote the receipe for struction this model. For example, |
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- Preprocess with sentencepiece |
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- Pretraining with flash attention2 and torch.compile and DeepSpeed |
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- Fine-tuning with databricks-dolly-15k-ja |
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If you find my mistake,error,...etc, please create issue. |
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If you create pulreqest, I'm very happy! |
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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: 0.0006 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 64 |
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- total_train_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.95) and epsilon=0.0001 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 1 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 4.2911 | 0.12 | 5000 | 4.2914 | |
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| 3.9709 | 0.24 | 10000 | 3.9900 | |
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| 3.8229 | 0.36 | 15000 | 3.8388 | |
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| 3.7197 | 0.47 | 20000 | 3.7454 | |
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| 3.652 | 0.59 | 25000 | 3.6739 | |
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| 3.597 | 0.71 | 30000 | 3.6177 | |
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| 3.5554 | 0.83 | 35000 | 3.5770 | |
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| 3.536 | 0.95 | 40000 | 3.5582 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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