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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-0.3b |
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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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# checkpoints-mistral-300M |
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This model is a fine-tuned version of [None](https://huggingface.co/None) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.205 |
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## Model description |
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More information needed |
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## Training and evaluation data |
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***** train metrics ***** |
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epoch = 13.91 |
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train_loss = 2.205 |
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***** eval metrics ***** |
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epoch = 13.91 |
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eval_loss = 2.4 |
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perplexity = 11.0228 |
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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: 0.0003 |
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- train_batch_size: 6 |
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- eval_batch_size: 6 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 192 |
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- total_eval_batch_size: 12 |
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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: 4 |
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- num_epochs: 6 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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## Usage |
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```python |
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from transformers import pipeline |
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pipe = pipeline("text-generation", model="ayousanz/japanese-mistral-0.3b-base") |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer |
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import torch |
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MODEL_NAME = "ayousanz/japanese-mistral-0.3b-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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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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no_repeat_ngram_size=2, |
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num_beams=3 |
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) |
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outputs_txt = tokenizer.decode(outputs[0]) |
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print(outputs_txt) |
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``` |
