---
library_name: transformers
base_model: None
tags:
- generated_from_trainer
model-index:
- name: trial2
  results: []
license: apache-2.0
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


## mistral-2b-base

Welcome to my model card!

This Model feature is ...

- trained by japanese
- trained in two stages: patch level and token level
- Suppression of unknown word generation by using byte fallback in SentencePiece tokenizer and conversion to huggingface Tokenizers format
- Use of Mistral 2B

Yukkuri shite ittene!

<!-- ## Intended uses & limitations

More information needed
 -->

## How to use the model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_path = "ce-lery/mistral-2b-base"
torch.set_float32_matmul_precision('high')

device = "cuda"
if (device != "cuda" and device != "cpu"):
    device = "cpu"

tokenizer = AutoTokenizer.from_pretrained(model_path,use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_path,
                                             trust_remote_code=True,
                                             ).to(device)

prompt = "自然言語処理とは、"
inputs = tokenizer(prompt,
                   add_special_tokens=True,
                   return_tensors="pt").to(model.device)
with torch.no_grad():
    outputs = model.generate(
        inputs["input_ids"],
        max_new_tokens=4096,
        do_sample=True,
        early_stopping=False,
        top_p=0.95,
        top_k=50,
        temperature=0.7,
        no_repeat_ngram_size=2,
        num_beams=3
    )

print(outputs.tolist()[0])
outputs_txt = tokenizer.decode(outputs[0])
print(outputs_txt)

```

## Training and evaluation data

40B token. The contents are following.

- Wikipedia
- Wikibooks
- Wikiversity
- CC-100
- OSCAR2109
- mC4 (head 150GB)

## Training procedure

Please refer [ce-lery/mistral-2b-recipe](https://github.com/ce-lery/mistral-2b-recipe).  
The Guide for this repository is published [here](). It is written in Japanese.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 128
- total_train_batch_size: 256
- optimizer: Use adamw_bnb_8bit with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_min_lr
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1.0

### Training results

Please refer [here](https://huggingface.co./ce-lery/mistral-2b-base/tensorboard).

### Framework versions

- Transformers 4.46.2
- Pytorch 2.4.0a0+f70bd71a48.nv24.06
- Datasets 2.20.0
- Tokenizers 0.20.3