ce-lery/mistral-2b-base

<!-- 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

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. 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
• —trainbatchsize: 2
• —evalbatchsize: 2
• —seed: 42
• —gradientaccumulationsteps: 128
• —totaltrainbatch_size: 256
• —optimizer: Use adamwbnb8bit with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• —lrschedulertype: cosinewithmin_lr
• —lrschedulerwarmup_steps: 1000
• —num_epochs: 1.0

Training results

Please refer here.

Framework versions

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