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!

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 . 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.

Framework versions

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