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