checkpoints-mistral-300M

This model is a fine-tuned version of None on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.205

Model description

More information needed

Training and evaluation data

***** train metrics *****

epoch = 13.91 train_loss = 2.205

***** eval metrics *****

epoch = 13.91 eval_loss = 2.4 perplexity = 11.0228

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0003
train_batch_size: 6
eval_batch_size: 6
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 16
total_train_batch_size: 192
total_eval_batch_size: 12
optimizer: Adam with betas=(0.9,0.95) and epsilon=0.0001
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 4
num_epochs: 6
mixed_precision_training: Native AMP

Framework versions

Transformers 4.35.2
Pytorch 2.1.2+cu121
Datasets 2.14.5
Tokenizers 0.14.1

Usage

from transformers import pipeline

pipe = pipeline("text-generation", model="ayousanz/japanese-mistral-0.3b-base")

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import torch

MODEL_NAME = "ayousanz/japanese-mistral-0.3b-base"
torch.set_float32_matmul_precision('high')

DEVICE = "cuda"
if torch.cuda.is_available():
    print("cuda")
    DEVICE = "cuda"
else:
    print("cpu")
    DEVICE = "cpu"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME,use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    trust_remote_code=True,
).to(DEVICE)

prompt = "大規模言語モデルとは、"

inputs = tokenizer(prompt, add_special_tokens=False,return_tensors="pt").to(model.device)
with torch.no_grad():

    outputs = model.generate(
        inputs["input_ids"],
        max_new_tokens=256,
        do_sample=True,
        early_stopping=False,
        top_p=0.95,
        top_k=50,
        temperature=0.9,
        no_repeat_ngram_size=2,
        num_beams=3
    )

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

ayousanz
/

japanese-mistral-0.3b-base