mistral-300M

mistralアーキテクチャを使った日本語LLM(0.3B)

学習環境

• A5000 × 7

学習パラメータ

hf_config.json

{
    "model_type": "gpt2",
    "config_name":"gpt2-medium" ,
    "tokenizer_name":"/home/ubuntu/slm/spm_tokenizer_neologdn_bytefallback_nofast" ,
    "train_file":"../wiki.txt",
    "validation_split_percentage":5,
    "output_dir":"checkpoints-mistral-300M-FA2-3",
    "do_train":true,
    "do_eval":true,
    "prediction_loss_only":true,
    "remove_unused_columns":false ,
    "learning_rate":3.0e-4 ,
    "weight_decay":0.1 ,
    "adam_beta2":0.95 ,
    "num_train_epochs":10,
    "logging_dir":"checkpoints-mistral-300M-FA2-3/logs",
    "logging_strategy": "steps" ,
    "logging_steps":10 ,
    "evaluation_strategy":"steps" ,
    "save_strategy": "steps" ,
    "eval_steps":500 ,
    "save_steps":500 ,
    "load_best_model_at_end":true ,
    "save_total_limit":10 ,
    "warmup_steps":4 ,
    "lr_scheduler_type":"cosine" ,
    "per_device_train_batch_size":8,
    "per_device_eval_batch_size":8,
    "block_size":1024 ,
    "adam_epsilon":1.0e-4 ,
    "fp16":true ,
    "gradient_accumulation_steps":16,
    "push_to_hub":false,
    "dataloader_num_workers": 8,
    "optim":"adamw_bnb_8bit" ,
    "torch_compile":true
}

モデルパラメータ

{
  "architectures": [
    "MistralForCausalLM"
  ],
  "bos_token_id": 0,
  "eos_token_id": 0,
  "hidden_act": "silu",
  "hidden_size": 1024,
  "initializer_range": 0.02,
  "intermediate_size": 2400,
  "max_position_embeddings": 4096,
  "model_type": "mistral",
  "num_attention_heads": 16,
  "num_hidden_layers": 24,
  "num_key_value_heads": 8,
  "rms_norm_eps": 1e-05,
  "rope_theta": 10000.0,
  "sliding_window": 1024,
  "tie_word_embeddings": false,
  "torch_dtype": "float16",
  "transformers_version": "4.35.2",
  "use_cache": true,
  "vocab_size": 50257
}

deepspeedのパラメータ

{
    "fp16": {
        "enabled": "auto",
        "loss_scale": 0.0,
        "loss_scale_window": 1000,
        "initial_scale_power": 16,
        "hysteresis": 1,
        "min_loss_scale": 0
    },
    
    "bf16": {
        "enabled": "auto"
    },
    

    "optimizer": {
        "type": "AdamW",
        "params": {
            "lr": "auto",
            "betas": "auto",
            "eps": "auto",
            "weight_decay": "auto"
        }
    },

    "scheduler": {
        "type": "WarmupDecayLR",
        "params": {
            "warmup_min_lr": "auto",
            "warmup_max_lr": "auto",
            "warmup_num_steps": "auto",
            "total_num_steps": "auto"
        }
    },

    "zero_optimization": {
        "stage": 0,
        "allgather_partitions": true,
        "allgather_bucket_size": 2e6,
        "overlap_comm": true,
        "reduce_scatter": true,
        "reduce_bucket_size": 2e6,
        "contiguous_gradients": true,
        "round_robin_gradients":true
    },

    "dump_state": true,
    "comms_logger": {
      "enabled": true,
      "verbose": false,
      "prof_all": true,
      "debug": false
    },
    
    "gradient_accumulation_steps": "auto",
    "gradient_clipping": "auto",
    "train_batch_size": "auto",
    "train_micro_batch_size_per_gpu": "auto"

 
}

TensorBoardの結果

eval/loss

train/learning_rate

train/loss

モデルの推論コード

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import torch

MODEL_NAME = "./pretrain/checkpoints-mistral-300M-FA2-3/checkpoint-12000/"
torch.set_float32_matmul_precision('high')

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

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

# streamer = TextStreamer(tokenizer)

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=100,
        do_sample=True,
        early_stopping=False,
        top_p=0.95,
        top_k=50,
        temperature=0.9,
        # streamer=streamer,
        no_repeat_ngram_size=2,
        num_beams=3
    )

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


prompt = "まどマギで一番可愛いキャラは、"


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=100,
        do_sample=True,
        early_stopping=False,
        top_p=0.95,
        top_k=50,
        temperature=0.9,
        # streamer=streamer,
        no_repeat_ngram_size=2,
        num_beams=3
    )

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

備考

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