ce-lery/japanese-mistral-300m-instruction-GGUF

Quantized GGUF model files for japanese-mistral-300m-instruction from ce-lery

Name	Quant method	Size
japanese-mistral-300m-instruction.fp16.gguf	fp16	712.33 MB
japanese-mistral-300m-instruction.q2_k.gguf	q2_k	176.84 MB
japanese-mistral-300m-instruction.q3_k_m.gguf	q3_k_m	195.04 MB
japanese-mistral-300m-instruction.q4_k_m.gguf	q4_k_m	234.80 MB
japanese-mistral-300m-instruction.q5_k_m.gguf	q5_k_m	266.47 MB
japanese-mistral-300m-instruction.q6_k.gguf	q6_k	307.38 MB
japanese-mistral-300m-instruction.q8_0.gguf	q8_0	379.17 MB

Original Model Card:

japanese-mistral-300m-instruction

Overview

Welcome to my model card!

This Model feature is ...

• Suppression of unknown word generation by using byte fallback in SentencePiece tokenizer and conversion to huggingface Tokenizers format
• Pretrained by wikipedia dataset and cc100 dataset
• Use of Mistral 300M
• Fine-tuning ce-lery/japanese-mistral-300m-base with kunishou/databricks-dolly-15k-ja

Yukkuri shite ittene!

How to use the model

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import os

MODEL_NAME = "ce-lery/japanese-mistral-300m-instruction"
torch.set_float32_matmul_precision('high')

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

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

MAX_ASSISTANT_LENGTH = 100
MAX_INPUT_LENGTH = 128
INPUT_PROMPT = r'<s>\n以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさい。\n[SEP]\n指示:\n{instruction}\n[SEP]\n入力:\n{input}\n[SEP]\n応答:\n'
NO_INPUT_PROMPT = r'<s>\n以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。\n[SEP]\n指示:\n{instruction}\n[SEP]\n応答:\n'

def prepare_input(instruction, input_text):
    if input_text != "":
        prompt = INPUT_PROMPT.format(instruction=instruction, input=input_text)
    else:
        prompt = NO_INPUT_PROMPT.format(instruction=instruction)
    return prompt

def format_output(output):
    output = output.lstrip("<s>").rstrip("</s>").replace("[SEP]", "").replace("\\n", "\n")
    return output

def generate_response(instruction, input_text):
    prompt = prepare_input(instruction, input_text)
    token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
    n = len(token_ids[0])
    # print(n)

    with torch.no_grad():
        output_ids = model.generate(
            token_ids.to(model.device),
            min_length=n,
            max_length=min(MAX_INPUT_LENGTH, n + MAX_ASSISTANT_LENGTH),
            top_p=0.95,
            top_k=50,
            temperature=0.4,
            do_sample=True,
            no_repeat_ngram_size=2,
            num_beams=3,
            pad_token_id=tokenizer.pad_token_id,
            bos_token_id=tokenizer.bos_token_id,
            eos_token_id=tokenizer.eos_token_id,
            bad_words_ids=[[tokenizer.unk_token_id]]
        )

    output = tokenizer.decode(output_ids.tolist()[0])
    formatted_output_all = format_output(output)
    response = f"Assistant:{formatted_output_all.split('応答:')[-1].strip()}"

    return formatted_output_all, response 

instruction = "あなたは何でも正確に答えられるAIです。"
questions = [
    "日本で一番高い山は？",
    "日本で一番広い湖は？",
    "世界で一番高い山は？",
    "世界で一番広い湖は？",
    "冗談を言ってください。",
]

# 各質問に対して応答を生成して表示
for question in questions:
    formatted_output_all, response = generate_response(instruction, question)
    print(response)

Receipe

If you want to restruct this model, you can refer this Github repository.

I wrote the receipe for struction this model. For example,

• Preprocess with sentencepiece
• Pretraining with flash attention2 and torch.compile and DeepSpeed
• Fine-tuning with databricks-dolly-15k-ja

If you find my mistake,error,...etc, please create issue. If you create pulreqest, I'm very happy!

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-06
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• gradient_accumulation_steps: 64
• total_train_batch_size: 256
• optimizer: Adam with betas=(0.9,0.95) and epsilon=0.0001
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 1000
• num_epochs: 200
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
3.595	3.51	40	3.5299
3.4769	7.02	80	3.3722
3.3037	10.53	120	3.1871
3.1255	14.05	160	3.0088
2.9615	17.56	200	2.8684
2.8468	21.07	240	2.7808
2.7699	24.58	280	2.7205
2.7139	28.09	320	2.6793
2.6712	31.6	360	2.6509
2.6356	35.12	400	2.6294
2.6048	38.63	440	2.6120
2.5823	42.14	480	2.5974
2.5536	45.65	520	2.5849
2.5293	49.16	560	2.5740
2.5058	52.67	600	2.5644
2.482	56.19	640	2.5556
2.4575	59.7	680	2.5477
2.4339	63.21	720	2.5405
2.4073	66.72	760	2.5350
2.3845	70.23	800	2.5303
2.3606	73.74	840	2.5253
2.329	77.26	880	2.5215
2.3071	80.77	920	2.5185
2.2768	84.28	960	2.5155
2.2479	87.79	1000	2.5144
2.2181	91.3	1040	2.5151
2.1901	94.81	1080	2.5139
2.1571	98.33	1120	2.5148
2.1308	101.84	1160	2.5166
2.1032	105.35	1200	2.5193
2.0761	108.86	1240	2.5204
2.0495	112.37	1280	2.5269
2.0231	115.88	1320	2.5285
2.0021	119.4	1360	2.5328
1.9793	122.91	1400	2.5383
1.9575	126.42	1440	2.5442
1.9368	129.93	1480	2.5488
1.9216	133.44	1520	2.5534
1.902	136.95	1560	2.5584
1.8885	140.47	1600	2.5609
1.8728	143.98	1640	2.5657
1.8605	147.49	1680	2.5697
1.8476	151.0	1720	2.5741
1.8402	154.51	1760	2.5770
1.8274	158.02	1800	2.5803
1.8218	161.54	1840	2.5829
1.8144	165.05	1880	2.5847
1.8097	168.56	1920	2.5867
1.8076	172.07	1960	2.5883
1.8014	175.58	2000	2.5892
1.8001	179.09	2040	2.5899
1.7987	182.61	2080	2.5903
1.7971	186.12	2120	2.5906
1.7979	189.63	2160	2.5907
1.7975	193.14	2200	2.5907

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.1+cu121
• Datasets 2.14.5
• Tokenizers 0.14.1