metadata

base_model: llm-jp/llm-jp-3-13b
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - llama
  - trl
license: cc
language:
  - ja
datasets:
  - elyza/ELYZA-tasks-100
  - weblab-GENIAC/aya-ja-evol-instruct-calm3-dpo-masked

Uploaded model

Developed by: JunichiroMorita
License: CC-BY-NC-SA
Finetuned from model : llm-jp/llm-jp-3-13b

Usage

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("JunichiroMorita/llm-jp-3-13b_lora_20241201")
model = AutoModelForCausalLM.from_pretrained("JunichiroMorita/llm-jp-3-13b_lora_20241201", device_map="auto", torch_dtype=torch.bfloat16)
chat = [
    {"role": "system", "content": "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。"},
    {"role": "user", "content": "自然言語処理とは何か"},
]
tokenized_input = tokenizer.apply_chat_template(chat, add_generation_prompt=True, tokenize=True, return_tensors="pt").to(model.device)
with torch.no_grad():
    output = model.generate(
        tokenized_input,
        max_new_tokens=2048,
        do_sample=True,
        top_p=0.95,
        temperature=0.7,
        repetition_penalty=1.05,
    )[0]
print(tokenizer.decode(output))

Data

[1]:関根聡, 安藤まや, 後藤美知子, 鈴木久美, 河原大輔, 井之上直也, 乾健太郎. ichikara-instruction: LLMのための日本語インストラクションデータの構築. 言語処理学会第30回年次大会(2024)