Gemma2-27b-ft-jp-r64_alpha64

Uploaded model

• Developed by: tomo1222
• License: Gemma
• Finetuned from model : tomo1222/gemma-2-27b-bf16-4bit

tomo1222/gemma-2-27b-bf16-4bit : google/gemma-2-27bをUnslothで直接用いるために、BitsAndBytesを用いて4bit量子化し、そのまま保存したもの。

This gemma2 model was trained 2x faster with Unsloth and Huggingface's TRL library.

<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>

output code

library

pip install unsloth
pip install --no-deps --upgrade "flash-attn>=2.6.3"
pip install -U ragatouille
pip install fugashi unidic-lite

inference code using Google Colaboratory(L4)

from datasets import concatenate_datasets, load_dataset
from unsloth import FastLanguageModel
import random
import json

from huggingface_hub import login
from google.colab import userdata
login(userdata.get('HFtoken'))


with open("elyza-tasks-100-TV_0.jsonl","r",encoding='utf-8') as f:
    tasks = [json.loads(l) for l in f.readlines()]

model_name = "tomo1222/Gemma2-27b-ft-jp-r64_alpha64"


max_seq_length = 4096

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = model_name,
    max_seq_length = max_seq_length,
    dtype = None,
    load_in_4bit = True,
)

# google/gemma-2-9bのテンプレート
tokenizer.chat_template = """
{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}
"""
FastLanguageModel.for_inference(model) # Enable native 2x faster inference

dataset = load_dataset("tomo1222/Japanese-QA111dataset")
ref_tasks = list(dataset["train"])  
ref_tasks_input = [task["input"] for task in ref_tasks]

dic = {}
dic_input = {}
for i, task in enumerate(ref_tasks):
  dic[ref_tasks_input[i]] = task["output"]
  dic_input[ref_tasks_input[i]] = task["input"]

"""# 2. RAGのロード"""

from ragatouille import RAGPretrainedModel
RAG = RAGPretrainedModel.from_pretrained("bclavie/JaColBERTv2")
RAG.encode(ref_tasks_input)

def search_ref_input(input, k=10):
  retreived=RAG.search_encoded_docs(query=input,k=k)
  print(retreived)
  text ="質問・文章をよく読んで、正確で親切な回答を書きなさい。\n"
  for data in retreived[::-1]: # inverse order
    key = data["content"]
    output = dic[key]
    input = dic_input[key]
    text+="### 質問:\n"+input+"\n\n### 回答:\n"+output+"\n\n\n"
  return text

"""# Prompt"""
output_data=[]

for i, task in enumerate(tasks):
  text = (
    search_ref_input(task["input"], 20)
    + "あなたは日本語が堪能な優秀な人間です。\n"
    + "**文脈**を踏まえて、改行と箇条書きを駆使して、日本語で**詳細に**書きなさい。\n"
    + "優秀な人間になりきって、推測をいれずに根拠をもってわかりやすく答えてください。"
    + f"### 質問:\n{task['input']}\n\n### 回答:\n"
  )
  print(task["input"])
  inputs = tokenizer(text, return_tensors="pt").to("cuda")
  print(len(inputs['input_ids'][0]))
  output = model.generate(**inputs, max_new_tokens=1024,repetition_penalty=1.1,use_cache=True,
                          bad_words_ids = [tokenizer.encode("質問", add_special_tokens=False),
                                            tokenizer.encode("###", add_special_tokens=False),
                                            tokenizer.encode("#", add_special_tokens=False),
                                            tokenizer.encode("##", add_special_tokens=False),
                                            tokenizer.encode("---", add_special_tokens=False),
                                            tokenizer.encode("<h3>", add_special_tokens=False),
                                            tokenizer.encode("filepath", add_special_tokens=False),
                                            tokenizer.encode("> ", add_special_tokens=False),
                                          ]
                          )

  output_text = tokenizer.decode(output[0][inputs.input_ids.size(1):], skip_special_tokens=True).strip()
  print(i,output_text)
  print("---")
  output_data.append({"task_id":i,"output":output_text})

with open("output.jsonl","w",encoding="utf-8") as f:
    for result in output_data:
        json.dump(result, f, ensure_ascii=False)
        f.write('\n')