How to Use

1. load this model and tokenizer

from transformers import (
 AutoModelForCausalLM,
 AutoTokenizer,
 BitsAndBytesConfig,
)
import torch
from tqdm import tqdm
import json

model_name = "oxygen65/llm-jp-3-13b-finetune-3"

# QLoRA config
bnb_config = BitsAndBytesConfig(
 load_in_4bit=True,
 bnb_4bit_quant_type="nf4",
 bnb_4bit_compute_dtype=torch.bfloat16,
 bnb_4bit_use_double_quant=False,
)

# Load model
model = AutoModelForCausalLM.from_pretrained(
 model_name,
 quantization_config=bnb_config,
 device_map="auto",
)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

2. load Eval Datasets

tasks = []
with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
 item = ""
 for line in f:
 line = line.strip()
 item += line
 if item.endswith("}"):
 tasks.append(json.loads(item))
 item = ""

from datasets import load_dataset
sample_task_ds = load_dataset("elyza/ELYZA-tasks-100")
sample_tasks = sample_task_ds['test']
sample_tasks['input'][0]

3. set up retrievers

if you can't find "rank_bm25" python package in your environment

!pip install rank_bm25

from rank_bm25 import BM25Okapi
from nltk.tokenize import word_tokenize
import nltk
import numpy as np


# 必要なデータをダウンロード（初回のみ）
nltk.download('punkt')
nltk.download('punkt_tab')

def search_similar_documents_bm25(query, sample_tasks):
 # トークン化（BM25はトークン化されたデータを要求します）
 tokenized_documents = [word_tokenize(doc) for doc in sample_tasks['input']]

 # BM25オブジェクトの作成
 bm25 = BM25Okapi(tokenized_documents)

 tokenized_query = word_tokenize(query)
 # 類似度の計算
 doc_scores = bm25.get_scores(tokenized_query)
 # 類似度が高い順にソート
 sorted_indexes = np.argsort(doc_scores)[::-1]

 indexes = []
 for i in range(len(doc_scores)):
 if doc_scores[sorted_indexes[i]] < 20.0:
 break
 else:
 indexes.append(sorted_indexes[i])
 
 return indexes

from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
SentTF = SentenceTransformer('all-MiniLM-L6-v2')
def seearch_similar_documents_neuralRetriver(query, sample_tasks):
 global SentTF
 emb1 = SentTF.encode([query])
 emb2 = SentTF.encode(sample_tasks['input'])
 # 全ての組み合わせで類似度を計算
 similarity_matrix = cosine_similarity(emb1, emb2) #時間かかるので先に計算しておくべき
 # 類似度が高い順にソート
 sorted_indexes = np.argsort(similarity_matrix[0])[::-1]
 #print(sorted_indexes)
 
 indexes = []
 for i in range(len(sample_tasks['input'])):
 if similarity_matrix[0][sorted_indexes[i]] < 0.75:
 break
 else:
 indexes.append(sorted_indexes[i])
 
 return indexes

def create_icl_prompt(input, sample_tasks, task_id):
 indexes_bm25 = search_similar_documents_bm25(input, sample_tasks)
 indexes_neu = seearch_similar_documents_neuralRetriver(input, sample_tasks)
 indexes = list(set(indexes_bm25 + indexes_neu))
 icl_prompt = ""
 if indexes == []:
 return ""
 
 icl_prompt = f"""## 例題\n"""
 for i in range(len(indexes)):
 icl_prompt += f"""### 指示
{sample_tasks["input"][indexes[i]]}
### 回答
{sample_tasks["output"][indexes[i]]}
"""
 icl_prompt += f"""
## 本題: 以下の指示に従って回答してください。step by stepで回答してください。
"""
 return icl_prompt 

create_icl_prompt(tasks[2]["input"], sample_tasks, 0)

4. Inference

# llmjp
import re
pattern = r"^以下.*$"

# プロンプトの作成
sys_prompt = ""
icl_prompt = ""
results = []
loop = 0
for data in tqdm(tasks):
 task_id = data["task_id"]
 input = data["input"]
 # in context learning用のプロンプト
 icl_prompt = create_icl_prompt(input, sample_tasks, task_id)
 
 prompt = f"""{sys_prompt}{icl_prompt}### 指示
{input}
### 回答
""" 
 tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
 with torch.no_grad():
 outputs = model.generate(
 tokenized_input,
 max_new_tokens=512,
 do_sample=False,
 repetition_penalty=1.2,
 eos_token_id=tokenizer.eos_token_id,
 )[0]
 output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)

 while (True): #とりあえず出力。
 line = output.splitlines()
 if re.match(pattern, line[0]) and len(line) == 1:
 print(f"#========================= Unexpected answer =========================#\n {line}")
 outputs = model.generate(
 tokenized_input,
 max_new_tokens=512,
 do_sample=True,
 temperature=0.4,
 repetition_penalty=1.2
 )[0]
 output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)
 else: break 


 results.append({"task_id": data["task_id"], "input": input, "output": output})
 
 print(f"task_id: {data['task_id']}, prompt: {prompt}, output: {output}")
 

5. Dump results

import re
model_name = re.sub(".*/", "", model_name)
with open(f"./{model_name}-outputs.jsonl", 'w', encoding='utf-8') as f:
 for result in results:
 json.dump(result, f, ensure_ascii=False) # ensure_ascii=False for handling non-ASCII characters
 f.write('\n')

Uploaded model

• Developed by: oxygen65

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

Base model