metadata
base_model:
- oxygen65/llm-jp-3-13b-finetune-2
- llm-jp/llm-jp-3-13b
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: cc-by-nc-sa-4.0
language:
- ja
datasets:
- elyza/ELYZA-tasks-100
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.
