Downloads · 30 days
0
aki916/llm-jp-3-13b-it-v1.2
llm-jp-3-13b-it-v1.2 is a machine learning model from aki916. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
- Developed by: aki916 - License: apache-2.0 - Finetuned from model : llm-jp/llm-jp-3-13b
Downloads · 30 days
0
Access
Public
Updated Dec 16, 2024
Repo size
1 GB
Likes
0
Public
Click a slice to open those files.
.safetensors501 MB · 99%
From the Hugging Face model README
import json
from tqdm import tqdm
import sys
import os
import openai
from tenacity import (
retry,
stop_after_attempt,
wait_random_exponential,
) # for exponential backoff
from tqdm import tqdm
from datasets import load_dataset
import torch
from unsloth import FastLanguageModel
def main():
model_name = "aki916/llm-jp-3-13b-it-v1.2"
max_seq_length = 2048
dtype = None
load_in_4bit = True
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = model_name,
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
)
FastLanguageModel.for_inference(model)
# データセットの読み込み。
datasets = []
with open("data/elyza-tasks-100-TV_0.jsonl", "r") as f:
item = ""
for line in f:
line = line.strip()
item += line
if item.endswith("}"):
datasets.append(json.loads(item))
item = ""
# 推論
results = []
for dt in tqdm(datasets):
input = dt["input"]
prompt = f"""### 指示\n{input}\n### 回答\n"""
inputs = tokenizer([prompt], return_tensors = "pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens = 512, use_cache = True, do_sample=False, repetition_penalty=1.2)
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 回答')[-1]
results.append({"task_id": dt["task_id"], "input": input, "output": prediction})
# jsonlで保存
with open(f"{model_name.replace('/', '_', -1)}_output.jsonl", 'w', encoding='utf-8') as f:
for result in results:
json.dump(result, f, ensure_ascii=False)
f.write('\n')
print('finish dump')
print(f"{model_name.replace('/', '_', -1)}_output.jsonl")
if __name__ == "__main__":
main()
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.