File size: 3,801 Bytes
9632d0d fde9c57 9632d0d fde9c57 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 |
---
base_model: llm-jp/llm-jp-3-13b
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- ja
---
# Uploaded model
- **Developed by:** tomofusa
- **License:** apache-2.0
- **Finetuned from model :** llm-jp/llm-jp-3-13b
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
---
# How to use
There are the normal steps from sample codes.
0. ready to (you can skip this step in Google Colaboratry. )
```shell
# conda環境の構築
wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
# このコマンドではいくつか質問があるので答えて下さい。おそらくインストール先のデフォルトは/root/miniforge3かと思います
bash Miniforge3-$(uname)-$(uname -m).sh
# 以下、インストール先が/root/miniforge3であることを前提とします
export PATH=/root/miniforge3/bin:$PATH
conda init
# ここで一度、terminalを立ち上げ直す必要があります。
# 以下のリンク先に従い環境を作ります。
# https://docs.unsloth.ai/get-started/installation/conda-install
conda create --name unsloth_env python=3.10 pytorch-cuda=12.1 pytorch cudatoolkit xformers -c pytorch -c nvidia -c xformers -y
conda activate unsloth_env
pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
pip install --no-deps "trl<0.9.0" peft accelerate bitsandbytes
# jupyter notebook用のセットアップ。
conda install -c conda-forge ipykernel
python -m ipykernel install --user --name=unsloth_env --display-name "Python (unsloth_env)"
```
## Follow these steps, run in the notebook:
1. load model
```shell
%%capture
!pip install unsloth
!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
```
```python
from unsloth import FastLanguageModel
import torch
import json
model_name = "tomofusa/llm-jp-3-13b-finetune-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,
# token = "hf-token", # In the Google Colab case, it call from ENV. If you want to write the token directly, please comment it out.
)
FastLanguageModel.for_inference(model)
```
3. Set up datasets and run inference.
- Upload elyza-tasks-100-TV_0.jsonl to your workspace in manual.
```python
datasets = []
with open("./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 = ""
```
```python
from tqdm import tqdm
# inference
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})
```
4. Save results to jsonl.
```python
file_name = model_name.replace("/", "_") + "_output.jsonl"
with open(f"./{file_name}", 'w', encoding='utf-8') as f:
for result in results:
json.dump(result, f, ensure_ascii=False)
f.write('\n')
```
|