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---
base_model: llm-jp/llm-jp-3-13b
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **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)
# The models have been fine-tuned on the following datasets.
Dataset:ichikara-instruction-003-001-1.json
description:A manually constructed instruction dataset
データセット作成チーム： 関根聡, 安藤まや, 後藤美知子, 鈴木久美, 河原大輔, 井之上直也, 乾健太郎.
ichikara-instruction: LLMのための日本語インストラクションデータの構築. 言語処理学会第30回年次大会(2024)
I’m grateful to everyone here.
# Usage
```python
!pip uninstall unsloth -y
!pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
!pip install ipywidgets --upgrade
import torch
if torch.cuda.get_device_capability()[0] >= 8:
!pip install --no-deps packaging ninja einops "flash-attn>=2.6.3"
HF_TOKEN = "HF_TOKEN" #@param {type:"string"}
from unsloth import FastLanguageModel
import torch
max_seq_length = 512
dtype = None
load_in_4bit = True
model_id = "llm-jp/llm-jp-3-13b"
new_model_id = "model name"
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=model_id,
dtype=dtype,
load_in_4bit=load_in_4bit,
trust_remote_code=True,
)
model = FastLanguageModel.get_peft_model(
model,
r = 32,
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",],
lora_alpha = 32,
lora_dropout = 0.05,
bias = "none",
use_gradient_checkpointing = "unsloth",
random_state = 3407,
use_rslora = False,
loftq_config = None,
max_seq_length = max_seq_length,
)
from datasets import load_dataset
dataset = load_dataset("json", data_files="./ichikara-instruction-003-001-1.json")
prompt = """### 指示
{}
### 回答
{}"""
EOS_TOKEN = tokenizer.eos_token
def formatting_prompts_func(examples):
input = examples["text"]
output = examples["output"]
text = prompt.format(input, output) + EOS_TOKEN
return { "formatted_text" : text, }
pass
dataset = dataset.map(
formatting_prompts_func,
num_proc= 4,
)
from trl import SFTTrainer
from transformers import TrainingArguments
from unsloth import is_bfloat16_supported
trainer = SFTTrainer(
model = model,
tokenizer = tokenizer,
train_dataset=dataset["train"],
max_seq_length = max_seq_length,
dataset_text_field="formatted_text",
packing = False,
args = TrainingArguments(
per_device_train_batch_size = 2,
gradient_accumulation_steps = 4,
num_train_epochs = 1,
logging_steps = 10,
warmup_steps = 10,
save_steps=100,
save_total_limit=2,
max_steps=-1,
learning_rate = 2e-4,
fp16 = not is_bfloat16_supported(),
bf16 = is_bfloat16_supported(),
group_by_length=True,
seed = 3407,
output_dir = "outputs",
report_to = "none",
),
)
trainer_stats = trainer.train()
import json
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 = ""
from tqdm import tqdm
FastLanguageModel.for_inference(model)
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})
from tqdm import tqdm
FastLanguageModel.for_inference(model)
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})
with open(f"{new_model_id}_output.jsonl", 'w', encoding='utf-8') as f:
for result in results:
json.dump(result, f, ensure_ascii=False)
f.write('\n') |