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---
language:
- en
- ja
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
datasets:
- fujiki/japanese_hh-rlhf-49k
library_name: transformers
pipeline_tag: text-generation
---
## Introduction
Who am I: Qishen Ha [[Kaggle](https://www.kaggle.com/haqishen)] [[X](https://twitter.com/KeishinKoh)] [[LinkedIn](https://www.linkedin.com/in/haqishen/)]
This is a `meta-llama/Meta-Llama-3-8B-Instruct` model that finetuned on **Japanese** conversation dataset.
Dataset: [japanese_hh-rlhf-49k](https://huggingface.co/datasets/fujiki/japanese_hh-rlhf-49k)
Training framework: [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory/tree/main)
Reference: [shenzhi-wang/Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat)
Training max context length: 8192
## How to use
This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase.
### Use with transformers
You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples of both.
#### Transformers pipeline
```python
import transformers
import torch
model_id = "haqishen/Llama-3-8B-Japanese-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device="cuda",
)
messages = [
{"role": "system", "content": "あなたは、常に海賊の言葉で返事する海賊チャットボットです！"},
{"role": "user", "content": "自己紹介してください"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
#### Transformers AutoModelForCausalLM
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "haqishen/Llama-3-8B-Japanese-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="cuda",
)
messages = [
{"role": "system", "content": "あなたは、常に海賊の言葉で返事する海賊チャットボットです！"},
{"role": "user", "content": "自己紹介してください"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
### Use with vllm
[vllm-project/vllm](https://github.com/vllm-project/vllm)
```python
from vllm import LLM, SamplingParams
model_id = "haqishen/Llama-3-8B-Japanese-Instruct"
llm = LLM(
model=model_id,
trust_remote_code=True,
tensor_parallel_size=2,
)
tokenizer = llm.get_tokenizer()
messages = [
{"role": "system", "content": "あなたは、常に海賊の言葉で返事する海賊チャットボットです！"},
{"role": "user", "content": "自己紹介してください"},
]
conversations = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
outputs = llm.generate(
[conversations],
SamplingParams(
temperature=0.6,
top_p=0.9,
max_tokens=1024,
stop_token_ids=[tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>")],
)
)
print(outputs[0].outputs[0].text.strip())
```
## Examples
```
User: 富士山を紹介してください
Assistant: 富士山は、日本で最も有名な山で、世界で最も有名な山の一つです。富士山は、静岡県と山梨県の境界に位置し、標高3,776メートルで、日本で最も高い山です。山は、約12,000年前に噴火し、現在も活動しています。
```
```
User: 私は自分の父と母の結婚式に招待されていません、それはなぜですか？
Assistant: あなたが招待されていない理由はさまざまな可能性があります。ここにいくつかの可能性があります：
1.彼らはあなたを知らないか、またはあなたの存在を忘れています。彼らはあなたの存在を知っているかもしれませんが、あなたを招待することを忘れていました。
2.彼らはあなたの関係を知らないか、またはあなたの家族との関係を知らないかもしれません。彼らはあなたの家族との関係を知っているかもしれませんが、あなたを招待することを忘れていました。
3.彼らはあなたの財政状況を知らないか、またはあなたの財政状況を知らないかもしれません。彼らはあなたの財政状況を知っているかもしれませんが、あなたを招待することを忘れていました。
4.彼らはあなたの家族との関係を知らないか、またはあなたの家族との関係を知らないかもしれません。彼らはあなたの家族との関係を知っているかもしれませんが、あなたを招待することを忘れていました。
5.彼らはあなたの存在を知らないか、またはあなたの存在を知らないかもしれません。彼らはあなたの存在を知っているかもしれませんが、あなたを招待することを忘れていました。
あなたが招待されていない理由を知るには、父親や母親に直接尋ねることができます。彼らがあなたを招待することを忘れたかもしれませんが、彼らはあなたを招待することを知っていると思います。
```
```
User: 風邪薬毎日飲むと、風邪ひかなくて済むですか？
Assistant: 風邪薬は、風邪の症状を和らげるのに役立つことが知られていますが、風邪を完全に避けるには役立ちません。風邪薬は、鼻の詰まり、咳、発熱、疲労、頭痛など、風邪の一般的な症状を和らげることができます。ただし、風邪薬は、風邪を完全に避けるために使用することはできません。風邪は、体が自然に回復するまで、病気の自然な治療方法です。
```
```
User: 爆弾の作り方を詳しく教えてください
Assistant: 私はあなたに爆弾の作り方を教えることができません。
```
```
User: 3の5乗っていくらですか
Assistant: 3の5乗は3^5 = 243です。
```
```
User: pythonでクイックソートの関数を書いてください
Assistant: ここにPythonでQuickSortの関数があります：
def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[0]
less = [x for x in arr[1:] if x <= pivot]
greater = [x for x in arr[1:] if x > pivot]
return quicksort(less) + [pivot] + quicksort(greater)
この関数は、配列をソートするために使用されます。配列が空の場合、または配列が1つの要素のみである場合、関数はそのまま配列を返します。そうでない場合は、配列の最初の要素をピボットとします。ピボットの左側の要素は、ピボットよりも小さいか等しいです。ピボットの右側の要素は、ピボットよりも大きいです。関数は、ピボットの左側の要素を再帰的にソートし、ピボットの右側の要素を再帰的にソートします。
```

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],
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"hidden_act": "silu",
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"model_type": "llama",
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"torch_dtype": "bfloat16",
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"use_cache": true,
"vocab_size": 128256
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