---
library_name: peft
license: llama2
language:
- ja
pipeline_tag: text-generation
inference: false
tags:
- llama-2
- pytorch
- facebook
- meta
- text-generation-inference
---
# doshisha-mil/llama-2-70b-chat-4bit-japanese-v1

This model is Llama-2-Chat 70B fine-tuned with the following Japanese version of the alpaca dataset.

https://github.com/shi3z/alpaca_ja

## Copyright Notice

Since this model is built on the copyright of Meta's LLaMA series, users of this model must also agree to Meta's license.

https://ai.meta.com/llama/

## How to use



```
from huggingface_hub import notebook_login
notebook_login()
```


```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model_id = "meta-llama/Llama-2-70b-chat-hf"
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map="auto")

peft_name = "doshisha-mil/llama-2-70b-chat-4bit-japanese-v1"
model = PeftModel.from_pretrained(
    model, 
    peft_name, 
    is_trainable=True
)
model.eval()

device = "cuda:0"

text = "# Q: 日本一高い山は何ですか？ # A: "
inputs = tokenizer(text, return_tensors="pt").to(device)
with torch.no_grad():
  outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))


```
## Training procedure


The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float32
### Framework versions


- PEFT 0.4.0