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---
language:
- ja
license: llama2
library_name: peft
tags:
- llama-2
- pytorch
- facebook
- meta
- text-generation-inference
pipeline_tag: text-generation
inference: false
base_model: meta-llama/Llama-2-70b-chat-hf
---
# 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
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