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---
language:
- en
- ja
library_name: peft
datasets:
- HachiML/databricks-dolly-15k-ja-alpaca-format
base_model: meta-llama/Llama-2-13b-hf
---
## JGLUE Score
I evaluated this model using the following JGLUE tasks. Here are the scores:
| Task | Llama-2-13b-hf(*) | This Model |
|---------------------|:-----------------:|:----------:|
| JCOMMONSENSEQA(acc) | 75.06 | 75.78 |
| JNLI(acc) | 22.18 | 50.69 |
| MARC_JA(acc) | 38.83 | 79.64 |
| JSQUAD(exact_match) | 76.13 | 62.83 |
| **Average** | **53.05** | **67.23** |
- Note: Use v0.3 prompt template
- The JGLUE scores were measured using the following script:
[Stability-AI/lm-evaluation-harness](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable)
## How to use
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer
from peft import PeftModel
model_name = "meta-llama/Llama-2-13b-hf"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
pt_model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=bnb_config,
)
peft_name = "HachiML/Llama-2-13b-hf-qlora-dolly-ja-2ep"
model = PeftModel.from_pretrained(
pt_model,
peft_name,
)
```
## 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: float16
### Framework versions
- PEFT 0.4.0 |