File size: 1,682 Bytes
0c255b9 684cf74 c03573d 0c255b9 3a61f88 4c8175c 1b33b53 1795a6e c4761a6 8bcec92 3a61f88 c03573d 0c255b9 c03573d 0c255b9 684cf74 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 |
---
library_name: peft
datasets:
- HachiML/databricks-dolly-15k-ja-for-peft
language:
- en
- ja
---
## JGLUE Score
We evaluated our model using the following JGLUE tasks. Here are the scores:
| Task | Score |
|----------------|---------:|
| JSQUAD(exact_match) | 62.83 |
| JCOMMONSENSEQA(acc) | 75.78 |
| JNLI(acc) | 50.69 |
| MARC_JA(acc) | 79.64 |
| **Average** | **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 |