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README.md

@@ -1,53 +1,9 @@
 ---
 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     |
-|---------------------|----------:|
-| JCOMMONSENSEQA(acc) | 75.78     |
-| JNLI(acc)           | 50.69     |
-| MARC_JA(acc)        | 79.64     |
-| JSQUAD(exact_match) | 62.83     |
-| **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
@@ -61,4 +17,4 @@ The following `bitsandbytes` quantization config was used during training:
 ### Framework versions
 
 
-- PEFT 0.4.0
+- PEFT 0.4.0