README.md

---
library_name: peft
datasets:
- ohtaman/kokkai2022
language:
- ja
pipeline_tag: text-generation
---
## Training procedure

Finetune [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) with [ohtaman/kokkai2022](https://huggingface.co/datasets/ohtaman/kokkai2022)(currentry, private) dataset with LoRA.
The training parameters are

|param|value|
|:--:|:--:|
|r| 4|
|lora_alpha| 2|
|target_modules|- query_key_value<br> - dense<br> - dense_h_to_4h<br> - dense_4h_to_h|
|lora_dropout| 0.01|
|bias| None|
|task_type| CAUSAL_LM|
|optimizer|AdamW|
|lr|4e-4|

the prompt is something like

```
# question
{questioner}

{question_text}

# answer
{answerer}

{answer_text}

```

### Framework versions

- PEFT 0.4.0.dev0

### Example Notebook (Colab)

[Colaboratory](https://colab.research.google.com/drive/1oWHM5_DbltvrD27oZL4-fumXChkMkrC5?usp=sharing) (Pro is not needed.)

### Example Code

```python
tokenizer = transformers.AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True)
base_model = transformers.AutoModelForCausalLM.from_pretrained(base_model_name, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)
peft_model = peft.PeftModelForCausalLM.from_pretrained(base_model, peft_model_name, torch_dtype=torch.bfloat16)


prompt = "# question\n麻生太郎\n\n増税すべきとお考えか？\n# answer\n岸田文雄\n\n〔内閣総理大臣岸田文雄君登壇〕"
input_tokens = tokenizer(prompt, return_tensors="pt").to(peft_model.device)
input_length = input_tokens.input_ids.shape[1]

with torch.no_grad():
    outputs = peft_model.generate(
        input_ids=input_tokens["input_ids"],
        attention_mask=input_tokens["attention_mask"],
        return_dict_in_generate=True,
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.pad_token_id,
        max_length=max_length,
        temperature=0.7,
        top_p=0.9,
        repetition_penalty=1.05,
    )
    output_tokens = outputs.sequences[0, input_length:-1]

print(tokenizer.decode(output_tokens))
```