Update README.md

README.md

@@ -8,8 +8,68 @@ 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]
 
-- PEFT 0.4.0.dev0
+print(tokenizer.decode(output_tokens))
+```