Model save

README.md

@@ -11,107 +11,49 @@ model-index:
   results: []
 ---
 
----
-## Model Description  
-[gemma-2b-it 모델](https://huggingface.co/google/gemma-2b-it)을 Instruction Tuning한 예제 모델입니다.  
-Instruction Tuning에 대해 쉽게 공부할 수 있도록 한글로된 예제 코드를 제공하고 있습니다.  
-**git hub** : [https://github.com/aiqwe/instruction-tuning-with-rag-example](https://github.com/aiqwe/instruction-tuning-with-rag-example)  
-
-## Usage
-### Inference on GPU example
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
-model = AutoModelForCausalLM.from_pretrained(
-    "aiqwe/gemma-2b-it-example-v1",
-    device_map="cuda",
-    torch_dtype=torch.bfloat16,
-    attn_implementation="flash_attention_2"
-)
-
-input_text = "아파트 재건축에 대해 알려줘."
-input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
-
-outputs = model.generate(**input_ids, max_new_tokens=512)
-print(tokenizer.decode(outputs[0]))
-
-```
-
-
-### Inference on CPU example
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
-model = AutoModelForCausalLM.from_pretrained(
-    "aiqwe/gemma-2b-it-example-v1",
-    device_map="cpu",
-    torch_dtype=torch.bfloat16
-)
-
-input_text = "아파트 재건축에 대해 알려줘."
-input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
-
-outputs = model.generate(**input_ids, max_new_tokens=512)
-print(tokenizer.decode(outputs[0]))
-```
-
-### Inference on GPU with embedded function example
-내장된 함수로 네이버 검색 API를 통해 RAG를 지원받습니다.
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM 
-from utils import generate
-
-tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
-model = AutoModelForCausalLM.from_pretrained(
-    "aiqwe/gemma-2b-it-example-v1",
-    device_map="cuda",
-    torch_dtype=torch.bfloat16,
-    attn_implementation="flash_attention_2"
-)
-
-rag_config = {
-    "api_client_id": userdata.get('NAVER_API_ID'),
-    "api_client_secret": userdata.get('NAVER_API_SECRET')
-}
-completion = generate(
-    model=model,
-    tokenizer=tokenizer,
-    query=query,
-    max_new_tokens=512,
-    rag=True,
-    rag_config=rag_config
-)
-print(completion)
-```
-
-## Chat Template
-Gemma 모델의 Chat Template을 사용합니다.  
-[gemma-2b-it Chat Template](https://huggingface.co/google/gemma-2b-it#chat-template)
-
-## Training Spec
-학습은 구글 코랩 L4 Single GPU를 활용하였습니다.  
-
-| 구분            | 내용                  |
-|---------------|---------------------|
-| 학습 환경         | Google Colab        |
-| GPU           | L4(22.5GB)          |
-| 학습시 VRAM      | 약 17GB 사용           |
-| dtype         | bfloat16            |
-| Attention     | flash attention2    |
-| Tuning        | Lora(r=4, alpha=32) |
-| Learning Rate | 5e-5                |
-| LRScheduler   | Cosine              |
-| Optimizer     | adamw_torch_fused   |
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# gemma-2b-it-example-v1
+
+This model is a fine-tuned version of [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it) on an unknown dataset.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.05
+- num_epochs: 20
+
+### Training results
+
+
 
 ### Framework versions
 
 - PEFT 0.10.0
 - Transformers 4.40.1
 - Pytorch 2.2.1+cu121
-- Datasets 2.19.0
-- Tokenizers 0.19.1
-
-## Github Profile
-Github : https://github.com/aiqwe
+- Datasets 2.19.1
+- Tokenizers 0.19.1