LEESM
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llama-2-7b-hf-lora-oki100p

@@ -1,199 +1,87 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
 library_name: transformers
+license: mit
+datasets:
+- heegyu/open-korean-instructions
+language:
+- ko
+tags:
+- Llama-2-7b-hf
+- LoRA
 ---
+# Llama-2 model fine tuning (Seoul Cyber University TREX-Lab)
 <!-- Provide a quick summary of what the model is/does. -->
+## Summary
+  - Base Model : meta-llama/Llama-2-7b-hf
+  - Dataset : heegyu/open-korean-instructions (100%)
+  - Tuning Method
+    - PEFT(Parameter Efficient Fine-Tuning)
+    - LoRA(Low-Rank Adaptation of Large Language Models)
+  - Related Articles : https://arxiv.org/abs/2106.09685
+  - Fine-tuning the Llama2 model with a random 100% of Korean chatbot data (open Korean instructions)
+  - Test whether fine tuning of a large language model is possible on A30 GPU*1 (successful)
 <!-- Provide a longer summary of what this model is. -->
+- **Developed by:** [SM.Lee of Seoul Cyber University]
+- **Language(s) (NLP):** [Korean]
+- **Finetuned from model :** [meta-llama/Llama-2-7b-hf]
+## Fine Tuning Detail
+- alpha value 16
+- r value 64 (it seems a bit big...@@)
+```
+peft_config = LoraConfig(
+    lora_alpha=16,
+    lora_dropout=0.1,
+    r=64,
+    bias='none',
+    task_type='CAUSAL_LM'
+)
+```
+- Mixed precision : 4bit (bnb_4bit_use_double_quant)
+```
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type='nf4',
+    bnb_4bit_compute_dtype='float16',
+)
+```
+- Use SFT trainer (https://huggingface.co/docs/trl/sft_trainer)
+```
+trainer = SFTTrainer(
+    model=peft_model,
+    train_dataset=dataset,
+    dataset_text_field='text',
+    max_seq_length=min(tokenizer.model_max_length, 2048),
+    tokenizer=tokenizer,
+    packing=True,
+    args=training_args
+)
+```
+### Train Result
+```
+time taken : executed in 2d 0h 17m
+```
+```
+TrainOutput(global_step=2001,
+            training_loss=0.6940358212922347,
+            metrics={
+               'train_runtime': 173852.2333,
+               'train_samples_per_second': 0.092,
+               'train_steps_per_second': 0.012,
+               'train_loss': 0.6940358212922347,
+               'epoch': 3.0})
+```