LEESM
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llama-3-Korean-Bllossom-8B-trexlab-oki10p

@@ -1,199 +1,85 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-## Model Details
-### Model Description
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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-3
+- LoRA
+- MLP-KTLim/llama-3-Korean-Bllossom-8B
 ---
+# MLP-KTLim/llama-3-Korean-Bllossom-8B model fine tuning (TREX-Lab at Seoul Cyber University)
 <!-- Provide a quick summary of what the model is/does. -->
+## Summary
+  - Base Model : MLP-KTLim/llama-3-Korean-Bllossom-8B
+  - Dataset : heegyu/open-korean-instructions (10%)
+  - Tuning Method
+    - PEFT(Parameter Efficient Fine-Tuning)
+    - LoRA(Low-Rank Adaptation of Large Language Models)
+  - Related Articles : https://arxiv.org/abs/2106.09685, https://arxiv.org/pdf/2403.10882
+  - Fine-tuning the Base Model with a random 10% 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:** [TREX-Lab at Seoul Cyber University]
+- **Language(s) (NLP):** [Korean]
+- **Finetuned from model :** [MLP-KTLim/llama-3-Korean-Bllossom-8B]
+## 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 21h 45m 55s
+```
+```
+TrainOutput(global_step=816, training_loss=1.718194248045192,
+            metrics={'train_runtime': 78354.6002,
+                     'train_samples_per_second': 0.083,
+                     'train_steps_per_second': 0.01,
+                     'train_loss': 1.718194248045192,
+                     'epoch': 2.99})
+```