Update README.md

README.md

@@ -1,58 +1,16 @@
----
-license: apache-2.0
-base_model: yanolja/KoSOLAR-10.7B-v0.2
-tags:
-- generated_from_trainer
-model-index:
-- name: lion-32bit_FA2_neftune_5_leaderboard_inst_v1.3_dedup_KoSOLAR-10.7B-v0.2_L1024
-  results: []
----
+Model
+base_model : yanolja/KoSOLAR-10.7B-v0.2
+
+Dataset
+공개 데이터 수집
+Deduplicating Training Data Makes Language Models Better 알고리즘 활용
+Code
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "jingyeom/SOLAR_KO_1.3_deup"
+model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-<!-- 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. -->
-
-# lion-32bit_FA2_neftune_5_leaderboard_inst_v1.3_dedup_KoSOLAR-10.7B-v0.2_L1024
-
-This model is a fine-tuned version of [yanolja/KoSOLAR-10.7B-v0.2](https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.2) 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: 1e-06
-- train_batch_size: 2
-- eval_batch_size: 8
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 6
-- gradient_accumulation_steps: 16
-- total_train_batch_size: 192
-- total_eval_batch_size: 48
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_ratio: 0.03
-- num_epochs: 3
-
-### Training results
-
-
-
-### Framework versions
-
-- Transformers 4.35.2
-- Pytorch 2.0.1
-- Datasets 2.15.0
-- Tokenizers 0.15.0