End of training

README.md

@@ -65,7 +65,7 @@ lora_model_dir: null
 lora_r: 8
 lora_target_linear: true
 lr_scheduler: cosine
-max_steps: 1
+max_steps: 50
 micro_batch_size: 2
 mlflow_experiment_name: /tmp/a3c5d8b320c8afde_train_data.json
 model_type: AutoModelForCausalLM
@@ -92,7 +92,7 @@ wandb_name: df6bf9d4-a987-43b4-acb3-402a434df76e
 wandb_project: Gradients-On-Demand
 wandb_run: your_name
 wandb_runid: df6bf9d4-a987-43b4-acb3-402a434df76e
-warmup_steps: 1
+warmup_steps: 10
 weight_decay: 0.0
 xformers_attention: null
 
@@ -103,6 +103,8 @@ xformers_attention: null
 # 5161bad0-d6f0-4882-ac96-320ea572c5fc
 
 This model is a fine-tuned version of [elyza/Llama-3-ELYZA-JP-8B](https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.8633
 
 ## Model description
 
@@ -129,14 +131,19 @@ The following hyperparameters were used during training:
 - total_train_batch_size: 8
 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: cosine
-- lr_scheduler_warmup_steps: 2
-- training_steps: 1
+- lr_scheduler_warmup_steps: 10
+- training_steps: 50
 
 ### Training results
 
 | Training Loss | Epoch  | Step | Validation Loss |
 |:-------------:|:------:|:----:|:---------------:|
 | No log        | 0.0000 | 1    | 1.3511          |
+| 1.2625        | 0.0004 | 10   | 1.1627          |
+| 0.8807        | 0.0009 | 20   | 0.9764          |
+| 0.9152        | 0.0013 | 30   | 0.8988          |
+| 0.8584        | 0.0018 | 40   | 0.8682          |
+| 0.8292        | 0.0022 | 50   | 0.8633          |
 
 
 ### Framework versions

adapter_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:02153f57f6ae0f41def4272106b166188c625dae1ed62b50129e514c37714b4e
+oid sha256:f270dc40f364b8b61b38710b054b67fc67d255acd7bca21eaebf9ad299e27c1c
 size 84047370