End of training

README.md

@@ -66,7 +66,7 @@ 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/f272d46c384988d4_train_data.json
 model_type: AutoModelForCausalLM
@@ -93,7 +93,7 @@ wandb_name: e90019fe-de9d-4403-a959-cb282c40bcc3
 wandb_project: Gradients-On-Demand
 wandb_run: your_name
 wandb_runid: e90019fe-de9d-4403-a959-cb282c40bcc3
-warmup_steps: 1
+warmup_steps: 10
 weight_decay: 0.0
 xformers_attention: null
 
@@ -104,6 +104,8 @@ xformers_attention: null
 # c1754119-55e6-46d8-8863-973237a2e37c
 
 This model is a fine-tuned version of [TinyLlama/TinyLlama_v1.1](https://huggingface.co/TinyLlama/TinyLlama_v1.1) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.4664
 
 ## Model description
 
@@ -130,14 +132,19 @@ 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        | 0.0005 | 1    | 2.6221          |
+| 2.5697        | 0.0050 | 10   | 2.5923          |
+| 2.5644        | 0.0099 | 20   | 2.4997          |
+| 2.4852        | 0.0149 | 30   | 2.4749          |
+| 2.3449        | 0.0198 | 40   | 2.4677          |
+| 2.5014        | 0.0248 | 50   | 2.4664          |
 
 
 ### Framework versions

adapter_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2607e8aecaed6f2c3fab87abf4ff1c7ef2622bca2574366758d19249e64e365b
+oid sha256:32de8430e139579667e16cb1ca7ab8c5593476bfed7b1bd6a2fe26c7c92f3f33
 size 25342042