Model save

README.md

@@ -3,23 +3,11 @@ license: other
 base_model: Qwen/Qwen1.5-4B
 tags:
 - generated_from_trainer
-datasets:
-- tyzhu/lmind_nq_train6000_eval6489_v1_qa
 metrics:
 - accuracy
 model-index:
 - name: lmind_nq_train6000_eval6489_v1_qa_Qwen_Qwen1.5-4B_lora2
-  results:
-  - task:
-      name: Causal Language Modeling
-      type: text-generation
-    dataset:
-      name: tyzhu/lmind_nq_train6000_eval6489_v1_qa
-      type: tyzhu/lmind_nq_train6000_eval6489_v1_qa
-    metrics:
-    - name: Accuracy
-      type: accuracy
-      value: 0.5594358974358974
+  results: []
 library_name: peft
 ---
 
@@ -28,10 +16,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # lmind_nq_train6000_eval6489_v1_qa_Qwen_Qwen1.5-4B_lora2
 
-This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on the tyzhu/lmind_nq_train6000_eval6489_v1_qa dataset.
+This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 2.2527
-- Accuracy: 0.5594
+- Loss: 2.4726
+- Accuracy: 0.5579
 
 ## Model description
 
@@ -62,22 +50,32 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: constant
 - lr_scheduler_warmup_ratio: 0.05
-- num_epochs: 10.0
+- num_epochs: 20.0
 
 ### Training results
 
-| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
-|:-------------:|:------:|:----:|:---------------:|:--------:|
-| 1.7657        | 0.9973 | 187  | 1.6215          | 0.5738   |
-| 1.497         | 2.0    | 375  | 1.6180          | 0.5742   |
-| 1.2345        | 2.9973 | 562  | 1.6951          | 0.5713   |
-| 1.0084        | 4.0    | 750  | 1.8059          | 0.5659   |
-| 0.8397        | 4.9973 | 937  | 1.9245          | 0.5647   |
-| 0.7186        | 6.0    | 1125 | 2.0345          | 0.5614   |
-| 0.6421        | 6.9973 | 1312 | 2.1148          | 0.5608   |
-| 0.5968        | 8.0    | 1500 | 2.1779          | 0.5585   |
-| 0.5417        | 8.9973 | 1687 | 2.2654          | 0.5568   |
-| 0.5356        | 9.9733 | 1870 | 2.2527          | 0.5594   |
+| Training Loss | Epoch   | Step | Accuracy | Validation Loss |
+|:-------------:|:-------:|:----:|:--------:|:---------------:|
+| 1.7657        | 0.9973  | 187  | 0.5738   | 1.6215          |
+| 1.497         | 2.0     | 375  | 0.5742   | 1.6180          |
+| 1.2345        | 2.9973  | 562  | 0.5713   | 1.6951          |
+| 1.0084        | 4.0     | 750  | 0.5659   | 1.8059          |
+| 0.8397        | 4.9973  | 937  | 0.5647   | 1.9245          |
+| 0.7186        | 6.0     | 1125 | 0.5614   | 2.0345          |
+| 0.6421        | 6.9973  | 1312 | 0.5608   | 2.1148          |
+| 0.5968        | 8.0     | 1500 | 0.5585   | 2.1779          |
+| 0.5417        | 8.9973  | 1687 | 0.5568   | 2.2654          |
+| 0.5356        | 9.9733  | 1870 | 0.5594   | 2.2527          |
+| 0.5261        | 10.9973 | 2057 | 2.3376   | 0.5585          |
+| 0.5179        | 12.0    | 2245 | 2.3704   | 0.5595          |
+| 0.5116        | 12.9973 | 2432 | 2.3617   | 0.5589          |
+| 0.5056        | 14.0    | 2620 | 2.4022   | 0.5581          |
+| 0.5063        | 14.9973 | 2807 | 2.3861   | 0.5587          |
+| 0.4796        | 16.0    | 2995 | 2.3658   | 0.5585          |
+| 0.4757        | 16.9973 | 3182 | 2.4195   | 0.5577          |
+| 0.4779        | 18.0    | 3370 | 2.4573   | 0.5573          |
+| 0.4782        | 18.9973 | 3557 | 2.4896   | 0.5589          |
+| 0.4784        | 19.9733 | 3740 | 2.4726   | 0.5579          |
 
 
 ### Framework versions