Model save

README.md

@@ -3,23 +3,11 @@ license: other
 base_model: Qwen/Qwen1.5-4B
 tags:
 - generated_from_trainer
-datasets:
-- tyzhu/lmind_hotpot_train8000_eval7405_v1_qa
 metrics:
 - accuracy
 model-index:
 - name: lmind_hotpot_train8000_eval7405_v1_qa_Qwen_Qwen1.5-4B_lora2
-  results:
-  - task:
-      name: Causal Language Modeling
-      type: text-generation
-    dataset:
-      name: tyzhu/lmind_hotpot_train8000_eval7405_v1_qa
-      type: tyzhu/lmind_hotpot_train8000_eval7405_v1_qa
-    metrics:
-    - name: Accuracy
-      type: accuracy
-      value: 0.49263492063492065
+  results: []
 library_name: peft
 ---
 
@@ -28,10 +16,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # lmind_hotpot_train8000_eval7405_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_hotpot_train8000_eval7405_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: 3.4933
-- Accuracy: 0.4926
+- Loss: 3.9177
+- Accuracy: 0.4908
 
 ## 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 |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 2.2624        | 1.0   | 250  | 2.3220          | 0.5159   |
-| 2.0942        | 2.0   | 500  | 2.3289          | 0.5176   |
-| 1.8479        | 3.0   | 750  | 2.3997          | 0.5148   |
-| 1.6153        | 4.0   | 1000 | 2.5067          | 0.5107   |
-| 1.3618        | 5.0   | 1250 | 2.6641          | 0.5052   |
-| 1.1477        | 6.0   | 1500 | 2.8411          | 0.5016   |
-| 0.9248        | 7.0   | 1750 | 3.0246          | 0.4978   |
-| 0.7705        | 8.0   | 2000 | 3.2090          | 0.4954   |
-| 0.6344        | 9.0   | 2250 | 3.3400          | 0.4935   |
-| 0.5612        | 10.0  | 2500 | 3.4933          | 0.4926   |
+| Training Loss | Epoch | Step | Accuracy | Validation Loss |
+|:-------------:|:-----:|:----:|:--------:|:---------------:|
+| 2.2624        | 1.0   | 250  | 0.5159   | 2.3220          |
+| 2.0942        | 2.0   | 500  | 0.5176   | 2.3289          |
+| 1.8479        | 3.0   | 750  | 0.5148   | 2.3997          |
+| 1.6153        | 4.0   | 1000 | 0.5107   | 2.5067          |
+| 1.3618        | 5.0   | 1250 | 0.5052   | 2.6641          |
+| 1.1477        | 6.0   | 1500 | 0.5016   | 2.8411          |
+| 0.9248        | 7.0   | 1750 | 0.4978   | 3.0246          |
+| 0.7705        | 8.0   | 2000 | 0.4954   | 3.2090          |
+| 0.6344        | 9.0   | 2250 | 0.4935   | 3.3400          |
+| 0.5612        | 10.0  | 2500 | 0.4926   | 3.4933          |
+| 0.4967        | 11.0  | 2750 | 3.5794   | 0.4917          |
+| 0.4696        | 12.0  | 3000 | 3.6326   | 0.4914          |
+| 0.4399        | 13.0  | 3250 | 3.7408   | 0.4920          |
+| 0.4324        | 14.0  | 3500 | 3.7450   | 0.4915          |
+| 0.4105        | 15.0  | 3750 | 3.8301   | 0.4922          |
+| 0.4081        | 16.0  | 4000 | 3.8488   | 0.4921          |
+| 0.3939        | 17.0  | 4250 | 3.8492   | 0.4913          |
+| 0.3924        | 18.0  | 4500 | 3.8751   | 0.4915          |
+| 0.382         | 19.0  | 4750 | 3.9337   | 0.4910          |
+| 0.3832        | 20.0  | 5000 | 3.9177   | 0.4908          |
 
 
 ### Framework versions