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_docidx_v3
 metrics:
 - accuracy
 model-index:
 - name: lmind_nq_train6000_eval6489_v1_docidx_v3_Qwen_Qwen1.5-4B_lora2
-  results:
-  - task:
-      name: Causal Language Modeling
-      type: text-generation
-    dataset:
-      name: tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3
-      type: tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3
-    metrics:
-    - name: Accuracy
-      type: accuracy
-      value: 0.44876923076923075
+  results: []
 library_name: peft
 ---
 
@@ -28,10 +16,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # lmind_nq_train6000_eval6489_v1_docidx_v3_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_docidx_v3 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: 4.5794
-- Accuracy: 0.4488
+- Loss: 5.3392
+- Accuracy: 0.4286
 
 ## 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 |
-|:-------------:|:------:|:----:|:---------------:|:--------:|
-| 1.9569        | 0.9985 | 341  | 3.0300          | 0.4736   |
-| 1.8799        | 2.0    | 683  | 3.0993          | 0.468    |
-| 1.7649        | 2.9985 | 1024 | 3.2750          | 0.4650   |
-| 1.6077        | 4.0    | 1366 | 3.4406          | 0.4625   |
-| 1.4321        | 4.9985 | 1707 | 3.6500          | 0.4586   |
-| 1.2382        | 6.0    | 2049 | 3.8598          | 0.4562   |
-| 1.0525        | 6.9985 | 2390 | 4.0638          | 0.4541   |
-| 0.8607        | 8.0    | 2732 | 4.2389          | 0.4515   |
-| 0.7099        | 8.9985 | 3073 | 4.3484          | 0.4516   |
-| 0.5823        | 9.9854 | 3410 | 4.5794          | 0.4488   |
+| Training Loss | Epoch   | Step | Accuracy | Validation Loss |
+|:-------------:|:-------:|:----:|:--------:|:---------------:|
+| 1.9569        | 0.9985  | 341  | 0.4736   | 3.0300          |
+| 1.8799        | 2.0     | 683  | 0.468    | 3.0993          |
+| 1.7649        | 2.9985  | 1024 | 0.4650   | 3.2750          |
+| 1.6077        | 4.0     | 1366 | 0.4625   | 3.4406          |
+| 1.4321        | 4.9985  | 1707 | 0.4586   | 3.6500          |
+| 1.2382        | 6.0     | 2049 | 0.4562   | 3.8598          |
+| 1.0525        | 6.9985  | 2390 | 0.4541   | 4.0638          |
+| 0.8607        | 8.0     | 2732 | 0.4515   | 4.2389          |
+| 0.7099        | 8.9985  | 3073 | 0.4516   | 4.3484          |
+| 0.5823        | 9.9854  | 3410 | 0.4488   | 4.5794          |
+| 0.4641        | 10.9985 | 3751 | 4.7090   | 0.4495          |
+| 0.3755        | 12.0    | 4093 | 4.9454   | 0.4354          |
+| 0.3235        | 12.9985 | 4434 | 5.0624   | 0.4379          |
+| 0.2691        | 14.0    | 4776 | 5.0957   | 0.4345          |
+| 0.2394        | 14.9985 | 5117 | 5.1831   | 0.4368          |
+| 0.2112        | 16.0    | 5459 | 5.3223   | 0.4326          |
+| 0.1994        | 16.9985 | 5800 | 5.3839   | 0.4301          |
+| 0.1834        | 18.0    | 6142 | 5.4236   | 0.4286          |
+| 0.1709        | 18.9985 | 6483 | 5.4840   | 0.4291          |
+| 0.166         | 19.9854 | 6820 | 5.3392   | 0.4286          |
 
 
 ### Framework versions