End of training

README.md

@@ -1,9 +1,12 @@
 ---
 license: apache-2.0
-library_name: peft
+base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
 tags:
 - generated_from_trainer
-base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+metrics:
+- accuracy
+- precision
+- recall
 model-index:
 - name: tiny-llama
   results: []
@@ -15,6 +18,22 @@ should probably proofread and complete it, then remove this comment. -->
 # tiny-llama
 
 This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.2265
+- Accuracy: 0.8327
+- Precision: 0.8301
+- Recall: 0.8327
+- Precision Macro: 0.7955
+- Recall Macro: 0.7536
+- Macro Fpr: 0.0148
+- Weighted Fpr: 0.0141
+- Weighted Specificity: 0.9765
+- Macro Specificity: 0.9873
+- Weighted Sensitivity: 0.8327
+- Macro Sensitivity: 0.7536
+- F1 Micro: 0.8327
+- F1 Macro: 0.7609
+- F1 Weighted: 0.8291
 
 ## Model description
 
@@ -30,44 +49,39 @@ More information needed
 
 ## Training procedure
 
-
-The following `bitsandbytes` quantization config was used during training:
-- quant_method: bitsandbytes
-- _load_in_8bit: False
-- _load_in_4bit: True
-- llm_int8_threshold: 6.0
-- llm_int8_skip_modules: None
-- llm_int8_enable_fp32_cpu_offload: False
-- llm_int8_has_fp16_weight: False
-- bnb_4bit_quant_type: nf4
-- bnb_4bit_use_double_quant: True
-- bnb_4bit_compute_dtype: bfloat16
-- bnb_4bit_quant_storage: uint8
-- load_in_4bit: True
-- load_in_8bit: False
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
-- train_batch_size: 8
-- eval_batch_size: 8
+- train_batch_size: 2
+- eval_batch_size: 2
 - seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 8
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- training_steps: 1
+- num_epochs: 10
 - mixed_precision_training: Native AMP
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Precision Macro | Recall Macro | Macro Fpr | Weighted Fpr | Weighted Specificity | Macro Specificity | Weighted Sensitivity | Macro Sensitivity | F1 Micro | F1 Macro | F1 Weighted |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
-| No log        | 0.0   | 1    | 4.1563          | 0.0225   | 0.0757    | 0.0225 | 0.0327          | 0.0617       | 0.4479    | 0.7566       | 0.9655               | 0.9325            | 0.0225               | 0.0617            | 0.0225   | 0.0149   | 0.0211      |
+| 1.0444        | 1.0   | 642  | 0.5968          | 0.8056   | 0.8050    | 0.8056 | 0.7122          | 0.6995       | 0.0175    | 0.0169       | 0.9730               | 0.9852            | 0.8056               | 0.6995            | 0.8056   | 0.6986   | 0.8014      |
+| 0.4788        | 2.0   | 1284 | 0.6966          | 0.8195   | 0.8222    | 0.8195 | 0.8092          | 0.7825       | 0.0161    | 0.0155       | 0.9755               | 0.9863            | 0.8195               | 0.7825            | 0.8195   | 0.7849   | 0.8172      |
+| 0.3354        | 3.0   | 1926 | 0.8046          | 0.8327   | 0.8276    | 0.8327 | 0.8058          | 0.7582       | 0.0148    | 0.0141       | 0.9758               | 0.9872            | 0.8327               | 0.7582            | 0.8327   | 0.7742   | 0.8282      |
+| 0.0571        | 4.0   | 2569 | 1.1143          | 0.8265   | 0.8312    | 0.8265 | 0.7904          | 0.7763       | 0.0152    | 0.0148       | 0.9772               | 0.9869            | 0.8265               | 0.7763            | 0.8265   | 0.7690   | 0.8262      |
+| 0.0187        | 5.0   | 3211 | 1.1104          | 0.8319   | 0.8316    | 0.8319 | 0.7745          | 0.7724       | 0.0149    | 0.0142       | 0.9770               | 0.9873            | 0.8319               | 0.7724            | 0.8319   | 0.7638   | 0.8303      |
+| 0.0071        | 6.0   | 3853 | 1.1445          | 0.8242   | 0.8210    | 0.8242 | 0.7684          | 0.7384       | 0.0157    | 0.0150       | 0.9755               | 0.9866            | 0.8242               | 0.7384            | 0.8242   | 0.7451   | 0.8209      |
+| 0.0002        | 7.0   | 4495 | 1.2032          | 0.8327   | 0.8302    | 0.8327 | 0.7985          | 0.7529       | 0.0148    | 0.0141       | 0.9765               | 0.9873            | 0.8327               | 0.7529            | 0.8327   | 0.7617   | 0.8293      |
+| 0.0028        | 8.0   | 5138 | 1.1918          | 0.8257   | 0.8226    | 0.8257 | 0.7738          | 0.7493       | 0.0155    | 0.0149       | 0.9756               | 0.9868            | 0.8257               | 0.7493            | 0.8257   | 0.7552   | 0.8229      |
+| 0.0           | 9.0   | 5780 | 1.2181          | 0.8311   | 0.8286    | 0.8311 | 0.7935          | 0.7522       | 0.0150    | 0.0143       | 0.9764               | 0.9872            | 0.8311               | 0.7522            | 0.8311   | 0.7592   | 0.8276      |
+| 0.0018        | 10.0  | 6420 | 1.2265          | 0.8327   | 0.8301    | 0.8327 | 0.7955          | 0.7536       | 0.0148    | 0.0141       | 0.9765               | 0.9873            | 0.8327               | 0.7536            | 0.8327   | 0.7609   | 0.8291      |
 
 
 ### Framework versions
 
-- PEFT 0.4.0
-- Transformers 4.39.3
-- Pytorch 2.1.2
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
 - Datasets 2.18.0
-- Tokenizers 0.15.2
+- Tokenizers 0.15.1

adapter_model.safetensors

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runs/Apr17_08-26-15_530f60bd592e/events.out.tfevents.1713342376.530f60bd592e.249.0

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