Training in progress, step 1100, checkpoint

checkpoint-1100/README.md

@@ -0,0 +1,219 @@
+---
+library_name: peft
+base_model: vietcuna-3b-v2/kalapa-vietcuna-3b/
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Data Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+
+
+## Training procedure
+
+
+The following `bitsandbytes` quantization config was used during training:
+- quant_method: bitsandbytes
+- load_in_8bit: True
+- load_in_4bit: False
+- 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: fp4
+- bnb_4bit_use_double_quant: False
+- bnb_4bit_compute_dtype: float32
+
+### Framework versions
+
+
+- PEFT 0.6.0.dev0

checkpoint-1100/adapter_config.json

@@ -0,0 +1,22 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "/kaggle/input/vietcuna-for-binary-classification/kalapa-vietcuna-3b",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "lora_alpha": 32,
+  "lora_dropout": 0.1,
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "query_key_value"
+  ],
+  "task_type": "SEQ_CLS"
+}

checkpoint-1100/adapter_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d07a2f5547423a5dfed95a8f666964cb126bc08bffc7bf1c1929d04252b6c3b4
+size 9873829

checkpoint-1100/optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ed55e82c1d5a4864a89bd719684caa64b6478c6f79c1977c1aa9996da08d2b7e
+size 42788

checkpoint-1100/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cd43efc4c024007f886dd98331e4896a7965aee9ba9f2aa57c9dee7fc369fdf3
+size 14244

checkpoint-1100/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c81396216906acec642d0d804e419c07617433835fd53ae8fd0da1f1563d75e9
+size 1064

checkpoint-1100/special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "pad_token": "<pad>",
+  "sep_token": "[SEP]",
+  "unk_token": "<unk>"
+}

checkpoint-1100/tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:17a208233d2ee8d8c83b23bc214df737c44806a1919f444e89b31e586cd956ba
+size 14500471

checkpoint-1100/tokenizer_config.json

@@ -0,0 +1,45 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sep_token": "[SEP]",
+  "tokenizer_class": "BloomTokenizer",
+  "unk_token": "<unk>"
+}

checkpoint-1100/trainer_state.json

@@ -0,0 +1,355 @@
+{
+  "best_metric": 0.6392325758934021,
+  "best_model_checkpoint": "output/checkpoint-700",
+  "epoch": 0.8823824325679335,
+  "eval_steps": 50,
+  "global_step": 1100,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.0,
+      "learning_rate": 1.3333333333333332e-06,
+      "loss": 0.785,
+      "step": 1
+    },
+    {
+      "epoch": 0.04,
+      "learning_rate": 5.9999999999999995e-05,
+      "loss": 0.6547,
+      "step": 50
+    },
+    {
+      "epoch": 0.04,
+      "eval_accuracy": 0.6626865671641791,
+      "eval_loss": 0.8300915956497192,
+      "eval_runtime": 61.5689,
+      "eval_samples_per_second": 5.441,
+      "eval_steps_per_second": 1.364,
+      "step": 50
+    },
+    {
+      "epoch": 0.08,
+      "learning_rate": 0.00012666666666666666,
+      "loss": 0.6654,
+      "step": 100
+    },
+    {
+      "epoch": 0.08,
+      "eval_accuracy": 0.6895522388059702,
+      "eval_loss": 0.6663276553153992,
+      "eval_runtime": 56.4633,
+      "eval_samples_per_second": 5.933,
+      "eval_steps_per_second": 1.488,
+      "step": 100
+    },
+    {
+      "epoch": 0.12,
+      "learning_rate": 0.00019333333333333333,
+      "loss": 0.6491,
+      "step": 150
+    },
+    {
+      "epoch": 0.12,
+      "eval_accuracy": 0.6537313432835821,
+      "eval_loss": 0.9172552824020386,
+      "eval_runtime": 56.4705,
+      "eval_samples_per_second": 5.932,
+      "eval_steps_per_second": 1.488,
+      "step": 150
+    },
+    {
+      "epoch": 0.16,
+      "learning_rate": 0.00026,
+      "loss": 0.641,
+      "step": 200
+    },
+    {
+      "epoch": 0.16,
+      "eval_accuracy": 0.6865671641791045,
+      "eval_loss": 0.6528732180595398,
+      "eval_runtime": 56.4485,
+      "eval_samples_per_second": 5.935,
+      "eval_steps_per_second": 1.488,
+      "step": 200
+    },
+    {
+      "epoch": 0.2,
+      "learning_rate": 0.0002941234084231146,
+      "loss": 0.6521,
+      "step": 250
+    },
+    {
+      "epoch": 0.2,
+      "eval_accuracy": 0.6835820895522388,
+      "eval_loss": 0.8197052478790283,
+      "eval_runtime": 56.4577,
+      "eval_samples_per_second": 5.934,
+      "eval_steps_per_second": 1.488,
+      "step": 250
+    },
+    {
+      "epoch": 0.24,
+      "learning_rate": 0.00027943192948090106,
+      "loss": 0.638,
+      "step": 300
+    },
+    {
+      "epoch": 0.24,
+      "eval_accuracy": 0.6835820895522388,
+      "eval_loss": 0.6961421370506287,
+      "eval_runtime": 56.4506,
+      "eval_samples_per_second": 5.934,
+      "eval_steps_per_second": 1.488,
+      "step": 300
+    },
+    {
+      "epoch": 0.28,
+      "learning_rate": 0.00026474045053868754,
+      "loss": 0.6857,
+      "step": 350
+    },
+    {
+      "epoch": 0.28,
+      "eval_accuracy": 0.6835820895522388,
+      "eval_loss": 0.7876304388046265,
+      "eval_runtime": 56.4651,
+      "eval_samples_per_second": 5.933,
+      "eval_steps_per_second": 1.488,
+      "step": 350
+    },
+    {
+      "epoch": 0.32,
+      "learning_rate": 0.000250048971596474,
+      "loss": 0.6469,
+      "step": 400
+    },
+    {
+      "epoch": 0.32,
+      "eval_accuracy": 0.6626865671641791,
+      "eval_loss": 0.7487305402755737,
+      "eval_runtime": 56.4489,
+      "eval_samples_per_second": 5.935,
+      "eval_steps_per_second": 1.488,
+      "step": 400
+    },
+    {
+      "epoch": 0.36,
+      "learning_rate": 0.00023535749265426052,
+      "loss": 0.6813,
+      "step": 450
+    },
+    {
+      "epoch": 0.36,
+      "eval_accuracy": 0.6626865671641791,
+      "eval_loss": 0.8447557091712952,
+      "eval_runtime": 56.4428,
+      "eval_samples_per_second": 5.935,
+      "eval_steps_per_second": 1.488,
+      "step": 450
+    },
+    {
+      "epoch": 0.4,
+      "learning_rate": 0.000220666013712047,
+      "loss": 0.645,
+      "step": 500
+    },
+    {
+      "epoch": 0.4,
+      "eval_accuracy": 0.6835820895522388,
+      "eval_loss": 0.6624641418457031,
+      "eval_runtime": 56.4531,
+      "eval_samples_per_second": 5.934,
+      "eval_steps_per_second": 1.488,
+      "step": 500
+    },
+    {
+      "epoch": 0.44,
+      "learning_rate": 0.0002059745347698335,
+      "loss": 0.7044,
+      "step": 550
+    },
+    {
+      "epoch": 0.44,
+      "eval_accuracy": 0.7014925373134329,
+      "eval_loss": 0.63779217004776,
+      "eval_runtime": 56.4382,
+      "eval_samples_per_second": 5.936,
+      "eval_steps_per_second": 1.488,
+      "step": 550
+    },
+    {
+      "epoch": 0.48,
+      "learning_rate": 0.00019128305582761997,
+      "loss": 0.6399,
+      "step": 600
+    },
+    {
+      "epoch": 0.48,
+      "eval_accuracy": 0.6716417910447762,
+      "eval_loss": 0.7784742712974548,
+      "eval_runtime": 56.4356,
+      "eval_samples_per_second": 5.936,
+      "eval_steps_per_second": 1.488,
+      "step": 600
+    },
+    {
+      "epoch": 0.52,
+      "learning_rate": 0.00017659157688540645,
+      "loss": 0.6039,
+      "step": 650
+    },
+    {
+      "epoch": 0.52,
+      "eval_accuracy": 0.6985074626865672,
+      "eval_loss": 0.665508508682251,
+      "eval_runtime": 56.4424,
+      "eval_samples_per_second": 5.935,
+      "eval_steps_per_second": 1.488,
+      "step": 650
+    },
+    {
+      "epoch": 0.56,
+      "learning_rate": 0.00016190009794319292,
+      "loss": 0.6321,
+      "step": 700
+    },
+    {
+      "epoch": 0.56,
+      "eval_accuracy": 0.6716417910447762,
+      "eval_loss": 0.6392325758934021,
+      "eval_runtime": 56.437,
+      "eval_samples_per_second": 5.936,
+      "eval_steps_per_second": 1.488,
+      "step": 700
+    },
+    {
+      "epoch": 0.6,
+      "learning_rate": 0.00014720861900097942,
+      "loss": 0.6448,
+      "step": 750
+    },
+    {
+      "epoch": 0.6,
+      "eval_accuracy": 0.6686567164179105,
+      "eval_loss": 0.7872520089149475,
+      "eval_runtime": 56.4423,
+      "eval_samples_per_second": 5.935,
+      "eval_steps_per_second": 1.488,
+      "step": 750
+    },
+    {
+      "epoch": 0.64,
+      "learning_rate": 0.0001325171400587659,
+      "loss": 0.6033,
+      "step": 800
+    },
+    {
+      "epoch": 0.64,
+      "eval_accuracy": 0.6895522388059702,
+      "eval_loss": 0.723906934261322,
+      "eval_runtime": 56.4576,
+      "eval_samples_per_second": 5.934,
+      "eval_steps_per_second": 1.488,
+      "step": 800
+    },
+    {
+      "epoch": 0.68,
+      "learning_rate": 0.00011782566111655239,
+      "loss": 0.6174,
+      "step": 850
+    },
+    {
+      "epoch": 0.68,
+      "eval_accuracy": 0.6925373134328359,
+      "eval_loss": 0.6893543601036072,
+      "eval_runtime": 56.4403,
+      "eval_samples_per_second": 5.935,
+      "eval_steps_per_second": 1.488,
+      "step": 850
+    },
+    {
+      "epoch": 0.72,
+      "learning_rate": 0.00010313418217433888,
+      "loss": 0.5859,
+      "step": 900
+    },
+    {
+      "epoch": 0.72,
+      "eval_accuracy": 0.6746268656716418,
+      "eval_loss": 0.6730663776397705,
+      "eval_runtime": 56.4424,
+      "eval_samples_per_second": 5.935,
+      "eval_steps_per_second": 1.488,
+      "step": 900
+    },
+    {
+      "epoch": 0.76,
+      "learning_rate": 8.844270323212536e-05,
+      "loss": 0.6,
+      "step": 950
+    },
+    {
+      "epoch": 0.76,
+      "eval_accuracy": 0.6686567164179105,
+      "eval_loss": 0.6526519060134888,
+      "eval_runtime": 56.4284,
+      "eval_samples_per_second": 5.937,
+      "eval_steps_per_second": 1.489,
+      "step": 950
+    },
+    {
+      "epoch": 0.8,
+      "learning_rate": 7.375122428991184e-05,
+      "loss": 0.6127,
+      "step": 1000
+    },
+    {
+      "epoch": 0.8,
+      "eval_accuracy": 0.7014925373134329,
+      "eval_loss": 0.6604557037353516,
+      "eval_runtime": 56.4364,
+      "eval_samples_per_second": 5.936,
+      "eval_steps_per_second": 1.488,
+      "step": 1000
+    },
+    {
+      "epoch": 0.84,
+      "learning_rate": 5.9059745347698334e-05,
+      "loss": 0.6186,
+      "step": 1050
+    },
+    {
+      "epoch": 0.84,
+      "eval_accuracy": 0.6865671641791045,
+      "eval_loss": 0.6518017053604126,
+      "eval_runtime": 56.4388,
+      "eval_samples_per_second": 5.936,
+      "eval_steps_per_second": 1.488,
+      "step": 1050
+    },
+    {
+      "epoch": 0.88,
+      "learning_rate": 4.4368266405484816e-05,
+      "loss": 0.5929,
+      "step": 1100
+    },
+    {
+      "epoch": 0.88,
+      "eval_accuracy": 0.6985074626865672,
+      "eval_loss": 0.6976536512374878,
+      "eval_runtime": 56.4535,
+      "eval_samples_per_second": 5.934,
+      "eval_steps_per_second": 1.488,
+      "step": 1100
+    }
+  ],
+  "logging_steps": 50,
+  "max_steps": 1246,
+  "num_train_epochs": 1,
+  "save_steps": 100,
+  "total_flos": 4.1300552228438016e+17,
+  "trial_name": null,
+  "trial_params": null
+}

checkpoint-1100/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e32e120c6d65576e6fe76fdf63d907877e27469586cc746d8cd6a71d07635ea9
+size 4472