exps

experiments/lm_head/mrpc/args.json

@@ -0,0 +1,42 @@
+{
+    "task_name": "mrpc",
+    "train_file": null,
+    "validation_file": null,
+    "max_length": 256,
+    "pad_to_max_length": false,
+    "model_name_or_path": "roberta-base",
+    "use_slow_tokenizer": false,
+    "per_device_train_batch_size": 32,
+    "per_device_eval_batch_size": 1,
+    "learning_rate": 5e-05,
+    "weight_decay": 0.0,
+    "num_train_epochs": 30,
+    "max_train_steps": null,
+    "gradient_accumulation_steps": 1,
+    "lr_scheduler_type": "linear",
+    "num_warmup_steps": 0,
+    "output_dir": "./outputs",
+    "peft_method": null,
+    "seed": 42,
+    "push_to_hub": false,
+    "hub_model_id": null,
+    "hub_token": null,
+    "checkpointing_steps": "1000",
+    "resume_from_checkpoint": null,
+    "with_tracking": false,
+    "report_to": "all",
+    "ignore_mismatched_sizes": true,
+    "save": false,
+    "load_step": 999,
+    "lora_r": 8,
+    "lora_alpha": 16,
+    "lora_dropout": 0.1,
+    "laplace_hessian": "kron",
+    "laplace_sub": "all",
+    "laplace_prior": "homo",
+    "laplace_optim_step": 1000,
+    "testing_set": "val",
+    "laplace_predict": "mc_corr",
+    "lm_head": true,
+    "cache_dir": "/content/cache/huggingface/metrics/"
+}

experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/args.json

@@ -0,0 +1,35 @@
+{
+    "task_name": "mrpc",
+    "train_file": null,
+    "validation_file": null,
+    "max_length": 512,
+    "pad_to_max_length": false,
+    "model_name_or_path": "roberta-base",
+    "use_slow_tokenizer": false,
+    "per_device_train_batch_size": 32,
+    "per_device_eval_batch_size": 1,
+    "learning_rate": 5e-05,
+    "max_grad_norm": 0.5,
+    "weight_decay": 0.0,
+    "num_train_epochs": 30,
+    "max_train_steps": null,
+    "gradient_accumulation_steps": 1,
+    "lr_scheduler_type": "linear",
+    "num_warmup_steps": 0,
+    "output_dir": "./outputs/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42",
+    "seed": 42,
+    "push_to_hub": false,
+    "hub_model_id": null,
+    "hub_token": null,
+    "checkpointing_steps": null,
+    "resume_from_checkpoint": null,
+    "with_tracking": false,
+    "report_to": "all",
+    "ignore_mismatched_sizes": true,
+    "save_train_results": false,
+    "lora_r": 8,
+    "lora_alpha": 16,
+    "lora_dropout": 0.1,
+    "testing_set": "val",
+    "lm_head": true
+}

experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/logfile.log

@@ -0,0 +1,160 @@
+05/12/2024 10:47:22 - INFO - __main__ -  Number of labels detected = 2
+05/12/2024 10:47:27 - INFO - __main__ - None
+05/12/2024 10:47:28 - INFO - __main__ - Sample 2619 of the training set: {'input_ids': [0, 133, 7069, 58, 551, 62, 19, 3659, 24387, 49, 403, 136, 1918, 1001, 6182, 2156, 2600, 2357, 6052, 9, 2339, 24387, 1857, 136, 123, 479, 2, 2, 10653, 32326, 1033, 58, 551, 62, 19, 3659, 24387, 49, 403, 136, 1918, 1001, 6182, 2156, 2600, 10, 2357, 12, 8596, 17984, 1601, 7, 5, 461, 479, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'labels': 1}.
+05/12/2024 10:47:28 - INFO - __main__ - Sample 456 of the training set: {'input_ids': [0, 16764, 8224, 503, 447, 13, 5, 3467, 12, 6996, 168, 32, 10, 7690, 1002, 13, 7369, 8, 8556, 16, 878, 239, 789, 9, 220, 395, 128, 29, 1939, 729, 11, 997, 12, 25566, 3576, 13212, 2636, 479, 2, 2, 35779, 11, 3576, 13212, 2636, 128, 29, 3467, 12, 6996, 168, 32, 10, 7690, 1002, 13, 7369, 2156, 8, 8556, 16, 878, 239, 789, 9, 395, 128, 29, 1939, 729, 11, 5, 997, 12, 6472, 4628, 976, 479, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'labels': 1}.
+05/12/2024 10:47:28 - INFO - __main__ - Sample 102 of the training set: {'input_ids': [0, 35167, 359, 11253, 128, 29, 1764, 388, 1965, 5060, 1981, 204, 4, 1749, 332, 7, 361, 6361, 4, 1096, 2156, 150, 4417, 5946, 5060, 1064, 231, 4, 245, 332, 7, 112, 6, 27639, 4, 1096, 479, 2, 2, 133, 5787, 359, 11253, 128, 29, 1764, 4648, 21, 62, 112, 4, 2545, 332, 2156, 50, 321, 4, 1366, 135, 2156, 7, 361, 4718, 4, 4671, 479, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'labels': 0}.
+05/12/2024 10:47:28 - INFO - __main__ -  Max training steps before recalculation = None
+05/12/2024 10:47:28 - INFO - __main__ -  num_update_steps_per_epoch initial = 115
+05/12/2024 10:47:28 - INFO - __main__ -  num training epochs initial = 30
+05/12/2024 10:47:28 - INFO - __main__ - Adjusted num_train_epochs based on max_train_steps: 30
+05/12/2024 10:47:28 - INFO - __main__ - PeftModelForSequenceClassification(
+  (base_model): LoraModel(
+    (model): RobertaForSequenceClassification(
+      (roberta): RobertaModel(
+        (embeddings): RobertaEmbeddings(
+          (word_embeddings): Embedding(50265, 768, padding_idx=1)
+          (position_embeddings): Embedding(514, 768, padding_idx=1)
+          (token_type_embeddings): Embedding(1, 768)
+          (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (encoder): RobertaEncoder(
+          (layer): ModuleList(
+            (0-11): 12 x RobertaLayer(
+              (attention): RobertaAttention(
+                (self): RobertaSelfAttention(
+                  (query): lora.Linear(
+                    (base_layer): Linear(in_features=768, out_features=768, bias=True)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.1, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=768, out_features=8, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=8, out_features=768, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (key): Linear(in_features=768, out_features=768, bias=True)
+                  (value): lora.Linear(
+                    (base_layer): Linear(in_features=768, out_features=768, bias=True)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.1, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=768, out_features=8, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=8, out_features=768, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (dropout): Dropout(p=0.1, inplace=False)
+                )
+                (output): RobertaSelfOutput(
+                  (dense): Linear(in_features=768, out_features=768, bias=True)
+                  (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+                  (dropout): Dropout(p=0.1, inplace=False)
+                )
+              )
+              (intermediate): RobertaIntermediate(
+                (dense): Linear(in_features=768, out_features=3072, bias=True)
+                (intermediate_act_fn): GELUActivation()
+              )
+              (output): RobertaOutput(
+                (dense): Linear(in_features=3072, out_features=768, bias=True)
+                (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+            )
+          )
+        )
+      )
+      (classifier): ModulesToSaveWrapper(
+        (original_module): RobertaClassificationHead(
+          (dense): lora.Linear(
+            (base_layer): Linear(in_features=768, out_features=768, bias=True)
+            (lora_dropout): ModuleDict(
+              (default): Dropout(p=0.1, inplace=False)
+            )
+            (lora_A): ModuleDict(
+              (default): Linear(in_features=768, out_features=8, bias=False)
+            )
+            (lora_B): ModuleDict(
+              (default): Linear(in_features=8, out_features=768, bias=False)
+            )
+            (lora_embedding_A): ParameterDict()
+            (lora_embedding_B): ParameterDict()
+          )
+          (dropout): Dropout(p=0.1, inplace=False)
+          (out_proj): lora.Linear(
+            (base_layer): Linear(in_features=768, out_features=2, bias=True)
+            (lora_dropout): ModuleDict(
+              (default): Dropout(p=0.1, inplace=False)
+            )
+            (lora_A): ModuleDict(
+              (default): Linear(in_features=768, out_features=8, bias=False)
+            )
+            (lora_B): ModuleDict(
+              (default): Linear(in_features=8, out_features=2, bias=False)
+            )
+            (lora_embedding_A): ParameterDict()
+            (lora_embedding_B): ParameterDict()
+          )
+        )
+        (modules_to_save): ModuleDict(
+          (default): RobertaClassificationHead(
+            (dense): lora.Linear(
+              (base_layer): Linear(in_features=768, out_features=768, bias=True)
+              (lora_dropout): ModuleDict(
+                (default): Dropout(p=0.1, inplace=False)
+              )
+              (lora_A): ModuleDict(
+                (default): Linear(in_features=768, out_features=8, bias=False)
+              )
+              (lora_B): ModuleDict(
+                (default): Linear(in_features=8, out_features=768, bias=False)
+              )
+              (lora_embedding_A): ParameterDict()
+              (lora_embedding_B): ParameterDict()
+            )
+            (dropout): Dropout(p=0.1, inplace=False)
+            (out_proj): lora.Linear(
+              (base_layer): Linear(in_features=768, out_features=2, bias=True)
+              (lora_dropout): ModuleDict(
+                (default): Dropout(p=0.1, inplace=False)
+              )
+              (lora_A): ModuleDict(
+                (default): Linear(in_features=768, out_features=8, bias=False)
+              )
+              (lora_B): ModuleDict(
+                (default): Linear(in_features=8, out_features=2, bias=False)
+              )
+              (lora_embedding_A): ParameterDict()
+              (lora_embedding_B): ParameterDict()
+            )
+          )
+        )
+      )
+    )
+  )
+)
+05/12/2024 10:47:29 - INFO - __main__ -  num_update_steps_per_epoch before recalculation = 115
+05/12/2024 10:47:29 - INFO - __main__ -  num_update_steps_per_epoch after recalculation = 115
+05/12/2024 10:47:29 - INFO - __main__ -  num training epochs before recalculation = 30
+05/12/2024 10:47:30 - INFO - __main__ - ***** Running training *****
+05/12/2024 10:47:30 - INFO - __main__ -   Num examples = 3668
+05/12/2024 10:47:30 - INFO - __main__ -   Num Epochs = 30
+05/12/2024 10:47:30 - INFO - __main__ -   Instantaneous batch size per device = 32
+05/12/2024 10:47:30 - INFO - __main__ -   Total train batch size (w. parallel, distributed & accumulation) = 32
+05/12/2024 10:47:30 - INFO - __main__ -   Gradient Accumulation steps = 1
+05/12/2024 10:47:30 - INFO - __main__ -   Total optimization steps = 3450
+05/12/2024 10:47:37 - INFO - __main__ - epoch 0: {'accuracy': 0.3161764705882353, 'f1': 0.0}
+05/12/2024 10:51:01 - INFO - __main__ - epoch 5: {'accuracy': 0.8357843137254902, 'f1': 0.8838821490467937}
+05/12/2024 10:54:30 - INFO - __main__ - epoch 11: {'accuracy': 0.8357843137254902, 'f1': 0.8766114180478821}
+05/12/2024 10:58:00 - INFO - __main__ - epoch 17: {'accuracy': 0.8578431372549019, 'f1': 0.8999999999999999}
+05/12/2024 11:01:29 - INFO - __main__ - epoch 23: {'accuracy': 0.8602941176470589, 'f1': 0.8991150442477877}
+05/12/2024 11:04:59 - INFO - __main__ - epoch 29: {'accuracy': 0.8602941176470589, 'f1': 0.8987566607460036}

experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_0/README.md

@@ -0,0 +1,202 @@
+---
+library_name: peft
+base_model: roberta-base
+---
+
+# 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]
+- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
+### Framework versions
+
+- PEFT 0.10.0

experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_0/adapter_config.json

@@ -0,0 +1,31 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "roberta-base",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "classifier.out_proj",
+    "classifier.dense",
+    "query",
+    "value"
+  ],
+  "task_type": "SEQ_CLS",
+  "use_dora": false,
+  "use_rslora": false
+}

experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_0/adapter_model.safetensors

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+oid sha256:3f9003ec3ef48d2c0382dbe843e1ab9ccacb634b36849380ae5891056ec59bbc
+size 3778440

experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_0/all_results.json

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+{"eval_accuracy": 0.3161764705882353, "eval_f1": 0.0}

experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_0/all_results_la_kron_all_homo_mc_corr_1000.json

@@ -0,0 +1 @@
+{"eval_accuracy": 0.3161764705882353, "eval_f1": 0.0}

experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_0/eval_res.json

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experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_0/eval_res_la_kron_all_homo_mc_corr_1000.json

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experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_0/gpu_stats.json

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experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_0/special_tokens_map.json

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+{
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experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_0/tokenizer_config.json

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experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_1379/README.md

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+---
+library_name: peft
+base_model: roberta-base
+---
+
+# 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]
+- **Funded by [optional]:** [More Information Needed]
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+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
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+- **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. -->
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+
+### 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 Dataset 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 Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+[More Information Needed]
+
+#### Metrics
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+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+
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+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
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+## 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).
+
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+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
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+
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+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+[More Information Needed]
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+## Model Card Authors [optional]
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+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.10.0

experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_1379/adapter_config.json

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+  "loftq_config": {},
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+  "megatron_config": null,
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+  "peft_type": "LORA",
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+  "task_type": "SEQ_CLS",
+  "use_dora": false,
+  "use_rslora": false
+}

experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_1379/adapter_model.safetensors

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experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_1379/eval_res_la_kron_all_homo_mc_corr_1000.json

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experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_1379/gpu_stats.json

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experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_1379/special_tokens_map.json

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experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_1379/tokenizer_config.json

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experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_2069/README.md

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+---
+library_name: peft
+base_model: roberta-base
+---
+
+# 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]
+- **Funded by [optional]:** [More Information Needed]
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+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
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+- **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
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+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
+### 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 Dataset 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. -->
+
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+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
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+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+
+<!-- Relevant interpretability work for the model goes here -->
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+
+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).
+
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+
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+
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+
+### Compute Infrastructure
+
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+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.10.0

experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_2069/adapter_config.json

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+}

experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_2069/adapter_model.safetensors

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experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_2069/all_results.json

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experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_2069/eval_res.json

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experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_2069/eval_res_la_kron_all_homo_mc_corr_1000.json

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experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_2069/gpu_stats.json

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experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_2069/special_tokens_map.json

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experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_2069/tokenizer_config.json

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experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_2759/README.md

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+---
+library_name: peft
+base_model: roberta-base
+---
+
+# 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]
+- **Funded by [optional]:** [More Information Needed]
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+
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+
+<!-- Provide the basic links for the model. -->
+
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+
+## 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
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+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
+### 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 Dataset 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. -->
+
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+
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+
+<!-- This should link to a Dataset Card if possible. -->
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+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+
+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).
+
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+
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+
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+
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+
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+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.10.0

experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_2759/adapter_config.json

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experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_2759/eval_res_la_kron_all_homo_mc_corr_1000.json

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experiments/lm_head/mrpc/roberta-base_lora_lmhead_8_16_0.1_5e-05_42/step_2759/gpu_stats.json

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