starcoder-1b-kubeflow ckpt

.ipynb_checkpoints/README-checkpoint.md

@@ -0,0 +1,202 @@
+---
+base_model: bigcode/starcoderbase-1b
+library_name: peft
+---
+
+# 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 0.12.1.dev0

README.md

@@ -1,3 +1,202 @@
----
-license: mit
----
+---
+base_model: bigcode/starcoderbase-1b
+library_name: peft
+---
+
+# 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 0.12.1.dev0

adapter_config.json

@@ -0,0 +1,31 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "bigcode/starcoderbase-1b",
+  "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": 32,
+  "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": [
+    "c_proj",
+    "c_fc",
+    "c_attn",
+    "q_attn"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f8ad067c72fbc48be19d899f3c8042114dea086ec96a0bb89bb8896a1d2618c9
+size 22241240

checkpoint-100/README.md

@@ -0,0 +1,202 @@
+---
+base_model: bigcode/starcoderbase-1b
+library_name: peft
+---
+
+# 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 0.12.1.dev0

checkpoint-100/adapter_config.json

@@ -0,0 +1,31 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "bigcode/starcoderbase-1b",
+  "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": 32,
+  "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": [
+    "c_proj",
+    "c_fc",
+    "c_attn",
+    "q_attn"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

checkpoint-100/adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b3003dd24d4a067aa5cfb3f0fd25ed897ca76295a5107433cbc964f7fcb62c3
+size 22241240

checkpoint-100/optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cdb079f1f095f86ab6ccaf883541331c83ba03e63c29175e8a55f0ad2ce24e88
+size 44594810

checkpoint-100/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b87d966c97e4572e34f9c1e44ac612e8552bea603c9bd8806e8d6587f80994fc
+size 14244

checkpoint-100/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:032c582972fc6346d384b9e7b8947150a7a6ec9d0a2ec1d495601a4de3bf0bb7
+size 1064

checkpoint-100/trainer_state.json

@@ -0,0 +1,93 @@
+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 0.2,
+  "eval_steps": 25,
+  "global_step": 100,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.05,
+      "grad_norm": 0.18923527002334595,
+      "learning_rate": 0.0001666666666666667,
+      "loss": 1.1779,
+      "step": 25
+    },
+    {
+      "epoch": 0.05,
+      "eval_loss": 0.7435017824172974,
+      "eval_runtime": 621.1563,
+      "eval_samples_per_second": 4.356,
+      "eval_steps_per_second": 0.546,
+      "step": 25
+    },
+    {
+      "epoch": 0.1,
+      "grad_norm": 0.16780352592468262,
+      "learning_rate": 0.00019148936170212768,
+      "loss": 1.2708,
+      "step": 50
+    },
+    {
+      "epoch": 0.1,
+      "eval_loss": 0.7384106516838074,
+      "eval_runtime": 619.3795,
+      "eval_samples_per_second": 4.369,
+      "eval_steps_per_second": 0.547,
+      "step": 50
+    },
+    {
+      "epoch": 0.15,
+      "grad_norm": 0.1804531365633011,
+      "learning_rate": 0.00018085106382978726,
+      "loss": 1.2848,
+      "step": 75
+    },
+    {
+      "epoch": 0.15,
+      "eval_loss": 0.7341726422309875,
+      "eval_runtime": 619.225,
+      "eval_samples_per_second": 4.37,
+      "eval_steps_per_second": 0.547,
+      "step": 75
+    },
+    {
+      "epoch": 0.2,
+      "grad_norm": 0.17204701900482178,
+      "learning_rate": 0.00017021276595744682,
+      "loss": 1.3443,
+      "step": 100
+    },
+    {
+      "epoch": 0.2,
+      "eval_loss": 0.7313376665115356,
+      "eval_runtime": 619.8363,
+      "eval_samples_per_second": 4.366,
+      "eval_steps_per_second": 0.547,
+      "step": 100
+    }
+  ],
+  "logging_steps": 25,
+  "max_steps": 500,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 9223372036854775807,
+  "save_steps": 100,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": false
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 2.0158630330368e+16,
+  "train_batch_size": 1,
+  "trial_name": null,
+  "trial_params": null
+}

checkpoint-100/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf0bab29e9b3132b540b844be698b8d6cee10ba9f03ea8a1a2c3173e08e976c0
+size 5240

checkpoint-200/README.md

@@ -0,0 +1,202 @@
+---
+base_model: bigcode/starcoderbase-1b
+library_name: peft
+---
+
+# 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 0.12.1.dev0

checkpoint-200/adapter_config.json

@@ -0,0 +1,31 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "bigcode/starcoderbase-1b",
+  "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": 32,
+  "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": [
+    "c_proj",
+    "c_fc",
+    "c_attn",
+    "q_attn"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

checkpoint-200/adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8b5b2b1426588bc72c6c555730957c57f7ceb1253700443b95466edcb2f56a29
+size 22241240

checkpoint-200/optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:56b47228a9a66196b25a42e6e0da290208b140c1ebf737cdd743ddce0954a631
+size 44594810

checkpoint-200/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5a9a52eafab5a471ac7ee0af300f883755d2ea6ce6faec139412c6085bd63a0e
+size 14180

checkpoint-200/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d088660a09b7c158eef1451d55c201f101f9c9d27cb3830c9b89f2415244cf34
+size 1064

checkpoint-200/trainer_state.json

@@ -0,0 +1,153 @@
+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 0.4,
+  "eval_steps": 25,
+  "global_step": 200,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.05,
+      "grad_norm": 0.18923527002334595,
+      "learning_rate": 0.0001666666666666667,
+      "loss": 1.1779,
+      "step": 25
+    },
+    {
+      "epoch": 0.05,
+      "eval_loss": 0.7435017824172974,
+      "eval_runtime": 621.1563,
+      "eval_samples_per_second": 4.356,
+      "eval_steps_per_second": 0.546,
+      "step": 25
+    },
+    {
+      "epoch": 0.1,
+      "grad_norm": 0.16780352592468262,
+      "learning_rate": 0.00019148936170212768,
+      "loss": 1.2708,
+      "step": 50
+    },
+    {
+      "epoch": 0.1,
+      "eval_loss": 0.7384106516838074,
+      "eval_runtime": 619.3795,
+      "eval_samples_per_second": 4.369,
+      "eval_steps_per_second": 0.547,
+      "step": 50
+    },
+    {
+      "epoch": 0.15,
+      "grad_norm": 0.1804531365633011,
+      "learning_rate": 0.00018085106382978726,
+      "loss": 1.2848,
+      "step": 75
+    },
+    {
+      "epoch": 0.15,
+      "eval_loss": 0.7341726422309875,
+      "eval_runtime": 619.225,
+      "eval_samples_per_second": 4.37,
+      "eval_steps_per_second": 0.547,
+      "step": 75
+    },
+    {
+      "epoch": 0.2,
+      "grad_norm": 0.17204701900482178,
+      "learning_rate": 0.00017021276595744682,
+      "loss": 1.3443,
+      "step": 100
+    },
+    {
+      "epoch": 0.2,
+      "eval_loss": 0.7313376665115356,
+      "eval_runtime": 619.8363,
+      "eval_samples_per_second": 4.366,
+      "eval_steps_per_second": 0.547,
+      "step": 100
+    },
+    {
+      "epoch": 0.25,
+      "grad_norm": 0.2668892741203308,
+      "learning_rate": 0.00015957446808510637,
+      "loss": 1.2563,
+      "step": 125
+    },
+    {
+      "epoch": 0.25,
+      "eval_loss": 0.7282304167747498,
+      "eval_runtime": 619.962,
+      "eval_samples_per_second": 4.365,
+      "eval_steps_per_second": 0.547,
+      "step": 125
+    },
+    {
+      "epoch": 0.3,
+      "grad_norm": 0.21089820563793182,
+      "learning_rate": 0.00014893617021276596,
+      "loss": 0.7529,
+      "step": 150
+    },
+    {
+      "epoch": 0.3,
+      "eval_loss": 0.7184526324272156,
+      "eval_runtime": 619.2606,
+      "eval_samples_per_second": 4.37,
+      "eval_steps_per_second": 0.547,
+      "step": 150
+    },
+    {
+      "epoch": 0.35,
+      "grad_norm": 0.158623605966568,
+      "learning_rate": 0.00013829787234042554,
+      "loss": 0.7957,
+      "step": 175
+    },
+    {
+      "epoch": 0.35,
+      "eval_loss": 0.7136204242706299,
+      "eval_runtime": 619.6757,
+      "eval_samples_per_second": 4.367,
+      "eval_steps_per_second": 0.547,
+      "step": 175
+    },
+    {
+      "epoch": 0.4,
+      "grad_norm": 0.16229337453842163,
+      "learning_rate": 0.00012765957446808513,
+      "loss": 0.7322,
+      "step": 200
+    },
+    {
+      "epoch": 0.4,
+      "eval_loss": 0.7111368179321289,
+      "eval_runtime": 620.1668,
+      "eval_samples_per_second": 4.363,
+      "eval_steps_per_second": 0.547,
+      "step": 200
+    }
+  ],
+  "logging_steps": 25,
+  "max_steps": 500,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 9223372036854775807,
+  "save_steps": 100,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": false
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 4.0317260660736e+16,
+  "train_batch_size": 1,
+  "trial_name": null,
+  "trial_params": null
+}

checkpoint-200/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf0bab29e9b3132b540b844be698b8d6cee10ba9f03ea8a1a2c3173e08e976c0
+size 5240

checkpoint-300/README.md

@@ -0,0 +1,202 @@
+---
+base_model: bigcode/starcoderbase-1b
+library_name: peft
+---
+
+# 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 0.12.1.dev0

checkpoint-300/adapter_config.json

@@ -0,0 +1,31 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "bigcode/starcoderbase-1b",
+  "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": 32,
+  "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": [
+    "c_proj",
+    "c_fc",
+    "c_attn",
+    "q_attn"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

checkpoint-300/adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7d289994bea48630a214064b1f4f4d540dd50c8fe0b1009ca26cc7f2d6ea2a07
+size 22241240

checkpoint-300/optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:50ffaab46c673d6d0a38348867dc10cc692d2fbad4bf3dcde4d644393b20adbf
+size 44594810

checkpoint-300/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:79ec5c37f4c16cfc2374f2325906bbe634a17293146746c113a3ef50890336e0
+size 14244

checkpoint-300/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:011625d6b9cb46061d32ca7fe2179d571926a66a901f0584d591ac4f9b8e30db
+size 1064

checkpoint-300/trainer_state.json

@@ -0,0 +1,213 @@
+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 0.6,
+  "eval_steps": 25,
+  "global_step": 300,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.05,
+      "grad_norm": 0.18923527002334595,
+      "learning_rate": 0.0001666666666666667,
+      "loss": 1.1779,
+      "step": 25
+    },
+    {
+      "epoch": 0.05,
+      "eval_loss": 0.7435017824172974,
+      "eval_runtime": 621.1563,
+      "eval_samples_per_second": 4.356,
+      "eval_steps_per_second": 0.546,
+      "step": 25
+    },
+    {
+      "epoch": 0.1,
+      "grad_norm": 0.16780352592468262,
+      "learning_rate": 0.00019148936170212768,
+      "loss": 1.2708,
+      "step": 50
+    },
+    {
+      "epoch": 0.1,
+      "eval_loss": 0.7384106516838074,
+      "eval_runtime": 619.3795,
+      "eval_samples_per_second": 4.369,
+      "eval_steps_per_second": 0.547,
+      "step": 50
+    },
+    {
+      "epoch": 0.15,
+      "grad_norm": 0.1804531365633011,
+      "learning_rate": 0.00018085106382978726,
+      "loss": 1.2848,
+      "step": 75
+    },
+    {
+      "epoch": 0.15,
+      "eval_loss": 0.7341726422309875,
+      "eval_runtime": 619.225,
+      "eval_samples_per_second": 4.37,
+      "eval_steps_per_second": 0.547,
+      "step": 75
+    },
+    {
+      "epoch": 0.2,
+      "grad_norm": 0.17204701900482178,
+      "learning_rate": 0.00017021276595744682,
+      "loss": 1.3443,
+      "step": 100
+    },
+    {
+      "epoch": 0.2,
+      "eval_loss": 0.7313376665115356,
+      "eval_runtime": 619.8363,
+      "eval_samples_per_second": 4.366,
+      "eval_steps_per_second": 0.547,
+      "step": 100
+    },
+    {
+      "epoch": 0.25,
+      "grad_norm": 0.2668892741203308,
+      "learning_rate": 0.00015957446808510637,
+      "loss": 1.2563,
+      "step": 125
+    },
+    {
+      "epoch": 0.25,
+      "eval_loss": 0.7282304167747498,
+      "eval_runtime": 619.962,
+      "eval_samples_per_second": 4.365,
+      "eval_steps_per_second": 0.547,
+      "step": 125
+    },
+    {
+      "epoch": 0.3,
+      "grad_norm": 0.21089820563793182,
+      "learning_rate": 0.00014893617021276596,
+      "loss": 0.7529,
+      "step": 150
+    },
+    {
+      "epoch": 0.3,
+      "eval_loss": 0.7184526324272156,
+      "eval_runtime": 619.2606,
+      "eval_samples_per_second": 4.37,
+      "eval_steps_per_second": 0.547,
+      "step": 150
+    },
+    {
+      "epoch": 0.35,
+      "grad_norm": 0.158623605966568,
+      "learning_rate": 0.00013829787234042554,
+      "loss": 0.7957,
+      "step": 175
+    },
+    {
+      "epoch": 0.35,
+      "eval_loss": 0.7136204242706299,
+      "eval_runtime": 619.6757,
+      "eval_samples_per_second": 4.367,
+      "eval_steps_per_second": 0.547,
+      "step": 175
+    },
+    {
+      "epoch": 0.4,
+      "grad_norm": 0.16229337453842163,
+      "learning_rate": 0.00012765957446808513,
+      "loss": 0.7322,
+      "step": 200
+    },
+    {
+      "epoch": 0.4,
+      "eval_loss": 0.7111368179321289,
+      "eval_runtime": 620.1668,
+      "eval_samples_per_second": 4.363,
+      "eval_steps_per_second": 0.547,
+      "step": 200
+    },
+    {
+      "epoch": 0.45,
+      "grad_norm": 0.2108817994594574,
+      "learning_rate": 0.00011702127659574468,
+      "loss": 0.671,
+      "step": 225
+    },
+    {
+      "epoch": 0.45,
+      "eval_loss": 0.708846390247345,
+      "eval_runtime": 683.0846,
+      "eval_samples_per_second": 3.961,
+      "eval_steps_per_second": 0.496,
+      "step": 225
+    },
+    {
+      "epoch": 0.5,
+      "grad_norm": 0.24791103601455688,
+      "learning_rate": 0.00010638297872340425,
+      "loss": 0.5157,
+      "step": 250
+    },
+    {
+      "epoch": 0.5,
+      "eval_loss": 0.7082125544548035,
+      "eval_runtime": 621.1208,
+      "eval_samples_per_second": 4.357,
+      "eval_steps_per_second": 0.546,
+      "step": 250
+    },
+    {
+      "epoch": 0.55,
+      "grad_norm": 0.17589089274406433,
+      "learning_rate": 9.574468085106384e-05,
+      "loss": 0.4863,
+      "step": 275
+    },
+    {
+      "epoch": 0.55,
+      "eval_loss": 0.7080355882644653,
+      "eval_runtime": 619.9715,
+      "eval_samples_per_second": 4.365,
+      "eval_steps_per_second": 0.547,
+      "step": 275
+    },
+    {
+      "epoch": 0.6,
+      "grad_norm": 0.18594518303871155,
+      "learning_rate": 8.510638297872341e-05,
+      "loss": 0.5497,
+      "step": 300
+    },
+    {
+      "epoch": 0.6,
+      "eval_loss": 0.7067092657089233,
+      "eval_runtime": 619.2811,
+      "eval_samples_per_second": 4.37,
+      "eval_steps_per_second": 0.547,
+      "step": 300
+    }
+  ],
+  "logging_steps": 25,
+  "max_steps": 500,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 9223372036854775807,
+  "save_steps": 100,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": false
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 6.0475890991104e+16,
+  "train_batch_size": 1,
+  "trial_name": null,
+  "trial_params": null
+}

checkpoint-300/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf0bab29e9b3132b540b844be698b8d6cee10ba9f03ea8a1a2c3173e08e976c0
+size 5240

checkpoint-400/README.md

@@ -0,0 +1,202 @@
+---
+base_model: bigcode/starcoderbase-1b
+library_name: peft
+---
+
+# 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 0.12.1.dev0

checkpoint-400/adapter_config.json

@@ -0,0 +1,31 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "bigcode/starcoderbase-1b",
+  "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": 32,
+  "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": [
+    "c_proj",
+    "c_fc",
+    "c_attn",
+    "q_attn"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

checkpoint-400/adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:040bdbe17b6a4596b2a7a8008957c8e536113b092c49a90ca55fd4918546f748
+size 22241240

checkpoint-400/optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c3b9ed5c80976fb574ae9a997544da020bf1a811116d86d303a2bc32b8a04de
+size 44594810

checkpoint-400/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d86f35baf79b824eb04e0ea9ba7c619c7f80de41c6535aacbaac77b6d4578acc
+size 14244

checkpoint-400/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0ee470593ab3968bc9ba7865981396cc326f4b2a1146c08e7eab84acb6bb9b43
+size 1064

checkpoint-400/trainer_state.json

@@ -0,0 +1,273 @@
+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 0.8,
+  "eval_steps": 25,
+  "global_step": 400,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.05,
+      "grad_norm": 0.18923527002334595,
+      "learning_rate": 0.0001666666666666667,
+      "loss": 1.1779,
+      "step": 25
+    },
+    {
+      "epoch": 0.05,
+      "eval_loss": 0.7435017824172974,
+      "eval_runtime": 621.1563,
+      "eval_samples_per_second": 4.356,
+      "eval_steps_per_second": 0.546,
+      "step": 25
+    },
+    {
+      "epoch": 0.1,
+      "grad_norm": 0.16780352592468262,
+      "learning_rate": 0.00019148936170212768,
+      "loss": 1.2708,
+      "step": 50
+    },
+    {
+      "epoch": 0.1,
+      "eval_loss": 0.7384106516838074,
+      "eval_runtime": 619.3795,
+      "eval_samples_per_second": 4.369,
+      "eval_steps_per_second": 0.547,
+      "step": 50
+    },
+    {
+      "epoch": 0.15,
+      "grad_norm": 0.1804531365633011,
+      "learning_rate": 0.00018085106382978726,
+      "loss": 1.2848,
+      "step": 75
+    },
+    {
+      "epoch": 0.15,
+      "eval_loss": 0.7341726422309875,
+      "eval_runtime": 619.225,
+      "eval_samples_per_second": 4.37,
+      "eval_steps_per_second": 0.547,
+      "step": 75
+    },
+    {
+      "epoch": 0.2,
+      "grad_norm": 0.17204701900482178,
+      "learning_rate": 0.00017021276595744682,
+      "loss": 1.3443,
+      "step": 100
+    },
+    {
+      "epoch": 0.2,
+      "eval_loss": 0.7313376665115356,
+      "eval_runtime": 619.8363,
+      "eval_samples_per_second": 4.366,
+      "eval_steps_per_second": 0.547,
+      "step": 100
+    },
+    {
+      "epoch": 0.25,
+      "grad_norm": 0.2668892741203308,
+      "learning_rate": 0.00015957446808510637,
+      "loss": 1.2563,
+      "step": 125
+    },
+    {
+      "epoch": 0.25,
+      "eval_loss": 0.7282304167747498,
+      "eval_runtime": 619.962,
+      "eval_samples_per_second": 4.365,
+      "eval_steps_per_second": 0.547,
+      "step": 125
+    },
+    {
+      "epoch": 0.3,
+      "grad_norm": 0.21089820563793182,
+      "learning_rate": 0.00014893617021276596,
+      "loss": 0.7529,
+      "step": 150
+    },
+    {
+      "epoch": 0.3,
+      "eval_loss": 0.7184526324272156,
+      "eval_runtime": 619.2606,
+      "eval_samples_per_second": 4.37,
+      "eval_steps_per_second": 0.547,
+      "step": 150
+    },
+    {
+      "epoch": 0.35,
+      "grad_norm": 0.158623605966568,
+      "learning_rate": 0.00013829787234042554,
+      "loss": 0.7957,
+      "step": 175
+    },
+    {
+      "epoch": 0.35,
+      "eval_loss": 0.7136204242706299,
+      "eval_runtime": 619.6757,
+      "eval_samples_per_second": 4.367,
+      "eval_steps_per_second": 0.547,
+      "step": 175
+    },
+    {
+      "epoch": 0.4,
+      "grad_norm": 0.16229337453842163,
+      "learning_rate": 0.00012765957446808513,
+      "loss": 0.7322,
+      "step": 200
+    },
+    {
+      "epoch": 0.4,
+      "eval_loss": 0.7111368179321289,
+      "eval_runtime": 620.1668,
+      "eval_samples_per_second": 4.363,
+      "eval_steps_per_second": 0.547,
+      "step": 200
+    },
+    {
+      "epoch": 0.45,
+      "grad_norm": 0.2108817994594574,
+      "learning_rate": 0.00011702127659574468,
+      "loss": 0.671,
+      "step": 225
+    },
+    {
+      "epoch": 0.45,
+      "eval_loss": 0.708846390247345,
+      "eval_runtime": 683.0846,
+      "eval_samples_per_second": 3.961,
+      "eval_steps_per_second": 0.496,
+      "step": 225
+    },
+    {
+      "epoch": 0.5,
+      "grad_norm": 0.24791103601455688,
+      "learning_rate": 0.00010638297872340425,
+      "loss": 0.5157,
+      "step": 250
+    },
+    {
+      "epoch": 0.5,
+      "eval_loss": 0.7082125544548035,
+      "eval_runtime": 621.1208,
+      "eval_samples_per_second": 4.357,
+      "eval_steps_per_second": 0.546,
+      "step": 250
+    },
+    {
+      "epoch": 0.55,
+      "grad_norm": 0.17589089274406433,
+      "learning_rate": 9.574468085106384e-05,
+      "loss": 0.4863,
+      "step": 275
+    },
+    {
+      "epoch": 0.55,
+      "eval_loss": 0.7080355882644653,
+      "eval_runtime": 619.9715,
+      "eval_samples_per_second": 4.365,
+      "eval_steps_per_second": 0.547,
+      "step": 275
+    },
+    {
+      "epoch": 0.6,
+      "grad_norm": 0.18594518303871155,
+      "learning_rate": 8.510638297872341e-05,
+      "loss": 0.5497,
+      "step": 300
+    },
+    {
+      "epoch": 0.6,
+      "eval_loss": 0.7067092657089233,
+      "eval_runtime": 619.2811,
+      "eval_samples_per_second": 4.37,
+      "eval_steps_per_second": 0.547,
+      "step": 300
+    },
+    {
+      "epoch": 0.65,
+      "grad_norm": 0.24192531406879425,
+      "learning_rate": 7.446808510638298e-05,
+      "loss": 0.4659,
+      "step": 325
+    },
+    {
+      "epoch": 0.65,
+      "eval_loss": 0.7063791155815125,
+      "eval_runtime": 619.417,
+      "eval_samples_per_second": 4.369,
+      "eval_steps_per_second": 0.547,
+      "step": 325
+    },
+    {
+      "epoch": 0.7,
+      "grad_norm": 0.1753111630678177,
+      "learning_rate": 6.382978723404256e-05,
+      "loss": 0.4427,
+      "step": 350
+    },
+    {
+      "epoch": 0.7,
+      "eval_loss": 0.7059241533279419,
+      "eval_runtime": 619.429,
+      "eval_samples_per_second": 4.369,
+      "eval_steps_per_second": 0.547,
+      "step": 350
+    },
+    {
+      "epoch": 0.75,
+      "grad_norm": 0.22774459421634674,
+      "learning_rate": 5.319148936170213e-05,
+      "loss": 0.4565,
+      "step": 375
+    },
+    {
+      "epoch": 0.75,
+      "eval_loss": 0.7058201432228088,
+      "eval_runtime": 619.3116,
+      "eval_samples_per_second": 4.369,
+      "eval_steps_per_second": 0.547,
+      "step": 375
+    },
+    {
+      "epoch": 0.8,
+      "grad_norm": 0.18160311877727509,
+      "learning_rate": 4.2553191489361704e-05,
+      "loss": 0.4964,
+      "step": 400
+    },
+    {
+      "epoch": 0.8,
+      "eval_loss": 0.7054587006568909,
+      "eval_runtime": 619.5833,
+      "eval_samples_per_second": 4.367,
+      "eval_steps_per_second": 0.547,
+      "step": 400
+    }
+  ],
+  "logging_steps": 25,
+  "max_steps": 500,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 9223372036854775807,
+  "save_steps": 100,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": false
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 8.0634521321472e+16,
+  "train_batch_size": 1,
+  "trial_name": null,
+  "trial_params": null
+}

checkpoint-400/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf0bab29e9b3132b540b844be698b8d6cee10ba9f03ea8a1a2c3173e08e976c0
+size 5240

checkpoint-500/README.md

@@ -0,0 +1,202 @@
+---
+base_model: bigcode/starcoderbase-1b
+library_name: peft
+---
+
+# 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 0.12.1.dev0

checkpoint-500/adapter_config.json

@@ -0,0 +1,31 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "bigcode/starcoderbase-1b",
+  "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": 32,
+  "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": [
+    "c_proj",
+    "c_fc",
+    "c_attn",
+    "q_attn"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

checkpoint-500/adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f8ad067c72fbc48be19d899f3c8042114dea086ec96a0bb89bb8896a1d2618c9
+size 22241240

checkpoint-500/optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:38c403b2631f02989de64e1b8df594c52858112ebf6a2ff452e592d07848473a
+size 44594810

checkpoint-500/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:392861f8be0d9de34cd06fa510b6a2f8731642b86d2cd629a1d819625ac50f24
+size 14244

checkpoint-500/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f4fe8e328be6e10e053e14666b2e571c45c73d9a8291556b08910e3da67b3e6
+size 1064

checkpoint-500/trainer_state.json

@@ -0,0 +1,333 @@
+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 1.0,
+  "eval_steps": 25,
+  "global_step": 500,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.05,
+      "grad_norm": 0.18923527002334595,
+      "learning_rate": 0.0001666666666666667,
+      "loss": 1.1779,
+      "step": 25
+    },
+    {
+      "epoch": 0.05,
+      "eval_loss": 0.7435017824172974,
+      "eval_runtime": 621.1563,
+      "eval_samples_per_second": 4.356,
+      "eval_steps_per_second": 0.546,
+      "step": 25
+    },
+    {
+      "epoch": 0.1,
+      "grad_norm": 0.16780352592468262,
+      "learning_rate": 0.00019148936170212768,
+      "loss": 1.2708,
+      "step": 50
+    },
+    {
+      "epoch": 0.1,
+      "eval_loss": 0.7384106516838074,
+      "eval_runtime": 619.3795,
+      "eval_samples_per_second": 4.369,
+      "eval_steps_per_second": 0.547,
+      "step": 50
+    },
+    {
+      "epoch": 0.15,
+      "grad_norm": 0.1804531365633011,
+      "learning_rate": 0.00018085106382978726,
+      "loss": 1.2848,
+      "step": 75
+    },
+    {
+      "epoch": 0.15,
+      "eval_loss": 0.7341726422309875,
+      "eval_runtime": 619.225,
+      "eval_samples_per_second": 4.37,
+      "eval_steps_per_second": 0.547,
+      "step": 75
+    },
+    {
+      "epoch": 0.2,
+      "grad_norm": 0.17204701900482178,
+      "learning_rate": 0.00017021276595744682,
+      "loss": 1.3443,
+      "step": 100
+    },
+    {
+      "epoch": 0.2,
+      "eval_loss": 0.7313376665115356,
+      "eval_runtime": 619.8363,
+      "eval_samples_per_second": 4.366,
+      "eval_steps_per_second": 0.547,
+      "step": 100
+    },
+    {
+      "epoch": 0.25,
+      "grad_norm": 0.2668892741203308,
+      "learning_rate": 0.00015957446808510637,
+      "loss": 1.2563,
+      "step": 125
+    },
+    {
+      "epoch": 0.25,
+      "eval_loss": 0.7282304167747498,
+      "eval_runtime": 619.962,
+      "eval_samples_per_second": 4.365,
+      "eval_steps_per_second": 0.547,
+      "step": 125
+    },
+    {
+      "epoch": 0.3,
+      "grad_norm": 0.21089820563793182,
+      "learning_rate": 0.00014893617021276596,
+      "loss": 0.7529,
+      "step": 150
+    },
+    {
+      "epoch": 0.3,
+      "eval_loss": 0.7184526324272156,
+      "eval_runtime": 619.2606,
+      "eval_samples_per_second": 4.37,
+      "eval_steps_per_second": 0.547,
+      "step": 150
+    },
+    {
+      "epoch": 0.35,
+      "grad_norm": 0.158623605966568,
+      "learning_rate": 0.00013829787234042554,
+      "loss": 0.7957,
+      "step": 175
+    },
+    {
+      "epoch": 0.35,
+      "eval_loss": 0.7136204242706299,
+      "eval_runtime": 619.6757,
+      "eval_samples_per_second": 4.367,
+      "eval_steps_per_second": 0.547,
+      "step": 175
+    },
+    {
+      "epoch": 0.4,
+      "grad_norm": 0.16229337453842163,
+      "learning_rate": 0.00012765957446808513,
+      "loss": 0.7322,
+      "step": 200
+    },
+    {
+      "epoch": 0.4,
+      "eval_loss": 0.7111368179321289,
+      "eval_runtime": 620.1668,
+      "eval_samples_per_second": 4.363,
+      "eval_steps_per_second": 0.547,
+      "step": 200
+    },
+    {
+      "epoch": 0.45,
+      "grad_norm": 0.2108817994594574,
+      "learning_rate": 0.00011702127659574468,
+      "loss": 0.671,
+      "step": 225
+    },
+    {
+      "epoch": 0.45,
+      "eval_loss": 0.708846390247345,
+      "eval_runtime": 683.0846,
+      "eval_samples_per_second": 3.961,
+      "eval_steps_per_second": 0.496,
+      "step": 225
+    },
+    {
+      "epoch": 0.5,
+      "grad_norm": 0.24791103601455688,
+      "learning_rate": 0.00010638297872340425,
+      "loss": 0.5157,
+      "step": 250
+    },
+    {
+      "epoch": 0.5,
+      "eval_loss": 0.7082125544548035,
+      "eval_runtime": 621.1208,
+      "eval_samples_per_second": 4.357,
+      "eval_steps_per_second": 0.546,
+      "step": 250
+    },
+    {
+      "epoch": 0.55,
+      "grad_norm": 0.17589089274406433,
+      "learning_rate": 9.574468085106384e-05,
+      "loss": 0.4863,
+      "step": 275
+    },
+    {
+      "epoch": 0.55,
+      "eval_loss": 0.7080355882644653,
+      "eval_runtime": 619.9715,
+      "eval_samples_per_second": 4.365,
+      "eval_steps_per_second": 0.547,
+      "step": 275
+    },
+    {
+      "epoch": 0.6,
+      "grad_norm": 0.18594518303871155,
+      "learning_rate": 8.510638297872341e-05,
+      "loss": 0.5497,
+      "step": 300
+    },
+    {
+      "epoch": 0.6,
+      "eval_loss": 0.7067092657089233,
+      "eval_runtime": 619.2811,
+      "eval_samples_per_second": 4.37,
+      "eval_steps_per_second": 0.547,
+      "step": 300
+    },
+    {
+      "epoch": 0.65,
+      "grad_norm": 0.24192531406879425,
+      "learning_rate": 7.446808510638298e-05,
+      "loss": 0.4659,
+      "step": 325
+    },
+    {
+      "epoch": 0.65,
+      "eval_loss": 0.7063791155815125,
+      "eval_runtime": 619.417,
+      "eval_samples_per_second": 4.369,
+      "eval_steps_per_second": 0.547,
+      "step": 325
+    },
+    {
+      "epoch": 0.7,
+      "grad_norm": 0.1753111630678177,
+      "learning_rate": 6.382978723404256e-05,
+      "loss": 0.4427,
+      "step": 350
+    },
+    {
+      "epoch": 0.7,
+      "eval_loss": 0.7059241533279419,
+      "eval_runtime": 619.429,
+      "eval_samples_per_second": 4.369,
+      "eval_steps_per_second": 0.547,
+      "step": 350
+    },
+    {
+      "epoch": 0.75,
+      "grad_norm": 0.22774459421634674,
+      "learning_rate": 5.319148936170213e-05,
+      "loss": 0.4565,
+      "step": 375
+    },
+    {
+      "epoch": 0.75,
+      "eval_loss": 0.7058201432228088,
+      "eval_runtime": 619.3116,
+      "eval_samples_per_second": 4.369,
+      "eval_steps_per_second": 0.547,
+      "step": 375
+    },
+    {
+      "epoch": 0.8,
+      "grad_norm": 0.18160311877727509,
+      "learning_rate": 4.2553191489361704e-05,
+      "loss": 0.4964,
+      "step": 400
+    },
+    {
+      "epoch": 0.8,
+      "eval_loss": 0.7054587006568909,
+      "eval_runtime": 619.5833,
+      "eval_samples_per_second": 4.367,
+      "eval_steps_per_second": 0.547,
+      "step": 400
+    },
+    {
+      "epoch": 0.85,
+      "grad_norm": 0.21065783500671387,
+      "learning_rate": 3.191489361702128e-05,
+      "loss": 0.5872,
+      "step": 425
+    },
+    {
+      "epoch": 0.85,
+      "eval_loss": 0.7043496966362,
+      "eval_runtime": 620.3928,
+      "eval_samples_per_second": 4.362,
+      "eval_steps_per_second": 0.546,
+      "step": 425
+    },
+    {
+      "epoch": 0.9,
+      "grad_norm": 0.30514490604400635,
+      "learning_rate": 2.1276595744680852e-05,
+      "loss": 0.5681,
+      "step": 450
+    },
+    {
+      "epoch": 0.9,
+      "eval_loss": 0.7028947472572327,
+      "eval_runtime": 619.3459,
+      "eval_samples_per_second": 4.369,
+      "eval_steps_per_second": 0.547,
+      "step": 450
+    },
+    {
+      "epoch": 0.95,
+      "grad_norm": 0.19263707101345062,
+      "learning_rate": 1.0638297872340426e-05,
+      "loss": 0.7266,
+      "step": 475
+    },
+    {
+      "epoch": 0.95,
+      "eval_loss": 0.7024132609367371,
+      "eval_runtime": 619.4408,
+      "eval_samples_per_second": 4.368,
+      "eval_steps_per_second": 0.547,
+      "step": 475
+    },
+    {
+      "epoch": 1.0,
+      "grad_norm": 0.2509796619415283,
+      "learning_rate": 0.0,
+      "loss": 0.5945,
+      "step": 500
+    },
+    {
+      "epoch": 1.0,
+      "eval_loss": 0.7022281885147095,
+      "eval_runtime": 619.6582,
+      "eval_samples_per_second": 4.367,
+      "eval_steps_per_second": 0.547,
+      "step": 500
+    }
+  ],
+  "logging_steps": 25,
+  "max_steps": 500,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 9223372036854775807,
+  "save_steps": 100,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": true
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 1.0079315165184e+17,
+  "train_batch_size": 1,
+  "trial_name": null,
+  "trial_params": null
+}

checkpoint-500/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf0bab29e9b3132b540b844be698b8d6cee10ba9f03ea8a1a2c3173e08e976c0
+size 5240

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf0bab29e9b3132b540b844be698b8d6cee10ba9f03ea8a1a2c3173e08e976c0
+size 5240