Upload fine-tuned Chronos-2 model with model card and initial metrics

README.md

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+---
+license: apache-2.0
+tags:
+- time-series-forecasting
+- solar-energy
+- chronos
+- autogluon
+- lora
+- australia
+library_name: autogluon
+base_model: amazon/chronos-2
+datasets:
+- codenhenhe/volta-solar-daily-v1
+---
+
+# Chronos-2 Fine-tuned for Australian Solar Generation Forecasting
+
+This repository contains a fine-tuned Chronos-2 model for daily solar generation forecasting in Australia. The model was fine-tuned using AutoGluon TimeSeriesPredictor and saved as an AutoGluon predictor directory.
+
+## Model Details
+
+- Base model: `amazon/chronos-2`
+- Framework: AutoGluon TimeSeriesPredictor
+- Fine-tuning method: LoRA
+- Forecasting task: Daily solar generation forecasting
+- Prediction length: 365 days
+- Context length: 730 days
+- Target variable: Daily solar generation
+- Evaluation metric during training: MSE
+
+## Input Features
+
+The model uses historical solar generation together with temporal and weather-related covariates.
+
+Known covariates include:
+
+- `day_of_year`
+- `month`
+- `ALLSKY_SFC_SW_DWN`
+- `T2M`
+- `WS2M`
+- `RH2M`
+
+## Initial Metrics
+
+The following metrics were obtained from the initial evaluation after fine-tuning. The model was evaluated on the 2024 and 2025 test periods.
+
+### Point Forecast Accuracy
+
+| Model | Period | MAE | MSE | RMSE | sMAPE (%) | R2 |
+|---|---:|---:|---:|---:|---:|---:|
+| Chronos Fine-Tuned | 2024 | 2.374061 | 11.042445 | 3.323017 | 10.768443 | 0.889873 |
+| Chronos Fine-Tuned | 2025 | 2.468275 | 12.076611 | 3.475142 | 11.053124 | 0.883387 |
+| Chronos Fine-Tuned | Overall | 2.421168 | 11.559528 | 3.399931 | 10.910783 | 0.886614 |
+
+### Probabilistic Metrics
+
+| Model | Period | Q_0.1 | Q_0.9 | Avg_Q_Loss |
+|---|---:|---:|---:|---:|
+| Chronos Fine-Tuned | 2024 | 0.601763 | 0.500016 | 0.550889 |
+| Chronos Fine-Tuned | 2025 | 0.635956 | 0.506356 | 0.571156 |
+| Chronos Fine-Tuned | Overall | 0.618859 | 0.503186 | 0.561023 |
+
+## Baseline Comparison
+
+The fine-tuned model was also compared against a seasonal naive baseline.
+
+| Model | MAE | RMSE | MAPE (%) | R2 |
+|---|---:|---:|---:|---:|
+| Chronos2-Volta AI | 2.600748 | 3.640213 | 12.897249 | 0.870021 |
+| Seasonal Naive Baseline | 6.080324 | 8.468857 | 29.693408 | 0.296490 |
+
+## Loading the Model
+
+This model is stored as an AutoGluon TimeSeriesPredictor directory. After downloading the repository, it can be loaded as follows:
+
+```python
+from autogluon.timeseries import TimeSeriesPredictor
+
+predictor = TimeSeriesPredictor.load("path_to_downloaded_model")
+```
+
+## Intended Use
+
+This model is intended for postcode-based daily solar generation forecasting in Australia. It can be used as part of a pipeline that maps a user postcode to a representative solar reference point, retrieves or simulates weather covariates, and forecasts future solar generation.
+
+## Limitations
+
+The model depends on the quality of postcode mapping, weather covariates, and historical solar generation data. Forecast accuracy may vary across regions, seasons, and unusual weather conditions.

learner.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ef25106b2ed1917522418077381998e7880c90c0d1fcf94f6377f643df1be8c9
+size 7432

logs/predictor_log.txt

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+Beginning AutoGluon training... Time limit = 3600s
+AutoGluon will save models to '/content/drive/MyDrive/TT/finetuned_model/chronos_2/weather_vars_6_1e_6_300_8'
+=================== System Info ===================
+AutoGluon Version:  1.5.0
+Python Version:     3.12.13
+Operating System:   Linux
+Platform Machine:   x86_64
+Platform Version:   #1 SMP Thu Apr 30 18:17:14 UTC 2026
+CPU Count:          2
+Pytorch Version:    2.9.1+cu128
+CUDA Version:       12.8
+GPU Memory:         GPU 0: 14.55/14.56 GB
+Total GPU Memory:   Free: 14.55 GB, Allocated: 0.02 GB, Total: 14.56 GB
+GPU Count:          1
+Memory Avail:       8.75 GB / 12.67 GB (69.1%)
+Disk Space Avail:   3.38 GB / 15.00 GB (22.5%)
+	WARNING: Available disk space is low and there is a risk that AutoGluon will run out of disk during fit, causing an exception. 
+	We recommend a minimum available disk space of 10 GB, and large datasets may require more.
+===================================================
+
+Fitting with arguments:
+{'enable_ensemble': False,
+ 'eval_metric': MSE,
+ 'hyperparameters': {'Chronos2': [{'ag_args': {'name_suffix': '_ZeroShot'}},
+                                  {'ag_args': {'name_suffix': '_FineTuned'},
+                                   'eval_during_fine_tune': True,
+                                   'fine_tune': True,
+                                   'fine_tune_batch_size': 8,
+                                   'fine_tune_context_length': 730,
+                                   'fine_tune_lora_config': {'lora_alpha': 64,
+                                                             'r': 32},
+                                   'fine_tune_lr': 1e-06,
+                                   'fine_tune_mode': 'lora',
+                                   'fine_tune_steps': 300}]},
+ 'known_covariates_names': ['day_of_year',
+                            'month',
+                            'ALLSKY_SFC_SW_DWN',
+                            'T2M',
+                            'WS2M',
+                            'RH2M'],
+ 'num_val_windows': 1,
+ 'prediction_length': 365,
+ 'quantile_levels': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
+ 'random_seed': 123,
+ 'refit_every_n_windows': 1,
+ 'refit_full': False,
+ 'skip_model_selection': False,
+ 'target': 'target',
+ 'time_limit': 3600,
+ 'verbosity': 2}
+
+Inferred time series frequency: 'D'
+Provided train_data has 1278500 rows, 500 time series. Median time series length is 2557 (min=2557, max=2557). 
+	Tuning data is provided. Setting num_val_windows = (1,). Validation scores will be computed on a single window of tuning_data.
+Provided tuning_data has 1461000 rows, 500 time series. Median time series length is 2922 (min=2922, max=2922). 
+
+Provided data contains following columns:
+	target: 'target'
+	known_covariates:
+		categorical:        []
+		continuous (float): ['day_of_year', 'month', 'ALLSKY_SFC_SW_DWN', 'T2M', 'WS2M', 'RH2M']
+
+To learn how to fix incorrectly inferred types, please see documentation for TimeSeriesPredictor.fit
+
+AutoGluon will gauge predictive performance using evaluation metric: 'MSE'
+	This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
+===================================================
+
+Starting training. Start time is 2026-05-15 07:25:02
+Models that will be trained: ['Chronos2_ZeroShot', 'Chronos2_FineTuned']
+Training timeseries model Chronos2_ZeroShot. Training for up to 1799.4s of the 3598.7s of remaining time.
+	-11.5679      = Validation score (-MSE)
+	1.38    s     = Training runtime
+	70.42   s     = Validation (prediction) runtime
+Training timeseries model Chronos2_FineTuned. Training for up to 3526.6s of the 3526.6s of remaining time.
+	-11.5228      = Validation score (-MSE)
+	144.65  s     = Training runtime
+	75.15   s     = Validation (prediction) runtime
+Training complete. Models trained: ['Chronos2_ZeroShot', 'Chronos2_FineTuned']
+Total runtime: 292.53 s
+Best model: Chronos2_FineTuned
+Best model score: -11.5228
+Loading predictor from path /content/drive/MyDrive/TT/finetuned_model/chronos_2/weather_vars_6_1e_6_300_8
+Loading predictor from path /content/drive/MyDrive/TT/finetuned_model/chronos_2/weather_vars
+Loading predictor from path /content/drive/MyDrive/TT/finetuned_model/chronos_2/weather_vars_6_1e_6_300_8

models/Chronos2_FineTuned/checkpoint-300/README.md

@@ -0,0 +1,206 @@
+---
+base_model: autogluon/chronos-2
+library_name: peft
+tags:
+- base_model:adapter:autogluon/chronos-2
+- lora
+- transformers
+---
+
+# 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 0.17.1

models/Chronos2_FineTuned/checkpoint-300/adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": {
+    "base_model_class": "Chronos2Model",
+    "parent_library": "chronos.chronos2.model"
+  },
+  "base_model_name_or_path": "autogluon/chronos-2",
+  "bias": "none",
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+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v",
+    "q"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

models/Chronos2_FineTuned/checkpoint-300/adapter_model.safetensors

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models/Chronos2_FineTuned/checkpoint-300/trainer_state.json

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+  "best_model_checkpoint": "/content/drive/MyDrive/TT/finetuned_model/chronos_2/weather_vars_6_1e_6_300_8/models/Chronos2_FineTuned/checkpoint-300",
+  "epoch": 1.0,
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models/Chronos2_FineTuned/checkpoint-300/training_args.bin

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models/Chronos2_FineTuned/fine-tuned-ckpt/README.md

@@ -0,0 +1,206 @@
+---
+base_model: autogluon/chronos-2
+library_name: peft
+tags:
+- base_model:adapter:autogluon/chronos-2
+- lora
+- transformers
+---
+
+# 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 0.17.1

models/Chronos2_FineTuned/fine-tuned-ckpt/adapter_config.json

@@ -0,0 +1,40 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": {
+    "base_model_class": "Chronos2Model",
+    "parent_library": "chronos.chronos2.model"
+  },
+  "base_model_name_or_path": "autogluon/chronos-2",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
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