Upload best LoRA adapter (auto_promote_best)

README.md

@@ -1,391 +1,210 @@
 ---
-license: apache-2.0
-base_model: google/gemma-4-E2B-it
+base_model: unsloth/gemma-4-e2b-it-unsloth-bnb-4bit
 library_name: peft
+pipeline_tag: text-generation
 tags:
-- vision
-- object-detection
-- marine-debris
-- environmental-ai
-- unsloth
+- base_model:adapter:unsloth/gemma-4-e2b-it-unsloth-bnb-4bit
 - lora
-- gemma-4
-- peft
-- edge-ai
-- on-device
-- mobile
-datasets:
-- cleansea
-- ocean_garbage
-- neural_ocean
-language:
-- en
-pipeline_tag: image-to-text
-metrics:
-- mAP
-model-index:
-- name: gemma-4-E2B-it-oceanguard-marine-debris
-  results:
-  - task:
-      type: object-detection
-      name: Marine Debris Detection (open-vocabulary, 8-class collapse)
-    dataset:
-      type: oceanguard-merged-marine-debris
-      name: OceanGuard merged hold-out (CleanSea + Ocean_garbage + Neural_Ocean)
-    metrics:
-    - type: mAP@0.5
-      value: 0.3253
-    - type: mAP@0.5_delta_vs_base
-      value: 0.2329
+- sft
+- transformers
+- trl
+- unsloth
 ---
 
-# Gemma 4 E2B — OceanGuard Marine Debris LoRA Adapter
-
-A LoRA fine-tune of [`google/gemma-4-E2B-it`](https://huggingface.co/google/gemma-4-E2B-it)
-that adapts the model to open-vocabulary detection of anthropogenic marine debris on
-consumer mobile hardware. The adapter is the training artefact behind the
-[OceanGuard AI](https://github.com/asferrer/OceanguardAI) Android application — a fully
-offline marine-debris intelligence toolkit submitted to the **Kaggle Gemma 4 Good
-Hackathon** (Global Resilience track, Unsloth $10 K bonus) and built on top of two prior
-peer-reviewed publications on underwater debris detection ([Sánchez-Ferrer et al., PRL
-2023](https://www.sciencedirect.com/science/article/pii/S0167865522003889?via%3Dihub);
-[IbPRIA 2022](https://link.springer.com/chapter/10.1007/978-3-031-04881-4_49)).
-
-> **Headline result.** On a held-out 200-image evaluation set the LoRA fine-tune reaches
-> **mAP@0.5 = 0.325**, against **0.092** for the unmodified base model
-> (Δ = **+0.233**, a **+252 %** relative improvement) while maintaining
-> **94.5 %** JSON-validity on the structured detection output.
-
-## Model Description
-
-OceanGuard AI runs **Gemma 4 E2B** fully on-device on a Samsung Galaxy S22 Ultra
-(Exynos 2200, CPU + Vulkan/Xclipse 920) for open-vocabulary deep analysis of marine debris
-over a 50-class taxonomy that collapses down to **11 canonical ecological-impact families**
-(`BOTTLE`, `CAN`, `FISHING_NET`, `GLOVE`, `MASK`, `METAL_DEBRIS`, `PLASTIC_DEBRIS`, `TIRE`,
-`FABRIC_DEBRIS`, `GLASS_DEBRIS`, `OTHER`).
-
-This adapter aligns Gemma 4 E2B with the exact structured output contract that the Android
-application consumes — a JSON array of `{box_2d, label, material}` objects — and reduces the
-rate at which detections fall through to the generic `PLASTIC_DEBRIS` catch-all. The same
-LoRA also improves material attribution for the downstream ecological-impact lookup that
-drives the app's health-score and report-generation pipelines.
-
-The adapter is distributed as a standard PEFT LoRA (≈120 MB, `r=16`, `α=32`) attached to a
-bf16 base model. End-to-end reproduction fits comfortably on a single consumer GPU (the
-ablation grid below was run on an RTX 5090, but the recipe also runs on Kaggle T4 with
-4-bit quantisation).
-
-## Intended Use
-
-**In-scope use cases.**
-
-- Mobile and edge marine-debris detection for citizen science, NGO field operations, and
-  coastal monitoring campaigns.
-- Research baselines for open-vocabulary detection over specialised environmental
-  taxonomies.
-- A grounding component for tool-augmented reporting pipelines (see the OceanGuard
-  two-phase tool-calling architecture).
-
-**Out-of-scope use cases.**
-
-- Autonomous decision-making (cleanup dispatch, regulatory enforcement, environmental
-  fines). This model is a triage and reporting aid; final decisions must remain with a
-  qualified human.
-- Real-time safety-critical detection (collision avoidance, navigation). End-to-end
-  latency on the reference device is on the order of seconds per image.
-- General-purpose object detection outside the marine-debris domain — the adapter narrows
-  the visual prior towards marine debris and may degrade on unrelated tasks.
-
-## Training Data
-
-The training set is a permissive-license merge of three open marine-debris datasets,
-including the **CleanSea** corpus introduced by the model author ([IbPRIA
-2022](https://link.springer.com/chapter/10.1007/978-3-031-04881-4_49)):
-
-| Source | License | Annotations | Used in |
-|---|---|---|---|
-| CleanSea | Research-friendly (per dataset card) | COCO bbox, 8-class | All experiments |
-| Ocean_garbage | Research-friendly (per dataset card) | COCO bbox, 8-class | All experiments |
-| Neural_Ocean | Research-friendly (per dataset card) | COCO bbox, 8-class | All experiments |
-
-The ablation grid (see §Evaluation) was run over **eleven dataset compositions** that vary
-the ratio of synthetic-to-real images. The published adapter corresponds to experiment
-`exp10_real_full`, which uses **13 637 real-image training samples** stratified by class
-across the three source datasets. The 8-class taxonomy
-(`Bottle`, `Can`, `Fishing_Net`, `Glove`, `Mask`, `Metal_Debris`, `Plastic_Debris`, `Tire`)
-is extended at inference time to a 50-class fine-grained vocabulary that is mapped back to
-the 11 canonical families through a deterministic lookup table; see
-`Gemma4VisionDetector.LABEL_TO_TYPE` in the Android source for the full mapping.
-
-**Geographic coverage.** The three source datasets were collected predominantly in
-coastal waters around Japan and the wider north-western Pacific. As a result the
-training distribution is biased towards the debris taxonomy, water clarity, lighting
-and sediment patterns of that region. This is reflected in the §Limitations and §Bias
-sections below; users surveying outside Japan-Pacific waters should expect a measurable
-domain shift and validate the adapter on a local hold-out before operational use.
-
-Re-distributors of the merged dataset must comply with the original dataset licenses.
-
-## Training Procedure
-
-The published adapter is the winner of an 11-experiment LoRA ablation grid. All runs used
-**Unsloth FastVisionModel 2026.5.2** on a single workstation GPU, in bf16.
-
-### Configuration (best: `exp10_real_full`)
-
-| Hyper-parameter | Value |
-|---|---|
-| Base model | `unsloth/gemma-4-E2B-it` |
-| LoRA `r` / `α` / dropout | 16 / 32 / 0 |
-| Trainable parameters | 31 039 488 (0.60 % of the 5.15 B base) |
-| Target modules | language head linear layers; vision tower frozen |
-| Optimiser | `adamw_8bit` (Unsloth fused), `lr = 1e-4` cosine schedule, `weight_decay = 0.01`, `warmup_ratio = 0.1` |
-| Batch size | 1 per device × 32 gradient accumulation = effective 32 |
-| Sequence length / image token budget | 1 536 / 128 |
-| Precision | bf16 |
-| Epochs | 3 |
-| Steps | 1 281 |
-| Seed | 42 |
-| Loss | causal-LM over the `box_2d`+`label`+`material` JSON tokens |
-
-### Convergence
-
-Loss decreased monotonically from **15.01** at step 10 to **0.456** at step 1 281; the
-last 20 logging windows had a standard deviation of **0.0092** and a minimum loss of
-**0.446** at step 1 230, indicating a clean cosine landing with no late-stage divergence.
-
-### Hardware and wall-clock
-
-- 1× NVIDIA RTX 5090 (32 GiB GDDR7), Windows 11, CUDA 12.8, PyTorch 2.7
-- Training wall-clock: **6 h 53 min** (24 777 s) for `exp10_real_full`
-- Deployment target: Samsung Galaxy S22 Ultra (Exynos 2200) via
-  [LiteRT-LM 0.11.0](https://github.com/google-ai-edge/LiteRT-LM)
-
-### Software
-
-`unsloth` 2026.5.2 · `transformers` 5.5.0 · `peft` · `trl` · `accelerate` · `bitsandbytes` ·
-`torch` 2.7.0+cu128.
-
-The complete reproducible pipeline (`prepare_datasets → generate_configs → run_experiments
-→ eval_grid → collect_results → auto_promote_best`) is published in the source repository
-under `OceanguardAI-App/finetune/experiments/`.
+# 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
 
-All runs are evaluated on a **200-image held-out test split** of the merged dataset, with
-the same JSON contract and post-processing as the production Android app
-(`temperature = 0.0`, `max_new_tokens = 256`).
-
-### Aggregate
-
-| Metric | Base Gemma 4 E2B | OceanGuard LoRA (`exp10_real_full`) |
-|---|---|---|
-| **mAP@0.5** | **0.0924** | **0.3253** *(+0.2329)* |
-| Predictions emitted | 47 | 292 |
-| JSON validity | 1.000 | 0.945 |
-| Mean inference latency (RTX 5090) | 1.96 s | 5.43 s |
-
-### Per-class mAP@0.5
-
-| Class | Base | LoRA | Δ |
-|---|---|---|---|
-| Plastic Debris | 0.112 | **0.608** | **+0.496** |
-| Fishing Net | 0.091 | **0.448** | **+0.357** |
-| Tire | 0.091 | **0.403** | **+0.312** |
-| Glove | 0.309 | **0.463** | **+0.154** |
-| Bottle | 0.045 | **0.312** | **+0.266** |
-| Mask | 0.091 | **0.308** | **+0.217** |
-| Can | 0.000 | 0.061 | +0.061 |
-| Metal Debris | 0.000 | 0.000 | 0.000 |
-
-The fine-tune produces the largest absolute gains on `Plastic_Debris`, `Fishing_Net` and
-`Tire` — three of the most ecologically harmful categories in the OceanGuard impact
-hierarchy. `Metal_Debris` remains unlearned in this run (99 ground-truth instances in the
-training set is the known floor); a class-balanced data campaign is planned. The ablation
-grid (`exp01 … exp10`) and class-wise comparison against the base model are recorded in
-`finetune/experiments/results/results.md` in the source repository.
-
-## Deployment
-
-The adapter targets **LiteRT-LM 0.11.0** on Exynos 2200 once the upstream
-LoRA-to-`.litertlm` conversion path lands.
-
-| Step | Tooling | Status |
-|---|---|---|
-| Train LoRA on RTX 5090 | Unsloth FastVisionModel | ✅ Done |
-| Publish adapter on HuggingFace | `huggingface-cli` | ✅ Done |
-| Use adapter via Transformers / PEFT | `model.load_adapter(...)` | ✅ Done — see notebook |
-| Merge LoRA into base | `peft_model.merge_and_unload()` | ✅ Documented |
-| Export merged model to `.litertlm` | `ai-edge-torch.generative` | ⏳ Blocked on upstream PEFT-export support (in progress, May 2026) |
-| Sideload to Android | `adb push` + `VlmModelManager` | ✅ Documented |
-
-Once the conversion path is unblocked the published adapter is the exact artefact that
-will ship on-device — no retraining required. Until then, the adapter is consumed through
-Unsloth or stock PEFT in the companion notebook
-([`docs/submission/notebook_finetune.ipynb`](https://github.com/asferrer/OceanguardAI-App/blob/main/docs/submission/notebook_finetune.ipynb)),
-and the shipped APK uses the **base** Gemma 4 E2B for single-shot deep analysis. This
-is disclosed honestly in the submission write-up.
-
-Reference benchmarks on the deployment target
-(Galaxy S22 Ultra, Exynos 2200, LiteRT-LM 0.11.0, Vulkan via Xclipse 920):
-
-- Decode throughput: 5.1 tok/s
-- Time to first token (warm / cold first load): 2.33 s / 17.34 s
-- Prefill throughput: 48.7 tok/s
-- Single-shot detection, ~100-token JSON output: ≈ 22 s end-to-end
-
-## Limitations
-
-- **Fixed-with-fallback taxonomy.** The fine-tune adheres to an 8-class core extended to
-  50 fine-grained classes. Out-of-distribution debris (polar microplastic, deep-sea
-  anthropogenic objects) is not represented in the training data and will be mapped to
-  the generic catch-all classes.
-- **CPU + Vulkan-only inference on the reference device.** The Exynos 2200 GPU pass via
-  Vulkan improves correctness but not throughput; on this hardware Gemma 4 E2B is
-  reserved for single-shot deep analysis (≈ 20 s end-to-end per image), not for
-  continuous live-camera operation.
-- **Geographic bias.** The training imagery is predominantly sourced from coastal
-  waters around Japan and the broader north-western Pacific. Performance on the
-  Mediterranean, tropical reefs, polar seas, the south Atlantic and freshwater debris
-  surveys is expected to be lower and is **not yet measured**. Users surveying outside
-  Japan-Pacific waters should treat the model as a baseline and validate against a
-  local hold-out set before drawing conclusions.
-- **Lighting and capture bias.** Synthetic-augmentation passes can inject lighting biases
-  that may degrade under poor underwater visibility.
-- **Underrepresented classes.** `Metal_Debris` (99 ground-truth instances) is not learned
-  at the current data scale; `Can` improves only marginally. Cross-checks are advised on
-  predictions for these classes until a class-balanced campaign is shipped.
-- **JSON validity is 94.5 %, not 100 %.** Roughly 1 in 18 detections in the eval set
-  emerge in malformed JSON and are dropped by the parser. The Android app handles
-  these gracefully by surfacing an empty detection set to the user rather than
-  fabricating boxes.
-- **Not certified.** This model is not certified for legal, regulatory or environmental
-  enforcement reporting. The OceanGuard app produces draft reports that a human reviews
-  before any official use.
-- **Function-calling head untouched.** The fine-tune is intentionally scoped to the
-  detection head and does **not** rewrite tool-calling syntax, to avoid regressing the
-  two-phase tool-calling pipeline that the Android app depends on for grounded report
-  generation.
-
-## Bias, Risks and Safety
-
-- **Spurious-correlation risk.** Marine-debris classes co-occur with characteristic
-  backgrounds (e.g. fishing nets with rocky shorelines). The model may exploit context
-  cues that do not generalise across geographies.
-- **Sensitive categories.** The `syringe` / `medical waste` class is rare and high-stakes;
-  positive detections should trigger human handling protocols rather than autonomous
-  action.
-- **Citizen-science abuse vectors.** As with any open-vocabulary detector, the model can
-  be prompted to label arbitrary objects; downstream applications should constrain
-  inputs to the marine-debris domain and filter implausible outputs (the OceanGuard app
-  does this via `LABEL_TO_TYPE` and the canonical 11-class projection in
-  `EnvironmentalImpact.IMPACT_MAP`).
-- **Privacy.** Photos analysed on-device in OceanGuard never leave the phone; this
-  adapter itself processes no user data. When used outside the app, downstream operators
-  are responsible for compliance with applicable privacy regulation.
-
-## How to Use
-
-```python
-from unsloth import FastVisionModel
-
-model, tokenizer = FastVisionModel.from_pretrained(
-    "unsloth/gemma-4-E2B-it",
-    load_in_4bit=True,
-)
-model.load_adapter(
-    "asferrer/gemma-4-E2B-it-oceanguard-marine-debris",
-    adapter_name="oceanguard",
-)
-model.set_adapter("oceanguard")
-FastVisionModel.for_inference(model)
-
-# Use the exact DETECTION_PROMPT shipped in the Android app — see
-# docs/submission/notebook_finetune.ipynb for the verbatim string and helper.
-```
-
-A fully reproducible end-to-end example (image loading, inference, JSON parsing,
-visualisation and quantitative evaluation) lives at
-[`docs/submission/notebook_finetune.ipynb`](https://github.com/asferrer/OceanguardAI-App/blob/main/docs/submission/notebook_finetune.ipynb)
-in the source repository.
-
-## License and Citation
-
-- **LoRA adapter** — Apache 2.0
-- **Base model** — `google/gemma-4-E2B-it`, subject to the Gemma Terms of Use
-- **Training datasets** — source-dataset licenses apply (CC-BY or equivalent; see dataset
-  cards)
-
-```bibtex
-@misc{sanchezferrer2026oceanguard_adapter,
-  author       = {S{\'a}nchez-Ferrer, Alejandro},
-  title        = {{Gemma 4 E2B OceanGuard Marine Debris LoRA Adapter}},
-  year         = {2026},
-  howpublished = {HuggingFace Model Hub},
-  note         = {LoRA adapter for google/gemma-4-E2B-it. Apache 2.0.},
-  url          = {https://huggingface.co/asferrer/gemma-4-E2B-it-oceanguard-marine-debris}
-}
-
-@misc{sanchezferrer2026oceanguard_app,
-  author       = {S{\'a}nchez-Ferrer, Alejandro},
-  title        = {{OceanGuard AI: Fully Offline Marine Debris Intelligence with Gemma 4 and Two-Phase Tool Calling}},
-  year         = {2026},
-  howpublished = {Kaggle Gemma 4 Good Hackathon (Global Resilience track)},
-  url          = {https://github.com/asferrer/OceanguardAI}
-}
-```
-
-## Related Publications by the Author
-
-This adapter is the latest iteration of an ongoing line of research on automated
-detection and recognition of underwater anthropogenic debris.
-
-### Peer-reviewed papers
-
-1. **Sánchez-Ferrer, A.**, Valero-Mas, J. J., Gallego, A. J., & Calvo-Zaragoza, J. (2023).
-   *An experimental study on marine debris location and recognition using object
-   detection.*
-   **Pattern Recognition Letters**, 168, 154–161.
-   [https://doi.org/10.1016/j.patrec.2022.12.019](https://www.sciencedirect.com/science/article/pii/S0167865522003889?via%3Dihub)
-
-2. **Sánchez-Ferrer, A.**, Gallego, A. J., Valero-Mas, J. J., & Calvo-Zaragoza, J. (2022).
-   *The CleanSea Set: A Benchmark Corpus for Underwater Debris Detection and
-   Recognition.*
-   In *Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2022)*,
-   Lecture Notes in Computer Science, Springer.
-   [doi.org/10.1007/978-3-031-04881-4_49](https://link.springer.com/chapter/10.1007/978-3-031-04881-4_49)
-
-### Theses
-
-3. **Sánchez-Ferrer, A.** (2024). *Modelos de difusión aplicados a la detección de
-   objetos en el fondo marino.* Master's Thesis, Universidad de Alicante.
-   [rua.ua.es/…/88244474](https://rua.ua.es/entities/publication/88244474-6165-4cd9-a4af-68eff29d65c6)
-
-4. **Sánchez-Ferrer, A.** (2021). *Deep Learning aplicado a la detección de residuos en
-   el fondo marino.* Bachelor's Thesis, Universidad de Alicante.
-   [rua.ua.es/…/92c34588](https://rua.ua.es/entities/publication/92c34588-9842-4ec3-8b15-97a8ce718e04)
-
-### Applied research
-
-5. **Sánchez-Ferrer, A.** (2025). *OceanGuard AI: Mapping and Mitigating Marine
-   Pollution.* Kaggle hackathon write-up.
-   [Kaggle write-up](https://www.kaggle.com/competitions/google-gemma-3n-hackathon/writeups/oceanguard-ai-mapping-and-mitigating-marine-pollut)
-
-The CleanSea corpus introduced in (2) is one of the three sources used to train this
-adapter; the detection methodology developed in (1) defines the marine-debris recognition
-task that this fine-tune operationalises on-device with Gemma 4 E2B; (3) and (4) lay the
-foundational work on which the present model card builds.
-
-## Acknowledgments
-
-- **Google DeepMind** for releasing Gemma 4 under terms that enable open research and
-  on-device deployment.
-- **The Unsloth team** for the FastVisionModel implementation that makes a Gemma 4
-  vision-language fine-tune tractable on a single workstation GPU.
-- **CleanSea, Ocean_garbage and Neural_Ocean** dataset authors for releasing
-  marine-debris annotations under permissive terms.
-- **Google AI Edge / LiteRT-LM** team for the on-device runtime that hosts the base model
-  in the OceanGuard Android app.
-- **Pattern Recognition and Artificial Intelligence (PRAI) group, Universidad de
-  Alicante** — research group hosting the doctoral programme under which this work is
-  carried out, and co-authors of the prior publications cited above.
+<!-- 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 0.19.1