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+---
+name: huggingface-model-release
+description: >-
+  Autonomously migrate machine learning models to the Hugging Face Hub from
+  GitHub repositories. Clones the repo, analyzes code/configs/README to extract
+  all metadata, uploads raw weights (one repo per checkpoint/variant), writes
+  model cards with proper YAML metadata, creates collections, and verifies the
+  release. Use when the user provides a GitHub repository URL and wants the
+  model uploaded/published/released on Hugging Face.
+---
+
+# Hugging Face Model Release
+
+Autonomously migrate ML models from GitHub repositories to the Hugging Face Hub
+following official best practices. This skill relies on the `hf` CLI for all
+Hub operations. It is based on https://huggingface.co/docs/hub/model-release-checklist.
+
+## Prerequisites
+
+Before starting, ensure the `hf` CLI is installed (`curl -LsSf https://hf.co/cli/install.sh` on MacOS and Linux, see also https://huggingface.co/docs/huggingface_hub/en/guides/cli for details). Next, verify the environment:
+
+```bash
+hf auth whoami
+hf version
+```
+
+Authenticate via `hf auth login` or the `HF_TOKEN` environment variable. You need **write** access in order to upload weights to the Hugging Face hub. Ask the user in case you need to obtain one, and ask them to which Hugging Face user or organization the weights can be pushed.
+
+## Core Workflow
+
+Follow these steps for every model release:
+
+1. **Clone and analyze** the GitHub repository
+2. **Download weights** (from GitHub releases, LFS, or external links found in the repo)
+3. **Create repo(s)** on the Hub — one per checkpoint/variant
+4. **Write model card** (README.md) with proper YAML metadata
+5. **Upload** raw weights + model card
+6. **Create collection** (if multiple variants)
+7. **Post-release verification**
+
+---
+
+## Step 1: Clone and Analyze the GitHub Repository
+
+Clone the repo and systematically extract all information needed for the release.
+**Do not ask the user** — infer everything from the repo contents.
+
+```bash
+git clone https://github.com/org/repo.git --depth 1
+cd repo
+```
+
+### What to look for
+
+Scan the repository for the following. Use the table below as a checklist — every
+field must be resolved before proceeding to Step 2.
+
+| Field | Where to find it |
+|-------|-----------------|
+| **Model architecture / framework** | Config files (`config.json`, `config.yaml`), model definition source code, README |
+| **Task the model solves** | README, paper abstract, demo scripts, config files |
+| **License** | `LICENSE` file, README badge/section, `setup.py`/`pyproject.toml` |
+| **Associated paper (arXiv ID)** | README citations, `CITATION.cff`, BibTeX blocks, links in docs |
+| **Training dataset** | README training section, config files, data-loading scripts |
+| **Base model (if fine-tune)** | Config files (`_name_or_path`, `base_model`), README, training scripts |
+| **Weight file locations** | GitHub Releases assets, Git LFS files, download URLs in README/scripts, Google Drive/Dropbox links in README |
+| **Multiple variants / checkpoints** | Subdirectories, release tags, README sections listing variants |
+| **Target repo name** | Derive from GitHub `org/repo` name, following Hub naming conventions |
+| **Language(s)** | README, paper, dataset metadata |
+| **Library / framework** | Imports in source code (`import transformers`, `import diffusers`, etc.), `requirements.txt`, `setup.py` |
+
+### Extraction strategy
+
+1. **Read `README.md`** (or `README.rst`) — this is the richest source. Look for
+   architecture descriptions, benchmark tables, usage examples, paper links,
+   dataset references, and download instructions.
+2. **Read `LICENSE`** — map to an SPDX identifier (see metadata-reference.md).
+3. **Read config files** — `config.json`, `*.yaml`, `hparams.yaml`, etc. for
+   architecture, parameter counts, training hyperparameters.
+4. **Check GitHub Releases** — `gh release list` and `gh release view <tag>` to
+   find weight files and changelogs.
+5. **Scan source code** — identify the framework (`transformers`, `diffusers`,
+   `timm`, `torch`, `jax`, etc.) from imports.
+6. **Check `CITATION.cff`** or BibTeX blocks for the paper reference.
+7. **Check for multiple variants** — look for size suffixes in filenames,
+   separate release tags, or subdirectories with different configs.
+
+If a piece of information is genuinely not available in the repo, use a sensible
+default or omit the optional field. Never block the workflow waiting for user input.
+
+## Step 2: Download Weights
+
+Based on what you found in Step 1, download the model weights. Keep each
+checkpoint/variant in its own directory — these map 1:1 to Hub repos in Step 3.
+
+### GitHub Releases
+
+```bash
+# List available releases
+gh release list --repo org/repo
+
+# Download all assets from a release
+gh release download v1.0 --repo org/repo --dir ./weights
+
+# Download a specific asset
+gh release download v1.0 --repo org/repo --pattern "*.bin" --dir ./weights
+```
+
+### Git LFS files (already in the cloned repo)
+
+```bash
+git lfs pull
+```
+
+### External links found in README
+
+If the README points to Google Drive, Dropbox, or other hosts:
+
+```bash
+# Google Drive
+pip install gdown
+gdown "https://drive.google.com/uc?id=FILE_ID" -O model_weights.bin
+
+# Dropbox (replace dl=0 with dl=1)
+curl -L "https://www.dropbox.com/scl/fi/XXXXX/model.bin?dl=1" -o model.bin
+
+# Direct URL
+curl -L "https://example.com/model_weights.bin" -o model_weights.bin
+```
+
+### Organizing variants
+
+If the repo contains multiple checkpoints (e.g. small, base, large), download
+each into a separate directory:
+
+```bash
+mkdir -p variants/model-small variants/model-base variants/model-large
+# Move or download the corresponding files into each directory
+```
+
+## Step 3: Create Repos on the Hub
+
+**One repo per checkpoint/variant.** Do NOT upload multiple variants into
+subdirectories of the same repo.
+
+Naming convention: `org/model-name` or `org/model-name-size-variant`
+
+Examples:
+- `google/vit-base-patch16-224`
+- `meta-llama/Llama-3-8B`
+- `meta-llama/Llama-3-8B-Instruct`
+
+```bash
+hf repos create org/model-name --private
+# Add --private to prepare the repo before making it public
+```
+
+For multiple variants, create each repo separately:
+
+```bash
+hf repos create org/model-name-small
+hf repos create org/model-name-base
+hf repos create org/model-name-large
+```
+
+## Step 4: Write the Model Card
+
+The model card is the `README.md` in the repo root. It consists of YAML
+frontmatter (metadata) and markdown body.
+
+For the full template, see [references/model-card-template.md](references/model-card-template.md).
+For metadata field details, see [references/metadata-reference.md](references/metadata-reference.md).
+
+### Critical YAML Metadata
+
+Every model card MUST include these fields:
+
+```yaml
+---
+pipeline_tag: text-generation       # The task — determines widgets and search
+library_name: transformers          # The library — enables code snippets
+license: apache-2.0                 # The license
+language:
+  - en                              # Language(s) the model handles
+---
+```
+
+### Conditional Metadata
+
+Add these fields when applicable:
+
+```yaml
+datasets:
+  - username/dataset-name           # Training dataset(s) — creates cross-links
+
+base_model: meta-llama/Llama-3-8B  # If this is a fine-tune or adaptation
+
+base_model_relation: quantized      # If this is a quantized version
+                                    # (use with base_model)
+
+tags:
+  - vision
+  - medical                         # Domain-specific tags for discoverability
+
+new_version: org/newer-model        # Set on OLD model to point to its successor
+```
+
+### Model Card Body
+
+Write sections in this order:
+
+1. **Title and summary** — one-paragraph description of what the model does
+2. **Usage examples** — copy-and-run code snippets for inference:
+
+```python
+from transformers import pipeline
+pipe = pipeline("text-generation", model="org/model-name")
+result = pipe("Your prompt here")
+```
+
+3. **Training details** — dataset, hyperparameters, hardware used
+4. **Evaluation results** — benchmarks with quantitative metrics
+5. **Limitations and biases** — known failure modes and ethical considerations
+6. **Citation** — BibTeX entry if there's an associated paper:
+
+```bibtex
+@article{author2024model,
+  title={Model Title},
+  author={Author, First and Author, Second},
+  journal={arXiv preprint arXiv:XXXX.XXXXX},
+  year={2024}
+}
+```
+
+### For Image/Video Models
+
+Add visual examples using the Gallery component:
+
+```markdown
+<Gallery>
+![Example 1](./images/example1.png)
+![Example 2](./images/example2.png)
+</Gallery>
+```
+
+## Step 5: Upload
+
+Upload raw weight files and the model card as-is — no conversion needed.
+
+```bash
+# Upload everything in a variant's directory
+hf upload org/model-name ./variants/model-name --commit-message "Initial release"
+
+# Or upload specific files
+hf upload org/model-name checkpoint.pth --commit-message "Add model weights"
+hf upload org/model-name README.md --commit-message "Add model card"
+hf upload org/model-name config.json --commit-message "Add config"
+```
+
+For very large uploads (>50 GB or unreliable connection), use resumable upload:
+
+```bash
+hf upload-large-folder org/model-name ./variants/model-name
+```
+
+After upload, make the repo public when ready:
+
+```bash
+hf repos settings org/model-name --no-private
+```
+
+For models requiring controlled access:
+
+```bash
+hf repos settings org/model-name --gated auto
+```
+
+## Step 6: Create a Collection
+
+If releasing multiple related variants, group them:
+
+```bash
+hf collections create "Model Family Name" \
+  --namespace org \
+  --description "Collection of Model variants including base, large, and instruct versions"
+
+# Note the returned collection slug, then add items:
+hf collections add-item org/model-family-XXXX org/model-name-base model
+hf collections add-item org/model-family-XXXX org/model-name-large model
+hf collections add-item org/model-family-XXXX org/model-name-instruct model
+```
+
+## Step 7: Post-Release Verification
+
+Run through this checklist after uploading:
+
+- [ ] **Verify the model loads**: run the code snippet from the model card in a clean environment
+- [ ] **Check metadata renders**: visit `hf.co/org/model-name` and confirm the pipeline tag, library, and license display correctly
+- [ ] **Test the widget**: if the pipeline tag supports it, confirm the inference widget works on the model page
+- [ ] **Verify cross-links**: confirm linked datasets and base models appear on the model page
+- [ ] **Check download**: `hf download org/model-name --dry-run` to confirm files are accessible
+
+### Add Evaluation Results (optional)
+
+Create `.eval_results/` YAML files in the repo to show benchmark scores on the
+model page:
+
+```yaml
+# .eval_results/mmlu.yaml
+- dataset:
+    id: cais/mmlu
+    task_id: college_chemistry
+  value: 76.1
+  date: "2024-06-15"
+  source:
+    url: https://huggingface.co/org/model-name
+    name: Model Card
+    user: your-username
+```
+
+Upload via:
+
+```bash
+hf upload org/model-name .eval_results/ .eval_results/ \
+  --commit-message "Add evaluation results"
+```
+
+---
+
+## Decision Tree
+
+Use this to handle common variations:
+
+**Is this a fine-tune of an existing model?**
+- Yes → set `base_model: original-org/original-model` in metadata
+
+**Is this a quantized version?**
+- Yes → set both `base_model` and `base_model_relation: quantized`
+- Upload to a separate repo (e.g. `org/model-name-GGUF`)
+
+**Are there multiple model sizes or variants?**
+- Yes → create one repo per variant, then create a collection
+
+**Does the model have an associated paper?**
+- Yes → add arXiv link in the model card body; the Hub auto-generates `arxiv:ID` tags
+- Add BibTeX citation block
+
+**Is this an image/video generation model?**
+- Yes → include example outputs using `<Gallery>` component
+- Add example images to the repo under `./images/`
+
+**Does the model need gated access?**
+- Yes → set gated access via `hf repos settings org/model-name --gated auto`
+- Document access conditions in the model card
+
+**Is this replacing an older model?**
+- Yes → add `new_version: org/newer-model` to the OLD model's metadata
+
+## Naming Conventions
+
+| Pattern | When to use | Example |
+|---------|------------|---------|
+| `org/model` | Single model | `google/gemma-2b` |
+| `org/model-size` | Size variants | `meta-llama/Llama-3-8B` |
+| `org/model-size-variant` | Functional variants | `meta-llama/Llama-3-8B-Instruct` |
+| `org/model-GGUF` | Quantized formats | `bartowski/Llama-3-8B-GGUF` |
+
+## Common Pitfalls
+
+- **Mixing variants in one repo**: use separate repos (one per checkpoint), group with collections
+- **Missing `pipeline_tag`**: model won't appear in task-filtered searches or get a widget
+- **Missing `library_name`**: no code snippets shown on the model page
+- **Forgetting `base_model`**: fine-tunes won't link back to the original in the model tree
+- **Uploading without a model card**: always include a README.md, even a minimal one
diff --git a/skills/huggingface-model-release/references/metadata-reference.md b/skills/huggingface-model-release/references/metadata-reference.md
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+# Model Card Metadata Reference
+
+Quick reference for YAML frontmatter fields in Hugging Face model cards.
+
+## Table of Contents
+
+- [pipeline_tag](#pipeline_tag)
+- [library_name](#library_name)
+- [license](#license)
+- [language](#language)
+- [datasets](#datasets)
+- [base_model](#base_model)
+- [tags](#tags)
+- [new_version](#new_version)
+- [co2_eq_emissions](#co2_eq_emissions)
+
+---
+
+## pipeline_tag
+
+Determines the task model solves, inference related functions (Inference Providers), search filtering, and code snippets. Pick the
+single most accurate tag.
+
+### Text and NLP
+
+| Tag | Use when |
+|-----|----------|
+| `text-generation` | Autoregressive LLMs (GPT, LLaMA, Mistral) |
+| `text-classification` | Sentiment analysis, topic classification |
+| `token-classification` | NER, POS tagging |
+| `question-answering` | Extractive QA |
+| `fill-mask` | Masked language models (BERT, RoBERTa, DeBERTa) |
+| `summarization` | Text summarization |
+| `translation` | Machine translation |
+| `conversational` | Dialogue / chat models |
+| `sentence-similarity` | Sentence embeddings, semantic search |
+| `feature-extraction` | Generic embeddings |
+| `zero-shot-classification` | Zero-shot text classification (NLI-based) |
+| `table-question-answering` | Answering questions about tabular data |
+
+### Vision
+
+| Tag | Use when |
+|-----|----------|
+| `image-classification` | Classify images into categories |
+| `object-detection` | Detect and localize objects |
+| `image-segmentation` | Semantic / instance / panoptic segmentation |
+| `depth-estimation` | Monocular depth prediction |
+| `image-feature-extraction` | Image embeddings (DINOv2, AIMv2, ViT) |
+| `zero-shot-image-classification` | CLIP-style zero-shot classification |
+| `keypoint-detection` | Keypoint detection models (e.g. ViTPose) |
+| `image-to-3d` | Image-to-3D models |
+
+### Vision-Language
+
+| Tag | Use when |
+|-----|----------|
+| `image-text-to-text` | VLMs that take image+text and output text (LLaVA, Qwen-VL) and some OCR models that take in task prompts |
+| `visual-question-answering` | Answer questions about images |
+| `document-question-answering` | Answer questions about documents/scans |
+| `image-to-text` | Image captioning |
+
+### Image/Video Generation
+
+| Tag | Use when |
+|-----|----------|
+| `text-to-image` | Generate images from text (FLUX.1, Z-Image, Qwen-Image) |
+| `image-to-image` | Image transformation (style transfer, super-resolution) |
+| `text-to-video` | Video generation from text |
+| `unconditional-image-generation` | Generate images without conditioning |
+| `text-to-3d` | Text-to-3D models |
+
+### Audio
+
+| Tag | Use when |
+|-----|----------|
+| `automatic-speech-recognition` | Speech-to-text (Whisper) |
+| `text-to-speech` | Text-to-audio synthesis |
+| `audio-classification` | Classify audio clips |
+| `text-to-audio` | Generate audio from text descriptions |
+
+### Multimodal
+
+| Tag | Use when |
+|-----|----------|
+| `any-to-any` | Models handling arbitrary input/output modalities (Gemma-3n, Qwen-Omni, Janus-Pro, MiniCPM-o) |
+
+### Others
+
+| Tag | Use when |
+|-----|----------|
+| `robotics` | Robotics models, vision-language-action (VLA) models |
+| `reinforcement-learning` | RL agents and policies |
+
+---
+
+## library_name
+
+Specifies the framework. Enables "Use this model" code snippets on the model
+page and allows download tracking.
+
+| Value | Framework |
+|-------|-----------|
+| `transformers` | Hugging Face Transformers |
+| `diffusers` | Hugging Face Diffusers (Stable Diffusion, etc.) |
+| `sentence-transformers` | Sentence Transformers |
+| `timm` | PyTorch Image Models |
+| `peft` | PEFT adapters (LoRA, QLoRA) |
+| `setfit` | SetFit few-shot classification |
+| `spacy` | spaCy NLP |
+| `fasttext` | FastText |
+| `flair` | Flair NLP |
+| `allennlp` | AllenNLP |
+| `adapter-transformers` | Adapter-based fine-tuning |
+| `onnx` | ONNX Runtime |
+| `tensorrt` | NVIDIA TensorRT |
+| `openvino` | Intel OpenVINO |
+| `mlx` | Apple MLX |
+| `gguf` | GGUF quantized models (llama.cpp) |
+
+If the model doesn't use any registered library, omit this field.
+
+---
+
+## license
+
+Use SPDX identifiers. Common choices:
+
+| Identifier | License |
+|------------|---------|
+| `apache-2.0` | Apache License 2.0 |
+| `mit` | MIT License |
+| `cc-by-4.0` | Creative Commons Attribution 4.0 |
+| `cc-by-sa-4.0` | CC Attribution-ShareAlike 4.0 |
+| `cc-by-nc-4.0` | CC Attribution-NonCommercial 4.0 |
+| `cc-by-nc-sa-4.0` | CC Attribution-NonCommercial-ShareAlike 4.0 |
+| `gpl-3.0` | GNU GPL v3 |
+| `llama3` | Meta Llama 3 Community License |
+| `llama3.1` | Meta Llama 3.1 Community License |
+| `llama3.2` | Meta Llama 3.2 Community License |
+| `gemma` | Google Gemma License |
+| `openrail` | Open RAIL License |
+| `openrail++` | Open RAIL++ License |
+| `bigscience-openrail-m` | BigScience OpenRAIL-M |
+| `other` | Custom license (describe in model card) |
+
+For custom or proprietary licenses, use `license: other` and add a
+`license_name` and optionally `license_link` field:
+
+```yaml
+license: other
+license_name: my-custom-license
+license_link: https://example.com/license
+```
+
+---
+
+## language
+
+ISO 639-1 codes. Use a list for multilingual models:
+
+```yaml
+language:
+  - en
+  - fr
+  - de
+  - zh
+```
+
+Common codes: `en` (English), `zh` (Chinese), `es` (Spanish), `fr` (French),
+`de` (German), `ja` (Japanese), `ko` (Korean), `ar` (Arabic), `hi` (Hindi),
+`pt` (Portuguese), `ru` (Russian), `multilingual`.
+
+---
+
+## datasets
+
+Link to Hub datasets used for training. Creates cross-links on both the model
+and dataset pages:
+
+```yaml
+datasets:
+  - username/my-dataset
+```
+
+Use the full `org/dataset-name` format for non-canonical datasets.
+
+---
+
+## base_model
+
+Set when this model derives from another model on the Hub.
+
+### Fine-tune
+
+```yaml
+base_model: meta-llama/Llama-3-8B
+```
+
+### Quantized version
+
+```yaml
+base_model: meta-llama/Llama-3-8B
+base_model_relation: quantized
+```
+
+### Merge of multiple models
+
+```yaml
+base_model:
+  - model-a/name
+  - model-b/name
+base_model_relation: merge
+```
+
+### Adapter (LoRA, etc.)
+
+```yaml
+base_model: meta-llama/Llama-3-8B
+base_model_relation: adapter
+```
+
+This populates the **Model Tree** on the Hub, showing the lineage of derived
+models.
+
+---
+
+## tags
+
+Free-form tags for discoverability. Use lowercase, hyphenated strings:
+
+```yaml
+tags:
+  - medical
+  - code
+  - biology
+  - finance
+  - multilingual
+  - rlhf
+  - dpo
+  - lora
+  - chat
+```
+
+Tags are searchable on the Hub. Combine domain tags (e.g. `medical`) with
+technique tags (e.g. `lora`, `rlhf`).
+
+---
+
+## new_version
+
+Set on the **older** model's card to point users to the updated version.
+Displays a banner on the old model's page:
+
+```yaml
+new_version: org/updated-model-name
+```
+
+---
+
+## co2_eq_emissions
+
+Report carbon emissions from training:
+
+```yaml
+co2_eq_emissions:
+  emissions: 123.45
+  source: "CodeCarbon"
+  training_type: "pre-training"
+  geographical_location: "US-East"
+  hardware_used: "8xA100 GPUs"
+```
+
+All sub-fields are optional except `emissions` (in kg CO2 equivalent).
+
+---
+
+## Full Example
+
+A complete frontmatter block for a fine-tuned LLM:
+
+```yaml
+---
+pipeline_tag: text-generation
+library_name: transformers
+license: apache-2.0
+language:
+  - en
+datasets:
+  - HuggingFaceH4/ultrachat_200k
+  - HuggingFaceH4/ultrafeedback_binarized
+base_model: meta-llama/Llama-3-8B
+tags:
+  - chat
+  - rlhf
+  - dpo
+co2_eq_emissions:
+  emissions: 85.2
+  source: CodeCarbon
+  training_type: fine-tuning
+  hardware_used: 4xA100 80GB
+---
+```
diff --git a/skills/huggingface-model-release/references/model-card-template.md b/skills/huggingface-model-release/references/model-card-template.md
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+# Model Card Template
+
+Use this template when creating a README.md for a new model repo. Replace all
+bracketed placeholders. Remove sections that do not apply.
+
+---
+
+````markdown
+---
+pipeline_tag: [PIPELINE_TAG]
+library_name: [LIBRARY_NAME]
+license: [LICENSE]
+language:
+  - [LANG_CODE]
+datasets:
+  - [username/dataset-name]
+base_model: [org/base-model-name]
+tags:
+  - [tag1]
+  - [tag2]
+---
+
+# [Model Name]
+
+[One-paragraph summary: what the model does, what architecture it uses,
+and what it was trained on. Keep it concise — this is the first thing
+users see.]
+
+## Model Details
+
+- **Architecture**: [e.g. Transformer decoder, ViT-B/16, UNet]
+- **Parameters**: [e.g. 7B, 125M]
+- **Training framework**: [e.g. PyTorch, JAX]
+- **Precision**: [e.g. float16, bfloat16]
+
+## Intended Uses
+
+- [Primary intended use case]
+- [Secondary use case, if any]
+
+### Out-of-Scope Uses
+
+- [Uses the model is NOT designed for or should NOT be used for]
+
+## Usage
+
+### Installation
+
+```bash
+pip install transformers torch
+```
+
+### Inference Example
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("[org/model-name]")
+tokenizer = AutoTokenizer.from_pretrained("[org/model-name]")
+
+inputs = tokenizer("[Your prompt]", return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### Pipeline Example
+
+```python
+from transformers import pipeline
+
+pipe = pipeline("[PIPELINE_TAG]", model="[org/model-name]")
+result = pipe("[Your input]")
+print(result)
+```
+
+## Training Details
+
+### Training Data
+
+[Describe the dataset(s) used. Link to Hub datasets when possible.]
+
+### Training Procedure
+
+- **Hardware**: [e.g. 8x A100 80GB GPUs]
+- **Training time**: [e.g. 72 hours]
+- **Optimizer**: [e.g. AdamW]
+- **Learning rate**: [e.g. 2e-5]
+- **Batch size**: [e.g. 32]
+- **Epochs**: [e.g. 3]
+- **Precision**: [e.g. bfloat16 mixed precision]
+
+### Training Hyperparameters
+
+| Parameter | Value |
+|-----------|-------|
+| Learning rate | [value] |
+| Batch size | [value] |
+| Weight decay | [value] |
+| Warmup steps | [value] |
+| Max sequence length | [value] |
+
+## Evaluation
+
+### Benchmarks
+
+| Benchmark | Metric | Score |
+|-----------|--------|-------|
+| [benchmark] | [metric] | [score] |
+
+### Qualitative Examples
+
+[Include 2-3 example inputs and outputs that demonstrate the model's
+capabilities and limitations.]
+
+## Limitations and Biases
+
+- [Known limitation 1]
+- [Known limitation 2]
+- [Known bias or ethical consideration]
+
+## Environmental Impact
+
+<!-- Remove this section if not applicable -->
+
+- **Hardware used**: [e.g. 8x A100 GPUs]
+- **Training duration**: [e.g. 72 hours]
+- **Carbon emissions**: [e.g. estimated using CodeCarbon]
+
+## Citation
+
+```bibtex
+@article{[key],
+  title={[Paper Title]},
+  author={[Author, First and Author, Second]},
+  journal={arXiv preprint arXiv:[XXXX.XXXXX]},
+  year={[YEAR]}
+}
+```
+
+## References
+
+- [Paper Title](https://arxiv.org/abs/[XXXX.XXXXX])
+
+## License
+
+This model is released under the [[LICENSE_NAME]]([LICENSE_URL]) license.
+````
+
+---
+
+## Gallery Section (for image/video models)
+
+Add this section after the summary if the model generates visual outputs:
+
+````markdown
+<Gallery>
+![Example prompt 1](./images/example1.png)
+![Example prompt 2](./images/example2.png)
+![Example prompt 3](./images/example3.png)
+</Gallery>
+````
+
+## Notebook Section
+
+If including a notebook for inference or fine-tuning, add it to the repo root
+and reference it:
+
+````markdown
+## Notebooks
+
+| Notebook | Description |
+|----------|-------------|
+| [inference.ipynb](./inference.ipynb) | Run inference with this model |
+| [finetune.ipynb](./finetune.ipynb) | Fine-tune on your own data |
+````
+
+Notebooks in the repo automatically get "Open in Colab" and "Open in Kaggle"
+buttons on the model page.