Update Doc Refs for Ministral3

docs/models/vlm/ministral3.md

@@ -49,157 +49,9 @@ Ministral 3 combines efficient language modeling with multimodal capabilities:
 - **Multimodal Projector**: Projects vision features to language model space
 - **Flexible Image Handling**: Supports variable resolution images and multiple images per conversation
 
-## Conversion with 🤗 Hugging Face
-
-### Import HF → Megatron
-To import the HF VL model to your desired Megatron path:
-```bash
-python examples/conversion/convert_checkpoints.py import \
---hf-model mistralai/Ministral-3-3B-Base-2512 \
---megatron-path /models/ministral3-3b
-```
-
-### Export Megatron → HF
-```bash
-python examples/conversion/convert_checkpoints.py export \
---hf-model mistralai/Ministral-3-3B-Base-2512 \
---megatron-path /results/ministral3_3b/checkpoints/iter_0001000 \
---hf-path ./ministral3-hf-export
-```
-
-### Run Inference on Converted Checkpoint
-
-```bash
-python examples/conversion/hf_to_megatron_generate_vlm.py \
---hf_model_path mistralai/Ministral-3-3B-Base-2512 \
---megatron_model_path /models/ministral3-3b \
---image_path <example image path> \
---prompt "Describe this image." \
---max_new_tokens 100
-```
-
-Note:
-- `--megatron_model_path` is optional. If not specified, the script will convert the model and then run forward.
-- You can also use image URLs: `--image_path="https://example.com/image.jpg"`
-
-## Finetune Recipes
-
-- See: [bridge.recipes.ministral3](../../apidocs/bridge/bridge.recipes.ministral3.md)
-- Available recipes:
-  - `ministral3_3b_finetune_config`: Finetuning for 3B VL model with PEFT support
-  - `ministral3_8b_finetune_config`: Finetuning for 8B VL model with PEFT support
-  - `ministral3_14b_finetune_config`: Finetuning for 14B VL model with PEFT support
-
-Before training, ensure the following environment variables are set:
-1. `SAVE_DIR`: checkpoint and log saving directory
-2. `HF_TOKEN`: to download models from HF Hub (if required)
-3. `HF_HOME`: (optional) to avoid re-downloading models and datasets
-4. `WANDB_API_KEY`: (optional) to enable WandB logging
-
-### Full Finetuning
-
-```bash
-torchrun --nproc-per-node=8 examples/models/vlm/ministral3/finetune_ministral3_vl.py \
---pretrained-checkpoint /models/ministral3-3b \
---dataset-type hf \
-train.global_batch_size=32 \
-train.train_iters=1000
-```
-
-Or programmatically:
-```python
-from megatron.bridge.recipes.ministral3 import ministral3_3b_finetune_config
-
-# Full finetuning
-config = ministral3_3b_finetune_config(
-    name="ministral3_3b_full_finetune",
-    pretrained_checkpoint="/models/ministral3-3b",
-    dataset_type="hf",
-    peft=None,
-    train_iters=1000,
-    global_batch_size=32,
-)
-```
-
-### Parameter-Efficient Finetuning (PEFT) with LoRA
-
-```bash
-torchrun --nproc-per-node=8 examples/models/vlm/ministral3/finetune_ministral3_vl.py \
---pretrained-checkpoint /models/ministral3-3b \
---peft-scheme lora \
---dataset-type hf \
-train.global_batch_size=64 \
-train.train_iters=1000
-```
-
-PEFT options:
-- `--peft-scheme`: Set to `lora` for LoRA or `dora` for DoRA. Omit for full finetuning.
-
-You can also combine PEFT with freeze options:
-- `--freeze-language-model`: Freeze the language model
-- `--freeze-vision-model`: Freeze the vision encoder
-- `--freeze-vision-projection`: Freeze the vision projection layer
-
-Example with freeze options:
-```bash
-torchrun --nproc-per-node=8 examples/models/vlm/ministral3/finetune_ministral3_vl.py \
---pretrained-checkpoint /models/ministral3-3b \
---peft-scheme lora \
---freeze-vision-model \
-train.global_batch_size=64
-```
-
-Programmatic configuration:
-```python
-from megatron.bridge.recipes.ministral3 import ministral3_3b_finetune_config
-
-# LoRA finetuning
-config = ministral3_3b_finetune_config(
-    name="ministral3_3b_lora_finetune",
-    pretrained_checkpoint="/models/ministral3-3b",
-    dataset_type="hf",
-    peft="lora",  # or "dora"
-    train_iters=1000,
-    global_batch_size=64,
-)
-
-# LoRA with vision model frozen
-config = ministral3_3b_finetune_config(
-    name="ministral3_3b_lora_language_only",
-    pretrained_checkpoint="/models/ministral3-3b",
-    peft="lora",
-    freeze_vision_model=True,
-    freeze_vision_projection=True,
-)
-```
-
-### Recommended Configurations
-
-| Model | Mode | TP | PP | Global Batch Size | Learning Rate | Hardware |
-|-------|------|----|----|-------------------|---------------|----------|
-| Ministral 3 3B | Full SFT | 1 | 1 | 32-64 | 5e-6 | 8 GPUs |
-| Ministral 3 3B | LoRA/DoRA | 1 | 1 | 64-128 | 1e-4 | 8 GPUs |
-| Ministral 3 8B | Full SFT | 2 | 1 | 32-64 | 5e-6 | 8 GPUs |
-| Ministral 3 8B | LoRA/DoRA | 1 | 1 | 64-128 | 1e-4 | 8 GPUs |
-| Ministral 3 14B | Full SFT | 4 | 1 | 16-32 | 5e-6 | 8 GPUs |
-| Ministral 3 14B | LoRA/DoRA | 2 | 1 | 32-64 | 1e-4 | 8 GPUs |
-
-**Note:** LoRA/DoRA significantly reduces memory requirements, allowing for larger batch sizes and fewer GPUs.
-
-## Example Datasets
-
-| Dataset | Maker Name | Description |
-|---------|------------|-------------|
-| [cord-v2](https://huggingface.co/datasets/naver-clova-ix/cord-v2) | `make_cord_v2_dataset` | OCR receipts: Single-image-text dataset for receipt understanding |
-| [MedPix-VQA](https://huggingface.co/datasets/mmoukouba/MedPix-VQA) | `make_medpix_dataset` | Medical VQA: Single-image Q&A for clinical images |
-| [The Cauldron (Raven subset)](https://huggingface.co/datasets/HuggingFaceM4/the_cauldron) | `make_raven_dataset` | Visual reasoning: Multi-image analogical reasoning |
-
-To change the dataset, specify `dataset.maker_name=<maker_name>` in your command.
-
 ## Examples
-- Checkpoint import/export: [examples/conversion/convert_checkpoints.py](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/examples/conversion/convert_checkpoints.py)
-- Generate with VLM (HF→Megatron): [examples/conversion/hf_to_megatron_generate_vlm.py](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/examples/conversion/hf_to_megatron_generate_vlm.py)
-- Finetuning script: [examples/models/vlm/ministral3/finetune_ministral3_vl.py](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/examples/models/vlm/ministral3/finetune_ministral3_vl.py)
+
+For checkpoint conversion, inference, finetuning recipes, and step-by-step training guides, see the [Ministral 3 Examples](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/examples/models/vlm/ministral3/README.md).
 
 ## Hugging Face Model Cards
 

examples/models/vlm/ministral3/README.md

@@ -1,53 +1,8 @@
 # Ministral 3 - Vision Language Model
 
-[Mistral AI's Ministral 3](https://huggingface.co/collections/mistralai/ministral-3) is a family of edge-optimized vision-language models designed for deployment across various hardware configurations. The Ministral 3 architecture combines a powerful language model with a vision encoder for multimodal understanding.
+This directory contains example scripts for Ministral 3 vision-language models.
 
-Ministral 3 models support multimodal tasks including image captioning, visual question answering, OCR, and general vision-language understanding. Despite their compact size, these models deliver strong performance for on-device and edge deployment scenarios.
-
-Ministral family models are supported via the Bridge system with auto-detected configuration and weight mapping.
-
-```{important}
-Please upgrade to `transformers` v5 and upgrade `mistral-common` in order to use the Ministral 3 models.
-```
-
-## Available Models
-
-### Vision-Language Models
-- **Ministral 3 3B** (`mistralai/Ministral-3-3B-Base-2512`): 3.4B parameter vision-language model
-  - 26 layers, 3072 hidden size
-  - 32 attention heads, 8 query groups (GQA)
-  - Vision encoder: ~0.4B parameters
-  - Recommended: 1 node, 8 GPUs
-
-- **Ministral 3 8B** (`mistralai/Ministral-3-8B-Base-2512`): 8.4B parameter vision-language model
-  - 34 layers, 4096 hidden size
-  - 32 attention heads, 8 query groups (GQA)
-  - Vision encoder: ~0.4B parameters
-  - Recommended: 1 node, 8 GPUs
-
-- **Ministral 3 14B** (`mistralai/Ministral-3-14B-Base-2512`): ~14B parameter vision-language model
-  - 40 layers, 5120 hidden size
-  - 32 attention heads, 8 query groups (GQA)
-  - Vision encoder: ~0.4B parameters
-  - Recommended: 1 node, 8 GPUs
-
-All models support extended context lengths up to 256K tokens using YaRN RoPE scaling.
-
-## Model Architecture Features
-
-Ministral 3 combines efficient language modeling with multimodal capabilities:
-
-**Language Model Features:**
-- **YaRN RoPE Scaling**: Advanced rope scaling for extended context lengths (up to 256K tokens)
-- **Grouped Query Attention (GQA)**: Memory-efficient attention mechanism with 8 query groups
-- **SwiGLU Activation**: Gated linear units with SiLU activation for improved performance
-- **RMSNorm**: Layer normalization without mean centering for faster computation
-- **Llama 4 Attention Scaling**: Position-dependent attention scaling for improved long-context handling
-
-**Vision-Language Features:**
-- **Vision Encoder**: Pre-trained vision encoder for robust visual understanding
-- **Multimodal Projector**: Projects vision features to language model space
-- **Flexible Image Handling**: Supports variable resolution images and multiple images per conversation
+For model introduction and architecture details, see the [Ministral 3 documentation](../../../../docs/models/vlm/ministral3.md).
 
 ## Workspace Configuration
 
@@ -172,19 +127,3 @@ W&B report coming soon.
 ## Evaluation
 
 Coming soon.
-
-## Hugging Face Model Cards
-
-- Ministral 3 3B Base: https://huggingface.co/mistralai/Ministral-3-3B-Base-2512
-- Ministral 3 3B Instruct: https://huggingface.co/mistralai/Ministral-3-3B-Instruct-2512
-- Ministral 3 8B Base: https://huggingface.co/mistralai/Ministral-3-8B-Base-2512
-- Ministral 3 8B Instruct: https://huggingface.co/mistralai/Ministral-3-8B-Instruct-2512
-- Ministral 3 14B Base: https://huggingface.co/mistralai/Ministral-3-14B-Base-2512
-- Ministral 3 14B Instruct: https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512
-
-## Related Docs
-- Related LLM: [Mistral](../llm/mistral.md)
-- Recipe usage: [Recipe usage](../../recipe-usage.md)
-- Customizing the training recipe configuration: [Configuration overview](../../training/config-container-overview.md)
-- Training entry points: [Entry points](../../training/entry-points.md)
-