docs(llm): drop validate-yaml reference from DeepSeek V4 Flash guide

README.md

@@ -21,6 +21,7 @@
 </div>
 
 ## 📣 News and Discussions
+- [04/25/2026][**DeepSeek V4 Flash**](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash) We now support finetuning `deepseek-ai/DeepSeek-V4-Flash`. Check out our [recipe](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/deepseek_v4/deepseek_v4_flash_hellaswag.yaml) and [guide](https://github.com/NVIDIA-NeMo/Automodel/blob/main/docs/guides/llm/dsv4-flash.md).
 - [04/22/2026][**Qwen3.6-27B**](https://huggingface.co/Qwen/Qwen3.6-27B) We now support finetuning `Qwen/Qwen3.6-27B`. Check out our [recipe](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/vlm_finetune/qwen3_5/qwen3_6_27b.yaml).
 - [04/20/2026][**Qwen-Image**](https://huggingface.co/Qwen/Qwen-Image) We now support finetuning `Qwen/Qwen-Image`, thanks to [@harshareddy832](https://github.com/harshareddy832). Check out our [recipe](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/diffusion/finetune/qwen_image_t2i_flow.yaml).
 - [04/16/2026][**Qwen3.6 MoE**](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) We now support finetuning `Qwen/Qwen3.6-35B-A3B`. Check out our [recipe](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/vlm_finetune/qwen3_5_moe/qwen3_6_35b.yaml).

docs/guides/llm/dsv4-flash.md

@@ -48,27 +48,10 @@ A new in-tree `HuggingFaceStorageReader` recognizes `F8_E8M0` and `F8_E5M2` dtyp
 
 ## Launch Training
 
-Two recipes ship under [`examples/llm_finetune/deepseek_v4/`](https://github.com/NVIDIA-NeMo/Automodel/tree/main/examples/llm_finetune/deepseek_v4):
-
-- [`deepseek_v4_flash_validate.yaml`](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/deepseek_v4/deepseek_v4_flash_validate.yaml) — single-node 8 × A100-80G infrastructure validation on a 4-layer truncated harness exercising the full attention zoo (`compress_ratios=[0, 0, 4, 128]` → SWA / SWA / CSA / HCA), `num_hash_layers=2`, `pp_size=2`, `ep_size=4`. Use this first to confirm the environment and checkpoint paths before scaling out.
-- [`deepseek_v4_flash_hellaswag.yaml`](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/deepseek_v4/deepseek_v4_flash_hellaswag.yaml) — HellaSwag finetune recipe. The yaml header documents how to scale `num_hidden_layers` and `ep_size` for the full 43-layer multi-node run.
+A ready-to-use recipe ships at [`examples/llm_finetune/deepseek_v4/deepseek_v4_flash_hellaswag.yaml`](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/deepseek_v4/deepseek_v4_flash_hellaswag.yaml). The yaml header documents how to scale `num_hidden_layers` and `ep_size` for the full 43-layer multi-node run.
 
 NeMo Automodel supports several ways to launch training — via the Automodel CLI with Slurm, interactive sessions, `torchrun`, and more. For full details on all launch options (Slurm batch jobs, multi-node configuration, environment variables, etc.), see the [Run on a Cluster](https://github.com/NVIDIA-NeMo/Automodel/blob/main/docs/launcher/slurm.md) guide.
 
-### Quick infrastructure validation (single node, 8 × A100-80G)
-
-Run the 4-layer validate harness to confirm forward / backward / PP / DCP health end-to-end before launching the full schedule:
-
-```bash
-PYTHONPATH=/path/to/Automodel:$PYTHONPATH automodel \
-  examples/llm_finetune/deepseek_v4/deepseek_v4_flash_validate.yaml \
-  --nproc-per-node 8 \
-  --model.config.pretrained_model_name_or_path=/your/local/dsv4-flash \
-  --model.config.name_or_path=/your/local/dsv4-flash
-```
-
-The validate yaml uses `num_hidden_layers=4` with `compress_ratios=[0, 0, 4, 128]`, exercising SWA / SWA / CSA / HCA on every forward, and `num_hash_layers=2` so both stage-0 layers are hash-routed.
-
 ### Standalone Slurm Script
 
 Below is a standalone Slurm script example for the HellaSwag recipe. Before running it, ensure your cluster environment is configured following the [Run on a Cluster](https://github.com/NVIDIA-NeMo/Automodel/blob/main/docs/launcher/slurm.md) guide. Then submit the job:

docs/model-coverage/llm/deepseek-ai/dsv4-flash.md

@@ -0,0 +1,91 @@
+# DeepSeek V4 Flash
+
+[DeepSeek V4 Flash](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash) is DeepSeek's latest fine-grained Mixture-of-Experts language model. It uses a 43-layer all-MoE backbone with 256 routed experts plus one shared expert per block, top-6 routing, and a hybrid per-layer attention zoo (SWA / CSA / HCA) selectable through `compress_ratios`. The first `num_hash_layers` blocks use a hash-clustering gate, and every block maintains `hc_mult=4` Hyper-Connection streams mixed via a learned col-norm-first Sinkhorn router.
+
+:::{card}
+| | |
+|---|---|
+| **Task** | Text Generation (MoE) |
+| **Architecture** | `DeepseekV4ForCausalLM` |
+| **Parameters** | fine-grained MoE, 256 routed + 1 shared expert |
+| **HF Org** | [deepseek-ai](https://huggingface.co/deepseek-ai) |
+:::
+
+## Available Models
+
+- **DeepSeek-V4-Flash**
+
+## Architecture
+
+- `DeepseekV4ForCausalLM`
+
+## Example HF Models
+
+| Model | HF ID |
+|---|---|
+| DeepSeek V4 Flash | [`deepseek-ai/DeepSeek-V4-Flash`](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash) |
+
+## Example Recipes
+
+| Recipe | Description |
+|---|---|
+| [`deepseek_v4_flash_hellaswag.yaml`](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/deepseek_v4/deepseek_v4_flash_hellaswag.yaml) | SFT — DeepSeek V4 Flash on HellaSwag with pipeline parallelism |
+
+
+## Try with NeMo AutoModel
+
+**1. Install** ([full instructions](../../../guides/installation.md)):
+
+```bash
+pip install nemo-automodel
+```
+
+**2. Clone the repo** to get the example recipes:
+
+```bash
+git clone https://github.com/NVIDIA-NeMo/Automodel.git
+cd Automodel
+```
+
+:::{note}
+The full 43-layer schedule requires a multi-node run; see the recipe yaml header for `ep_size` / `pp_size` guidance. See the [Launcher Guide](../../../launcher/slurm.md) for multi-node setup.
+:::
+
+**3. Run the recipe** from inside the repo:
+
+```bash
+automodel --nproc-per-node=8 examples/llm_finetune/deepseek_v4/deepseek_v4_flash_hellaswag.yaml
+```
+
+:::{dropdown} Run with Docker
+**1. Pull the container** and mount a checkpoint directory:
+
+```bash
+docker run --gpus all -it --rm \
+  --shm-size=8g \
+  -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
+  nvcr.io/nvidia/nemo-automodel:26.02.00
+```
+
+**2.** Navigate to the AutoModel directory (where the recipes are):
+
+```bash
+cd /opt/Automodel
+```
+
+**3. Run the recipe**:
+
+```bash
+automodel --nproc-per-node=8 examples/llm_finetune/deepseek_v4/deepseek_v4_flash_hellaswag.yaml
+```
+:::
+
+See the [Installation Guide](../../../guides/installation.md) and [LLM Fine-Tuning Guide](../../../guides/llm/finetune.md).
+
+## Fine-Tuning
+
+See the [Fine-Tune DeepSeek V4 Flash](../../../guides/llm/dsv4-flash.md) guide and the [Large MoE Fine-Tuning Guide](../../../guides/llm/large-moe-finetune.md).
+
+## Hugging Face Model Cards
+
+- [deepseek-ai/DeepSeek-V4-Flash](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash)

docs/model-coverage/llm/index.md

@@ -28,6 +28,7 @@ NeMo AutoModel supports the [AutoModelForCausalLM](https://huggingface.co/transf
 | Qwen / Alibaba Cloud | [Qwen3-Next](qwen/qwen3-next.md) | `Qwen3NextForCausalLM` |
 | DeepSeek | [DeepSeek](deepseek-ai/deepseek.md) | `DeepseekForCausalLM` |
 | DeepSeek | [DeepSeek-V3](deepseek-ai/deepseek-v3.md) | `DeepseekV3ForCausalLM`, `DeepseekV32ForCausalLM` |
+| DeepSeek | [DeepSeek V4 Flash](deepseek-ai/dsv4-flash.md) | `DeepseekV4ForCausalLM` |
 | Mistral AI | [Mistral](mistralai/mistral.md) | `MistralForCausalLM` |
 | Mistral AI | [Mixtral](mistralai/mixtral.md) | `MixtralForCausalLM` |
 | Mistral AI | [Ministral3 / Devstral](mistralai/ministral3.md) | `Mistral3ForConditionalGeneration` |
@@ -96,6 +97,7 @@ qwen/qwen3-moe
 qwen/qwen3-next
 deepseek-ai/deepseek
 deepseek-ai/deepseek-v3
+deepseek-ai/dsv4-flash
 mistralai/mistral
 mistralai/mixtral
 mistralai/ministral3