refactor(model)!: remove retired model support

.github/ISSUE_TEMPLATE/model_support_request.yml

@@ -27,9 +27,9 @@ body:
       description: Pick the closest fit for the requested model.
       options:
         - Dense decoder-only (e.g. Llama, Qwen, Gemma, Mistral, Nemotron)
-        - MoE decoder-only (e.g. DeepSeek V2/V3, OLMoE, Qwen3-MoE, MiniMax-M2)
+        - MoE decoder-only (e.g. DeepSeek V3, OLMoE, Qwen3-MoE, MiniMax-M2)
         - VLM / multimodal (e.g. Qwen-VL, Gemma-VL, Nemotron-VL)
-        - SSM / hybrid (e.g. Mamba, Nemotron-H)
+        - SSM / hybrid (e.g. Mamba, Nemotron 3)
         - Encoder-decoder (e.g. T5)
         - Other / unsure
     validations:

README.md

@@ -221,34 +221,28 @@ For a deeper dive into conversion design and advanced usage, see the [models REA
 
 Megatron Bridge provides out-of-the-box bridges and training recipes for a wide range of models, built on top of base model architectures from [Megatron Core](https://github.com/NVIDIA/Megatron-LM/tree/main/megatron/core). Refer to the [models directory](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/src/megatron/bridge/models) for the full list of model bridges.
 
-> **Deprecation notice:** Gemma 1 (2B/7B), Gemma 2 (2B/9B/27B), Llama 2,
-> Mistral 7B, Mistral Small 3 24B, Nemotron H v1 (4B/8B/47B/56B), and the
-> legacy `NemotronForCausalLM` bridge previously documented for Nemotron-4 340B
-> are no longer actively maintained or tested against current upstream
-> checkpoints. They will be removed in Megatron Bridge 0.7.0.
-
 | Family | Supported variants |
 |----------------|--------------------|
 | [**BAGEL**](docs/models/bagel/bagel.md) | BAGEL-7B-MoT (requires the tested MCore `dev` revision and `bagel` extra) |
 | [**Bailing**](docs/models/bailing/index.md) | Ling 2.0 / Ling MoE V2 (Bailing) |
 | [**BERT**](docs/models/bert/index.md) | Megatron-Style BERT (`MegatronBertForMaskedLM`) |
-| [**DeepSeek**](docs/models/deepseek/index.md) | DeepSeek V2 / V2 Lite (deprecated), DeepSeek V3, DeepSeek V4 / V4 Flash |
+| [**DeepSeek**](docs/models/deepseek/index.md) | DeepSeek V3, DeepSeek V4 / V4 Flash |
 | [**Diffusion**](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/src/megatron/bridge/diffusion/models) | FLUX, LLaDA 1.5, Nemotron-Labs Diffusion, WAN |
 | **Ernie** | [Ernie 4.5 MoE](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/src/megatron/bridge/models/ernie), [Ernie 4.5 VL MoE](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/main/src/megatron/bridge/models/ernie_vl) |
 | [**EXAONE**](docs/models/exaone/exaone.md) | EXAONE 4.0, EXAONE 4.5 VL, K-EXAONE MoE |
 | [**Falcon**](docs/models/falcon/index.md) | Falcon H1 |
-| [**Gemma**](docs/models/gemma/index.md) | Gemma / Gemma 2 (deprecated), Gemma 3, Gemma 3-VL, Gemma 4 (26B-A4B MoE / 31B dense), Gemma 4-VL (26B-A4B MoE) |
+| [**Gemma**](docs/models/gemma/index.md) | Gemma 3, Gemma 3-VL, Gemma 4 (26B-A4B MoE / 31B dense), Gemma 4-VL (26B-A4B MoE) |
 | [**GLM**](docs/models/glm/index.md) | GLM-4.5 / GLM-4.7 / GLM-4.7-Flash, GLM-4.5V, GLM-5 / GLM-5.1 / GLM-5.2 / [GLM-5.3](docs/models/glm/glm5-2.md) |
 | [**GPT-OSS**](docs/models/gpt_oss/index.md) | GPT-oss |
 | [**HY V3**](https://huggingface.co/tencent/Hy3-preview-Base) | Hy3 preview-Base (HF → Megatron checkpoint conversion) |
 | [**Kimi**](docs/models/kimi/index.md) | Kimi K2, Kimi-K2.5-VL, Kimi K3 |
-| [**Llama**](docs/models/llama/index.md) | Llama 2 (deprecated), Llama 3 / 3.1 / 3.2 / 3.3 |
+| [**Llama**](docs/models/llama/index.md) | Llama 3 / 3.1 / 3.2 / 3.3 |
 | [**MiniMax**](docs/models/minimax/index.md) | MiniMax-M2 / M2.5 / M2.7, MiniMax-M3 |
-| [**Mistral**](docs/models/mistral/index.md) | Mistral 7B / Small 3 24B (deprecated), Ministral 3 (3B/8B/14B) |
+| [**Mistral**](docs/models/mistral/index.md) | Ministral 3 (3B/8B/14B) |
 | [**Xiaomi-MiMo**](docs/models/mimo/index.md) | Xiaomi-MiMo, MiMo-V2-Flash |
 | [**Moonlight**](docs/models/moonlight/index.md) | Moonlight |
 | [**Muse Glimmer**](docs/models/muse_glimmer/muse-glimmer.md) | Muse Glimmer 30B |
-| [**Nemotron**](docs/models/nemotron/index.md) | Nemotron H v1 (deprecated), Nemotron Nano v2 (deprecated), Nemotron-3 Nano, Nemotron-3 Super, Nemotron-3 Ultra, Llama Nemotron (deprecated), Nemotron Nano v2 VL (deprecated), Nemotron-3 Nano Omni |
+| [**Nemotron**](docs/models/nemotron/index.md) | Nemotron-3 Nano, Nemotron-3 Super, Nemotron-3 Ultra, Nemotron-3 Nano Omni |
 | [**OLMoE**](docs/models/olmoe/index.md) | OLMoE |
 | [**Qwen**](docs/models/qwen/index.md) | Qwen2 / Qwen2.5, Qwen3, Qwen3-MoE, Qwen3 Next, Qwen3.5 (dense/MoE), Qwen2.5-VL, Qwen3-VL, Qwen3.5-VL, Qwen3.6-VL, Qwen2 Audio, Qwen2.5-Omni, Qwen3-Omni, Qwen3-ASR |
 | [**Sarvam**](docs/models/sarvam/index.md) | Sarvam |

docs/bridge-guide.md

@@ -63,7 +63,7 @@ from megatron.bridge import AutoBridge
 
 # Load with specific settings
 bridge = AutoBridge.from_hf_pretrained(
-    "meta-llama/Llama-2-7b-hf",
+    "meta-llama/Meta-Llama-3-8B",
     torch_dtype=torch.float16,
     device_map="auto",
     trust_remote_code=True,

docs/fern/versions/_nav_order.yml

@@ -8,20 +8,15 @@
 "./versions/nightly/pages/modelopt/quantization.mdx": 1
 "./versions/nightly/pages/models/README.mdx": 1
 "./versions/nightly/pages/models/bailing/ling-2.mdx": 1
-"./versions/nightly/pages/models/deepseek/deepseek-v2.mdx": 1
 "./versions/nightly/pages/models/falcon/falcon-h1.mdx": 1
-"./versions/nightly/pages/models/gemma/gemma.mdx": 1
 "./versions/nightly/pages/models/glm/glm45.mdx": 1
 "./versions/nightly/pages/models/gpt_oss/gpt-oss-20b.mdx": 1
 "./versions/nightly/pages/models/gpt_oss/gpt-oss-120b.mdx": 2
 "./versions/nightly/pages/models/kimi/kimi-k2.mdx": 1
-"./versions/nightly/pages/models/llama/llama2.mdx": 1
 "./versions/nightly/pages/models/mimo/mimo.mdx": 1
 "./versions/nightly/pages/models/minimax/minimax-m2.mdx": 1
 "./versions/nightly/pages/models/minimax/minimax-m3.mdx": 2
-"./versions/nightly/pages/models/mistral/mistral.mdx": 1
 "./versions/nightly/pages/models/moonlight/moonlight.mdx": 1
-"./versions/nightly/pages/models/nemotron/llama-nemotron.mdx": 1
 "./versions/nightly/pages/models/olmoe/olmoe.mdx": 1
 "./versions/nightly/pages/models/qwen/qwen3-235b-a22b.mdx": 1
 "./versions/nightly/pages/models/qwen/qwen3-30b-a3b.mdx": 2
@@ -37,12 +32,10 @@
 "./versions/nightly/pages/modelopt/distillation.mdx": 2
 "./versions/nightly/pages/models/bailing/index.mdx": 2
 "./versions/nightly/pages/models/deepseek/deepseek-v3.mdx": 2
-"./versions/nightly/pages/models/gemma/gemma2.mdx": 2
 "./versions/nightly/pages/models/glm/glm-45v.mdx": 2
 "./versions/nightly/pages/models/kimi/kimi-k25-vl.mdx": 2
 "./versions/nightly/pages/models/llama/llama3.mdx": 2
 "./versions/nightly/pages/models/mistral/ministral3.mdx": 2
-"./versions/nightly/pages/models/nemotron/nemotronh.mdx": 2
 "./versions/nightly/pages/models/qwen/qwen3-moe.mdx": 4
 "./versions/nightly/pages/performance-guide.mdx": 2
 "./versions/nightly/pages/performance-summary-archive.mdx": 2
@@ -75,8 +68,7 @@
 "./versions/nightly/pages/megatron-lm-to-megatron-bridge.mdx": 5
 "./versions/nightly/pages/models/gemma/gemma4-vl.mdx": 5
 "./versions/nightly/pages/models/gemma/index.mdx": 5
-"./versions/nightly/pages/models/nemotron/nemotron3.5-lightning.mdx": 8
-"./versions/nightly/pages/models/nemotron/nemotron-nano-v2-vl.mdx": 11
+"./versions/nightly/pages/models/nemotron/nemotron3.5-lightning.mdx": 6
 "./versions/nightly/pages/models/qwen/qwen3-vl.mdx": 7
 "./versions/nightly/pages/releases/known-issues.mdx": 5
 "./versions/nightly/pages/training/optimizer-scheduler.mdx": 5

docs/fern/versions/nightly.yml

@@ -46,8 +46,6 @@ navigation:
       path: ./nightly/pages/models/bailing/ling-2.mdx
     section: Bailing
   - contents:
-    - page: DeepSeek V2
-      path: ./nightly/pages/models/deepseek/deepseek-v2.mdx
     - page: DeepSeek V3
       path: ./nightly/pages/models/deepseek/deepseek-v3.mdx
     - page: DeepSeek V4
@@ -58,10 +56,6 @@ navigation:
       path: ./nightly/pages/models/falcon/falcon-h1.mdx
     section: Falcon
   - contents:
-    - page: Gemma
-      path: ./nightly/pages/models/gemma/gemma.mdx
-    - page: Gemma 2
-      path: ./nightly/pages/models/gemma/gemma2.mdx
     - page: Gemma 3
       path: ./nightly/pages/models/gemma/gemma3.mdx
     - page: Gemma 3 VL (Vision-Language)
@@ -96,8 +90,6 @@ navigation:
       path: ./nightly/pages/models/kimi/kimi-k3.mdx
     section: Kimi
   - contents:
-    - page: Llama 2
-      path: ./nightly/pages/models/llama/llama2.mdx
     - page: Llama 3
       path: ./nightly/pages/models/llama/llama3.mdx
     section: Llama
@@ -108,8 +100,6 @@ navigation:
       path: ./nightly/pages/models/minimax/minimax-m3.mdx
     section: MiniMax
   - contents:
-    - page: Mistral
-      path: ./nightly/pages/models/mistral/mistral.mdx
     - page: Ministral 3
       path: ./nightly/pages/models/mistral/ministral3.mdx
     section: Mistral
@@ -122,10 +112,6 @@ navigation:
       path: ./nightly/pages/models/moonlight/moonlight.mdx
     section: Moonlight
   - contents:
-    - page: Llama Nemotron
-      path: ./nightly/pages/models/nemotron/llama-nemotron.mdx
-    - page: Nemotron H and Nemotron Nano v2
-      path: ./nightly/pages/models/nemotron/nemotronh.mdx
     - page: Nemotron 3 Nano 30B-A3B
       path: ./nightly/pages/models/nemotron/nemotron3-nano.mdx
     - page: Nemotron 3 Nano 4B
@@ -139,8 +125,6 @@ navigation:
     - page: Nemotron 3.5 Lightning
       path: ./nightly/pages/models/nemotron/nemotron3.5-lightning.mdx
     # Super VL guides remain in GitHub, but are excluded from publication.
-    - page: Nemotron Nano V2 VL
-      path: ./nightly/pages/models/nemotron/nemotron-nano-v2-vl.mdx
     section: Nemotron
   - contents:
     - page: OLMoE

docs/fern/versions/nightly/pages/bridge-guide.mdx

@@ -63,7 +63,7 @@ from megatron.bridge import AutoBridge
 
 # Load with specific settings
 bridge = AutoBridge.from_hf_pretrained(
-    "meta-llama/Llama-2-7b-hf",
+    "meta-llama/Meta-Llama-3-8B",
     torch_dtype=torch.float16,
     device_map="auto",
     trust_remote_code=True,

docs/fern/versions/nightly/pages/index.mdx

@@ -206,18 +206,18 @@ Megatron Bridge provides out-of-the-box bridges and training recipes for a wide
 | Family | Supported variants |
 |----------------|--------------------|
 | [**Bailing**](docs/models/bailing/index.md) | Ling 2.0 (Bailing) |
-| [**DeepSeek**](docs/models/deepseek/index.md) | DeepSeek V2 / V2 Lite, DeepSeek V3, DeepSeek V4 |
+| [**DeepSeek**](docs/models/deepseek/index.md) | DeepSeek V3, DeepSeek V4 |
 | [**Falcon**](docs/models/falcon/index.md) | Falcon H1 |
-| [**Gemma**](docs/models/gemma/index.md) | Gemma / Gemma 2, Gemma 3, Gemma 3-VL, Gemma 4-VL (26B-A4B MoE) |
+| [**Gemma**](docs/models/gemma/index.md) | Gemma 3, Gemma 3-VL, Gemma 4-VL (26B-A4B MoE) |
 | [**GLM**](docs/models/glm/index.md) | GLM-4.5 / 4.7 / 4.7-Flash, GLM-4.5V, GLM-5 / 5.1 |
 | [**GPT-OSS**](docs/models/gpt_oss/index.md) | GPT-oss |
 | [**Kimi**](docs/models/kimi/index.md) | Kimi K2, Kimi-K2.5-VL |
-| [**Llama**](docs/models/llama/index.md) | Llama 2, Llama 3 / 3.1 / 3.2 / 3.3 |
+| [**Llama**](docs/models/llama/index.md) | Llama 3 / 3.1 / 3.2 / 3.3 |
 | [**MiniMax**](docs/models/minimax/index.md) | MiniMax-M2 / M2.5 / M2.7 |
-| [**Mistral**](docs/models/mistral/index.md) | Mistral, Ministral 3 (3B/8B/14B) |
+| [**Mistral**](docs/models/mistral/index.md) | Ministral 3 (3B/8B/14B) |
 | [**Xiaomi-MiMo**](docs/models/mimo/index.md) | Xiaomi-MiMo |
 | [**Moonlight**](docs/models/moonlight/index.md) | Moonlight |
-| [**Nemotron**](docs/models/nemotron/index.md) | Nemotron H, Nemotron Nano v2, Nemotron-3 Nano, Nemotron-3 Super, Llama Nemotron, Nemotron Nano v2 VL, Nemotron-3 Nano Omni |
+| [**Nemotron**](docs/models/nemotron/index.md) | Nemotron-3 Nano, Nemotron-3 Super, Nemotron-3 Nano Omni |
 | [**OLMoE**](docs/models/olmoe/index.md) | OLMoE |
 | [**Qwen**](docs/models/qwen/index.md) | Qwen2 / Qwen2.5, Qwen3, Qwen3-MoE, Qwen3 Next, Qwen2.5-VL, Qwen3-VL, Qwen3.5-VL, Qwen3.6-VL, Qwen2 Audio, Qwen2.5-Omni, Qwen3-Omni, Qwen3-ASR |
 | [**Sarvam**](docs/models/sarvam/index.md) | Sarvam |

docs/fern/versions/nightly/pages/modelopt/quantization.mdx

@@ -188,7 +188,6 @@ and ModelOpt scale suffixes, so a pattern can target the logical parameter name
 | Llama-3.2-1B | ✅ | ✅ |
 | Qwen3-8B | ✅ | ✅ |
 | Qwen3-30B-A3B | ✅ | ✅ |
-| Nemotron-H-8B-Base-8K | ✅ | ✅ |
 | Qwen3-VL-8B-Instruct | ✅ | ✅ |
 | Qwen3-VL-30B-A3B-Instruct | ✅ | ✅ |
 

docs/fern/versions/nightly/pages/models/deepseek/deepseek-v2.mdx

@@ -1,165 +1,7 @@
-# DeepSeek V2
+# DeepSeek V2 support removed
 
-> **Deprecation notice:** DeepSeek V2 and DeepSeek V2 Lite support is no
-> longer actively maintained or tested against current upstream checkpoints and
-> will be removed in Megatron Bridge 0.7.0.
+{/* Keep this notice for previews that use navigation from main. */}
 
-[DeepSeek-V2](https://github.com/deepseek-ai/DeepSeek-V2) is a Mixture-of-Experts (MoE) language model that uses innovative Multi-head Latent Attention (MLA) for efficient inference and DeepSeekMoE architecture for economical training and inference. The model achieves performance comparable to GPT-4 while using significantly fewer activated parameters. More information is available in the companion paper ["DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model"](https://arxiv.org/abs/2405.04434).
+Support for DeepSeek V2 and V2 Lite has been removed from Megatron Bridge. The former conversion and training instructions no longer apply.
 
-DeepSeek V2 models are supported via the Bridge system with auto-detected configuration and weight mapping.
-
-## Available Models
-
-Megatron Bridge supports the following DeepSeek V2 model variants:
-
-- **DeepSeek-V2**: 236B parameters (21B activated per token)
-- **DeepSeek-V2-Lite**: 16B parameters (2.4B activated per token)
-
-Both models support pretraining with expert parallelism for efficient MoE training.
-
-## Model Architecture Features
-
-- **Multi-head Latent Attention (MLA)**: Novel attention mechanism that reduces KV cache requirements
-- **DeepSeekMoE**: Efficient MoE architecture with routed and shared experts
-- **Expert Parallelism**: Distributes experts across GPUs for scalable training
-- **RoPE Embeddings**: Rotary position embeddings for position encoding
-- **128K Context Length**: Native support for long sequences (DeepSeek-V2)
-- **Pre-normalization**: RMSNorm before each transformer sub-layer
-
-## Conversion with 🤗 Hugging Face
-
-### Load HF → Megatron
-
-```python
-from megatron.bridge import AutoBridge
-
-# Example: DeepSeek-V2-Lite
-bridge = AutoBridge.from_hf_pretrained("deepseek-ai/DeepSeek-V2-Lite", trust_remote_code=True)
-provider = bridge.to_megatron_provider()
-
-# Optionally configure parallelism before instantiating the model
-provider.tensor_model_parallel_size = 1
-provider.pipeline_model_parallel_size = 1
-provider.expert_model_parallel_size = 8
-
-model = provider.provide_distributed_model(wrap_with_ddp=False)
-```
-
-### Import Checkpoint from HF
-
-```bash
-./scripts/conversion/convert.sh import \
-  --hf-model deepseek-ai/DeepSeek-V2-Lite \
-  --megatron-path /checkpoints/deepseek_v2_lite_megatron \
-  --trust-remote-code
-```
-
-### Export Megatron → HF
-
-```python
-from megatron.bridge import AutoBridge
-
-# Load the bridge from HF model ID
-bridge = AutoBridge.from_hf_pretrained("deepseek-ai/DeepSeek-V2-Lite", trust_remote_code=True)
-
-# Export a trained Megatron checkpoint to HF format
-bridge.export_ckpt(
-    megatron_path="/results/deepseek_v2_lite/checkpoints/iter_0000500",
-    hf_path="/exports/deepseek_v2_lite_hf",
-)
-```
-
-### Run Inference on Converted Checkpoint
-
-```bash
-uv run python examples/conversion/hf_to_megatron_generate_text.py \
-  --hf_model_path deepseek-ai/DeepSeek-V2-Lite \
-  --megatron_model_path /checkpoints/deepseek_v2_lite_megatron \
-  --prompt "What is artificial intelligence?" \
-  --max_new_tokens 100 \
-  --ep 8 \
-  --trust-remote-code
-```
-
-For more details, see [examples/conversion/hf_to_megatron_generate_text.py](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/examples/conversion/hf_to_megatron_generate_text.py)
-
-## Recipes
-
-See: [bridge.recipes.deepseek.deepseek_v2](../../apidocs/bridge/bridge.recipes.deepseek.deepseek_v2.md)
-
-### Available Recipes
-
-- **Pretrain recipes**:
-  - `deepseek_v2_lite_pretrain_config`: Pre-training for DeepSeek-V2-Lite (16B parameters, 2.4B activated per token)
-  - `deepseek_v2_pretrain_config`: Pre-training for DeepSeek-V2 (236B parameters, 21B activated per token)
-
-### Parallelism Configurations
-
-| Model | TP | PP | EP | Total GPUs | Use Case |
-|-------|----|----|----|-----------:|----------|
-| **DeepSeek-V2-Lite** | 1 | 1 | 8 | 8 | Pre-training (single node) |
-| **DeepSeek-V2** | 1 | 4 | 32 | 128 | Pre-training (16 nodes) |
-
-**Key Features**:
-- **Expert Parallelism**: EP=8 (V2-Lite) or EP=32 (V2) for efficient MoE training
-- **Selective Recomputation**: Enabled by default for memory optimization
-- **Sequence Length**: Default 4096, V2 supports up to 128K tokens
-
-### Pre-training Examples
-
-#### DeepSeek-V2-Lite (16B)
-
-```python
-from megatron.bridge.recipes.deepseek import deepseek_v2_lite_pretrain_config
-
-config = deepseek_v2_lite_pretrain_config()
-config.dataset.blend = [["/path/to/dataset_text_document"], None]
-config.checkpoint.save = "/results/deepseek_v2_lite/checkpoints"
-config.checkpoint.load = "/results/deepseek_v2_lite/checkpoints"
-config.logger.tensorboard_dir = "/results/deepseek_v2_lite/tb_logs"
-config.train.train_iters = 500_000
-config.train.global_batch_size = 512
-config.model.seq_length = 4096
-config.dataset.seq_length = 4096
-# Uses TP=1, PP=1, EP=8 (8 GPUs) automatically
-```
-
-#### DeepSeek-V2 (236B)
-
-```python
-from megatron.bridge.recipes.deepseek import deepseek_v2_pretrain_config
-
-config = deepseek_v2_pretrain_config()
-config.dataset.blend = [["/path/to/dataset_text_document"], None]
-config.checkpoint.save = "/results/deepseek_v2/checkpoints"
-config.checkpoint.load = "/results/deepseek_v2/checkpoints"
-config.logger.tensorboard_dir = "/results/deepseek_v2/tb_logs"
-config.train.train_iters = 500_000
-config.train.global_batch_size = 512
-config.model.seq_length = 4096
-config.dataset.seq_length = 4096
-# Uses TP=1, PP=4, EP=32 (128 GPUs) automatically
-```
-
-### Finetuning Recipes
-
-Finetuning recipes for DeepSeek V2 models are not currently available.
-
-## Hugging Face Model Cards & References
-
-### Hugging Face Model Cards
-- DeepSeek-V2: https://huggingface.co/deepseek-ai/DeepSeek-V2
-- DeepSeek-V2-Lite: https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite
-- DeepSeek-V2-Chat: https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat
-- DeepSeek-V2-Lite-Chat: https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat
-
-### Technical Papers
-- DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model: [arXiv:2405.04434](https://arxiv.org/abs/2405.04434)
-
-### Additional Resources
-- GitHub Repository: https://github.com/deepseek-ai/DeepSeek-V2
-
-## Related Docs
-- 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)
+See the [family overview](index.mdx) for supported models.

docs/fern/versions/nightly/pages/models/deepseek/index.mdx

@@ -4,6 +4,5 @@ DeepSeek model documentation is organized by model variant.
 
 | Variant | Guide |
 |---------|-------|
-| DeepSeek V2 | [deepseek-v2.md](deepseek-v2.md) |
 | DeepSeek V3 | [deepseek-v3.md](deepseek-v3.md) |
 | DeepSeek V4 | [deepseek-v4.md](deepseek-v4.md) |