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+*This model was contributed to Hugging Face Transformers on 2025-11-29.*
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+    </div>
+</div>
+
+# AFMoE
+
+AFMoE (Arcee Foundational Mixture of Experts) is a decoder-only transformer model that extends the Llama architecture with a sparse Mixture of Experts (MoE) approach. The model combines token-choice routing with shared experts and employs several architectural innovations for efficient inference and improved performance.
+
+## Key Architecture Features
+
+AFMoE introduces several key modifications to the standard transformer architecture:
+
+- **Mixture of Experts with Shared Experts**: Combines routed experts (activated per-token via learned routing) with always-active shared experts for stable base computation
+- **Token-Choice Routing**: Uses sigmoid or softmax-based routing with normalization and scaling for expert selection
+- **Q/K Normalization and Gating**: Applies RMSNorm to query and key projections and uses sigmoid gating on attention outputs for improved stability
+- **Hybrid Attention Patterns**: Alternates between sliding window attention and full attention across layers for efficiency with long contexts
+- **Dual Normalization**: Uses pre- and post-normalization around both attention and MLP blocks for training stability
+- **Configurable Dense Layers**: Allows initial layers to use dense MLPs before transitioning to sparse MoE layers
+
+The model supports extended context lengths with RoPE embeddings and includes all standard Transformers features including Flash Attention 2, SDPA, gradient checkpointing, and quantization support.
+
+> [!TIP]
+> AFMoE is particularly well-suited for scenarios requiring efficient scaling through sparsity while maintaining strong performance. The shared experts provide a stable computation baseline while routed experts enable model capacity scaling.
+
+The example below demonstrates how to generate text with AFMoE using [`Pipeline`] or the [`AutoModel`].
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```python
+from transformers import pipeline
+
+
+pipeline = pipeline(
+    task="text-generation",
+    model="arcee-ai/Trinity-Mini",
+    device=0
+)
+
+output = pipeline("The key innovation in mixture of experts is")
+print(output[0]["generated_text"])
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```python
+import torch
+
+from transformers import AfmoeForCausalLM, AutoTokenizer
+
+
+tokenizer = AutoTokenizer.from_pretrained("arcee-ai/Trinity-Mini")
+model = AfmoeForCausalLM.from_pretrained(
+    "arcee-ai/Trinity-Mini",
+    device_map="auto"
+)
+
+inputs = tokenizer("The key innovation in mixture of experts is", return_tensors="pt").to(model.device)
+with torch.no_grad():
+    outputs = model.generate(**inputs, max_new_tokens=50)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+</hfoption>
+</hfoptions>
+
+## Model Architecture Details
+
+### Expert Routing
+
+AFMoE uses token-choice routing where each token independently selects top-k experts based on router logits. The routing mechanism includes:
+
+- Configurable scoring function (sigmoid or softmax)
+- Optional route normalization for balanced expert utilization
+- Route scaling to control expert contribution strength
+- Bias correction for expert selection
+
+### Shared Experts
+
+Unlike standard MoE models, AFMoE includes shared experts that are always activated for every token, providing:
+
+- A stable computation baseline across all tokens
+- Reduced variance in model outputs
+- Better handling of out-of-distribution inputs
+
+### Attention Mechanism
+
+The hybrid attention pattern alternates between:
+
+- **Sliding Window Attention**: For efficiency on long sequences, with configurable window size
+- **Full Attention**: Applied every N layers (configurable via `global_attn_every_n_layers`) for global context
+
+All attention layers include Q/K normalization and output gating for improved training dynamics.
+
+## AfmoeConfig
+
+[[autodoc]] AfmoeConfig
+
+## AfmoeModel
+
+[[autodoc]] AfmoeModel
+    - forward
+
+## AfmoeForCausalLM
+
+[[autodoc]] AfmoeForCausalLM
+    - forward