Add Archon Model

Action: Fetch and analyze the model's HuggingFace configuration and modeling files.

1. Read the model's config.json (via AutoConfig.from_pretrained) to identify:

   • model_type string (this is the key used for registry lookup)
   • All architecture hyperparameters (hidden_size, num_layers, etc.)
   • Any model-specific fields (e.g., qk_norm, attention_bias, MoE fields)

2. Read the HuggingFace modeling_*.py source to identify:

   • Attention variant: Does it have Q/K norm? Attention bias? Sliding window? Multi-latent attention?
   • FFN variant: SwiGLU (gate_proj + up_proj + down_proj)? GeGLU? Standard MLP?
   • MoE support: Does it have MoE layers? What router type? Shared experts?
   • RoPE variant: Standard RoPE? YaRN? NTK-aware scaling? What is the inv_freq formula?
   • Normalization: RMSNorm or LayerNorm? Pre-norm or post-norm? Elementwise affine?
   • Weight tying: Does tie_word_embeddings appear in config?
   • State dict key names: What are the HF weight key naming conventions?

3. Summarize findings in a checklist like:

Target model: <name>
HF model_type: "<model_type>" (and variants like "<model_type>_moe" if applicable)
Attention: [standard GQA / with QK norm / with bias / sliding window / ...]
FFN: [SwiGLU / GeGLU / standard MLP / ...]
MoE: [no / yes - num_experts, top_k, shared_experts]
RoPE: [standard / YaRN / NTK-aware / ...]
Norm: [RMSNorm / LayerNorm] with [pre-norm / post-norm]
Weight tying: [yes / no]

Step 2: Select the Reference Model

Choose the closest existing implementation as a starting point:

Action: Copy the reference model directory as the starting point:

areal/experimental/models/archon/<model>/
  __init__.py
  spec.py
  model/
    args.py
    model.py
    rope.py
    state_dict_adapter.py
  infra/
    parallelize.py

Step 3: Implement args.py

Adapt <Model>ModelArgs to match the target model's HuggingFace config fields.

Key changes from reference:

1. Update the @dataclass fields to match the target model's hyperparameters:

   • Field names should use Archon conventions (dim, n_layers, n_heads, n_kv_heads, vocab_size, head_dim, hidden_dim, norm_eps, rope_theta, etc.)
   • Default values should match the smallest variant of the target model
   • Add model-specific fields (e.g., attention_bias, qk_norm, sliding_window)

2. Update from_hf_config() to correctly map HuggingFace config attributes:

   • Use getattr(hf_config, "field_name", default) for optional fields
   • Handle variant-specific fields (e.g., MoE fields only present in MoE variants)
   • The method must return an instance of the model args class

Critical: Verify every field mapping against the HF model's config.json. Incorrect mappings here cause silent errors downstream.

Base class contract (BaseModelArgs):

@dataclass
class <Model>ModelArgs(BaseModelArgs):
    # ... model-specific fields ...

    @classmethod
    def from_hf_config(
        cls,
        hf_config: PretrainedConfig,
        is_critic: bool = False,
        **kwargs,
    ) -> <Model>ModelArgs:
        # Map HF config fields to Archon model args
        ...

Step 4: Implement model.py

Adapt the model architecture to match the target model.

Key components to adapt:

1. Normalization (RMSNorm or similar):

   • Check if elementwise_affine is configurable
   • Check the epsilon default value
   • If the model uses LayerNorm, implement accordingly

2. Attention module:

   • Q/K/V projection: Check bias presence (nn.Linear(..., bias=True/False))
   • QK norm: Add q_norm/k_norm if the model has them, remove if it doesn't
   • GQA: n_kv_heads < n_heads for grouped-query attention
   • Ulysses SP: Keep the set_cp_group / _sp_enabled pattern from the reference
   • Output projection: Check bias presence

3. FeedForward module:

   • SwiGLU: w2(silu(w1(x)) * w3(x)) -- most common for modern LLMs
   • Check bias in linear layers
   • For MoE models: MoE module replaces FeedForward on designated layers

4. TransformerBlock: Pre-norm (most modern LLMs) vs post-norm

   • MoE layer detection via _is_moe_layer() if applicable

5. Top-level Model (<Model>Model(BaseArchonModel)):

   • tok_embeddings, layers (as ModuleDict), norm, output/score
   • init_weights(): Match initialization scheme from HF
   • init_buffers(): RoPE cache + MoE buffers
   • forward(): Must follow BaseArchonModel signature: (tokens, positions, cu_seqlens, max_seqlen, tree_attn_meta=None) -> Tensor

Base class contract (BaseArchonModel):

class <Model>Model(BaseArchonModel):
    def forward(self, tokens, positions, cu_seqlens, max_seqlen, tree_attn_meta=None) -> torch.Tensor: ...
    def init_weights(self) -> None: ...
    def init_buffers(self, buffer_device) -> None: ...

Step 5: Implement rope.py

Handle the rotary position embedding variant.

Options:

1. Standard RoPE (same as qwen2/qwen3): Re-export from qwen2:

   from areal.experimental.models.archon.qwen2.model.rope import (
       apply_rotary_emb,
       precompute_rope_cache,
       repeat_kv,
       reshape_for_broadcast,
       rotate_half,
   )
   

2. Custom RoPE (YaRN, NTK-aware, etc.): Implement custom precompute_rope_cache() and apply_rotary_emb() functions. The key difference is usually in how inv_freq is computed (scaling factors, interpolation, etc.).

Step 6: Implement state_dict_adapter.py

Map between HuggingFace and Archon weight key names.

This is the most error-prone step. The adapter must correctly handle:

1. Key name mapping (from_hf_map dict):

   • Embedding: model.embed_tokens.weight -> tok_embeddings.weight
   • Attention: model.layers.{}.self_attn.q_proj.weight -> layers.{}.attention.wq.weight
   • FFN: model.layers.{}.mlp.gate_proj.weight -> layers.{}.feed_forward.w1.weight
   • Norms: model.layers.{}.input_layernorm.weight -> layers.{}.attention_norm.weight
   • Output: lm_head.weight -> output.weight
   • Skip keys (set to None): rotary_emb.inv_freq (computed at runtime)
   • Model-specific keys: bias terms, QK norm weights, etc.

2. Reverse mapping (to_hf_map): Auto-generated from from_hf_map

3. MoE expert weights (if applicable): 3D<->2D conversion for expert weights. Copy the MoE handling from qwen3 if the model has MoE.

4. Weight tying: Skip output.weight during to_hf() if tie_word_embeddings=True

Verification approach: After implementation, the adapter should satisfy:

# Roundtrip: archon -> hf -> archon preserves all keys
hf_sd = adapter.to_hf(archon_sd)
roundtrip_sd = adapter.from_hf(hf_sd)
assert set(roundtrip_sd.keys()) == set(archon_sd.keys())

Base class contract (BaseStateDictAdapter):

class <Model>StateDictAdapter(BaseStateDictAdapter):
    def from_hf(self, hf_state_dict) -> dict[str, Any]: ...
    def to_hf(self, archon_state_dict) -> dict[str, Any]: ...
    def convert_single_to_hf(self, name, tensor) -> list[tuple[str, torch.Tensor]]: ...

Step 7: Implement parallelize.py

Define the parallelization strategy for the model.

The parallelize function applies parallelism in this order:

1. TP (Tensor Parallelism) -- shard attention/FFN across devices
2. EP (Expert Parallelism) -- for MoE models only
3. CP (Context Parallelism / Ulysses SP) -- sequence parallelism
4. AC (Activation Checkpointing) -- memory optimization
5. torch.compile -- compilation optimization
6. FSDP (Fully Sharded Data Parallelism) -- data parallelism

Key adaptations by model architecture:

• Attention with QK norm: wq/wk use use_local_output=False (DTensor output for norm), add SequenceParallel(sequence_dim=2) for q_norm/k_norm
• Attention without QK norm: wq/wk/wv all use use_local_output=True
• Attention with bias: Bias terms follow the same parallel plan as their weights
• MoE layers: Separate TP plan for MoE input/output, router gate, and expert weights. Copy from qwen3's apply_moe_ep_tp() and apply_non_moe_tp()
• Dense-only models: Simpler plan without MoE handling. Copy from qwen2

Function signature (must match ParallelizeFn protocol):

def parallelize_<model>(
    model: nn.Module,
    parallel_dims: ArchonParallelDims,
    param_dtype: torch.dtype = torch.bfloat16,
    reduce_dtype: torch.dtype = torch.float32,
    loss_parallel: bool = True,
    cpu_offload: bool = False,
    reshard_after_forward_policy: str = "default",
    ac_config: ActivationCheckpointConfig | None = None,
    enable_compile: bool = True,
) -> nn.Module:

Step 8: Create spec.py and Register

Assemble the ModelSpec and register it.

from areal.experimental.models.archon.model_spec import ModelSpec, register_model_spec
from areal.experimental.models.archon.pipeline_parallel import pipeline_llm
from areal.experimental.models.archon.<model>.infra.parallelize import parallelize_<model>
from areal.experimental.models.archon.<model>.model.args import <Model>ModelArgs
from areal.experimental.models.archon.<model>.model.model import <Model>Model
from areal.experimental.models.archon.<model>.model.state_dict_adapter import (
    <Model>StateDictAdapter,
)

<MODEL>_SPEC = ModelSpec(
    name="<Model>",
    model_class=<Model>Model,
    model_args_class=<Model>ModelArgs,
    state_dict_adapter_class=<Model>StateDictAdapter,
    parallelize_fn=parallelize_<model>,
    supported_model_types=frozenset({"<model_type>"}),  # From HF config.json
    pipelining_fn=pipeline_llm,
)

# Auto-register when module is imported
register_model_spec(<MODEL>_SPEC)

__all__ = ["<MODEL>_SPEC"]

Note: supported_model_types should include all HF model_type strings that this implementation handles (e.g., {"qwen3", "qwen3_moe"} for Qwen3).

Step 9: Register in __init__.py

Add the import to areal/experimental/models/archon/__init__.py:

from areal.experimental.models.archon.<model> import spec as <model>_spec  # noqa: F401

This triggers auto-registration when the module is imported.

Step 10: Verify and Test

Verification should be done in stages, adapting based on available hardware and the test patterns in tests/experimental/archon/.

Before writing tests, examine the existing test files to understand current