DeepSeek-V3.2-Exp · Embedded state

Embedded state

tensorh · 904

Learned token vectors entering the first block.

shape: $x_0$ [B batch, T positions, 7168 d]

Sources

Contextual evidence from Token embedding

The token embedding is a learned table of vocabulary × width rows, looked up by token ID.

embedding · deepseek-ai/DeepSeek-V3.2-Exp / inference/model.py
Pinned revision 87e509a2e5a1 ↗ · lines 108–127

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        self.weight = nn.Parameter(torch.empty(self.part_vocab_size, self.dim))

… lines 109–126 omitted …

        y = F.embedding(x, self.weight)

Contextual evidence from Token embedding

The released 671B configuration: width, layers, dense layers, heads, experts and routing, MLA ranks and head widths, indexer heads and top-k.

config · deepseek-ai/DeepSeek-V3.2-Exp / inference/config_671B_v3.2.json
Pinned revision 87e509a2e5a1 ↗ · lines 1–26

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{
    "vocab_size": 129280,
    "dim": 7168,
    "inter_dim": 18432,
    "moe_inter_dim": 2048,
    "n_layers": 61,
    "n_dense_layers": 3,
    "n_heads": 128,
    "n_routed_experts": 256,
    "n_shared_experts": 1,
    "n_activated_experts": 8,
    "n_expert_groups": 8,
    "n_limited_groups": 4,
    "route_scale": 2.5,
    "score_func": "sigmoid",
    "q_lora_rank": 1536,
    "kv_lora_rank": 512,
    "qk_nope_head_dim": 128,
    "qk_rope_head_dim": 64,
    "v_head_dim": 128,
    "dtype": "fp8",
    "scale_fmt": "ue8m0",
    "index_n_heads": 64,
    "index_head_dim": 128,
    "index_topk": 2048
}

Contextual evidence from Transformer blocks

The model embeds tokens, applies all blocks with a causal mask, normalizes and projects to vocabulary logits with an untied head. The reference generator projects only the last position; training projects every position.

transformer · deepseek-ai/DeepSeek-V3.2-Exp / inference/model.py
Pinned revision 87e509a2e5a1 ↗ · lines 880–908

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        self.embed = ParallelEmbedding(args.vocab_size, args.dim)
        self.layers = torch.nn.ModuleList()
        for layer_id in range(args.n_layers):
            self.layers.append(Block(layer_id, args))
        self.norm = RMSNorm(args.dim)
        # lm_head in the checkpoint is stored in bf16, while the parameter here is stored in fp32 for easier computation of logits later.
        self.head = ColumnParallelLinear(args.dim, args.vocab_size, dtype=torch.float32)
        self.register_buffer("freqs_cis", precompute_freqs_cis(args), persistent=False)

… lines 888–900 omitted …

        seqlen = tokens.size(1)
        freqs_cis = self.freqs_cis[start_pos:start_pos+seqlen]
        mask = torch.full((seqlen, seqlen), float("-inf"), device=tokens.device).triu_(1) if seqlen > 1 else None
        h, residual = self.embed(tokens), None
        for layer in self.layers:
            h, residual = layer(h, residual, start_pos, freqs_cis, mask)
        h, _ = self.norm(h, residual)
        logits = self.head(h[:, -1].float())

Contextual evidence from Transformer blocks

A block applies attention-norm, MLA, ffn-norm and the FFN; the first n_dense_layers blocks use the dense MLP, the rest MoE. Residual additions happen inside the next normalization.

block · deepseek-ai/DeepSeek-V3.2-Exp / inference/model.py
Pinned revision 87e509a2e5a1 ↗ · lines 826–851

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        self.attn = MLA(args)
        self.ffn = MLP(args.dim, args.inter_dim) if layer_id < args.n_dense_layers else MoE(args)
        self.attn_norm = RMSNorm(args.dim)
        self.ffn_norm = RMSNorm(args.dim)

… lines 830–843 omitted …

        if residual is None:
            x, residual = self.attn_norm(x), x
        else:
            x, residual = self.attn_norm(x, residual)
        x = self.attn(x, start_pos, freqs_cis, mask)
        x, residual = self.ffn_norm(x, residual)
        x = self.ffn(x)
        return x, residual