Vocabulary projection
×learnedself.lm_head(hidden_states)
Project each normalized feature vector into vocabulary logits.
- in
- [B batch, 1 positions, 7168 d]
- out
- [B batch, 1 positions, 163840 vocabulary]
- parameters
- 1,174,405,120
Parameters
1,174,405,120 parameters per instance.
| Component | Calculation | Parameters |
|---|---|---|
| Weight matrix | 1,174,405,120 | |
| Per-instance total | 1,174,405,120 | |
The reference module holds the weight output by input, [out, in].
Derived from the configured matrix dimensions; the model total matches the checkpoint’s tensors.
Explanation
Each entry scores one vocabulary token as the next token for its position. The softmax and the choice of token happen outside the model.
Codemodeling_kimi_linear.py 1243–1248 · config.json 18–254
def __init__(self, config):
super().__init__(config)
self.model = KimiLinearModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(
config.hidden_size, config.vocab_size, bias=False) "text_config": {
"_name_or_path": "",
"activation_situ_beta": 4.0,
"activation_situ_linear_beta": 25.0,
"add_cross_attention": false,
"architectures": [
"KimiLinearForCausalLM"
],
"attn_res_block_size": 12, "first_k_dense_replace": 1,
"forced_bos_token_id": null,
"forced_eos_token_id": null,
"hidden_act": "situ", "linear_attn_config": {
"full_attn_layers": [
4,
8,
12,
16,
20,
24,
28,
32,
36,
40,
44,
48,
52,
56,
60,
64,
68,
72,
76,
80,
84,
88,
92,
93
],
"gate_lower_bound": -5.0,
"head_dim": 128,
"kda_layers": [
1,
2,
3,
5,
6,
7,
9,
10,
11,
13,
14,
15,
17,
18,
19,
21,
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23,
25,
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53,
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59,
61,
62,
63,
65,
66,
67,
69,
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71,
73,
74,
75,
77,
78,
79,
81,
82,
83,
85,
86,
87,
89,
90,
91
],
"num_heads": 96,
"short_conv_kernel_size": 4,
"use_full_rank_gate": true
},
"max_length": 20,
"max_position_embeddings": 1048576,
"min_length": 0,
"mla_use_nope": true, "moe_intermediate_size": 3072,
"moe_layer_freq": 1,
"moe_renormalize": true,
"moe_router_activation_func": "sigmoid",
"no_repeat_ngram_size": 0,
"num_attention_heads": 96,
"num_beam_groups": 1,
"num_beams": 1,
"num_expert_group": 1,
"num_experts": 896,
"num_experts_per_token": 16,
"num_hidden_layers": 93,
"num_key_value_heads": 96,
"num_nextn_predict_layers": 0,
"num_return_sequences": 1,
"num_shared_experts": 2,
"output_attentions": false,
"output_hidden_states": false,
"output_scores": false,
"pad_token_id": 163839,
"prefix": null,
"problem_type": null,
"pruned_heads": {},
"q_lora_rank": 1536,
"qk_nope_head_dim": 128,
"qk_rope_head_dim": 64, "routed_expert_hidden_size": 3584,
"routed_scaling_factor": 1.0,
"sep_token_id": null,
"suppress_tokens": null,
"task_specific_params": null,
"temperature": 1.0,
"tf_legacy_loss": false,
"tie_encoder_decoder": false,
"tie_word_embeddings": false,Sources2
Direct evidence
The output head is a separate linear projection to the vocabulary, without bias.
head · moonshotai/Kimi-K3 / modeling_kimi_linear.py
Pinned revision f831ab668142 ↗ · lines 1243–1248
Read code excerpt
def __init__(self, config):
super().__init__(config)
self.model = KimiLinearModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(
config.hidden_size, config.vocab_size, bias=False)Direct evidence
The released text configuration: 93 layers of width 7,168, the lists of Kimi Delta Attention and full-attention layers, attention-residual blocks of 12 layers, one dense layer, 896 routed experts of which 16 are used, 2 shared experts, a routed-expert latent of 3,584 features, MLA ranks and head widths, the SiTU activation, vocabulary 163,840, untied output head.
config · moonshotai/Kimi-K3 / config.json
Pinned revision f831ab668142 ↗ · lines 18–254
Read code excerpt
"text_config": {
"_name_or_path": "",
"activation_situ_beta": 4.0,
"activation_situ_linear_beta": 25.0,
"add_cross_attention": false,
"architectures": [
"KimiLinearForCausalLM"
],
"attn_res_block_size": 12,… lines 27–45 omitted …
"first_k_dense_replace": 1,
"forced_bos_token_id": null,
"forced_eos_token_id": null,
"hidden_act": "situ",… lines 50–65 omitted …
"linear_attn_config": {
"full_attn_layers": [
4,
8,
12,
16,
20,
24,
28,
32,
36,
40,
44,
48,
52,
56,
60,
64,
68,
72,
76,
80,
84,
88,
92,
93
],
"gate_lower_bound": -5.0,
"head_dim": 128,
"kda_layers": [
1,
2,
3,
5,
6,
7,
9,
10,
11,
13,
14,
15,
17,
18,
19,
21,
22,
23,
25,
26,
27,
29,
30,
31,
33,
34,
35,
37,
38,
39,
41,
42,
43,
45,
46,
47,
49,
50,
51,
53,
54,
55,
57,
58,
59,
61,
62,
63,
65,
66,
67,
69,
70,
71,
73,
74,
75,
77,
78,
79,
81,
82,
83,
85,
86,
87,
89,
90,
91
],
"num_heads": 96,
"short_conv_kernel_size": 4,
"use_full_rank_gate": true
},
"max_length": 20,
"max_position_embeddings": 1048576,
"min_length": 0,
"mla_use_nope": true,… lines 174–175 omitted …
"moe_intermediate_size": 3072,
"moe_layer_freq": 1,
"moe_renormalize": true,
"moe_router_activation_func": "sigmoid",
"no_repeat_ngram_size": 0,
"num_attention_heads": 96,
"num_beam_groups": 1,
"num_beams": 1,
"num_expert_group": 1,
"num_experts": 896,
"num_experts_per_token": 16,
"num_hidden_layers": 93,
"num_key_value_heads": 96,
"num_nextn_predict_layers": 0,
"num_return_sequences": 1,
"num_shared_experts": 2,
"output_attentions": false,
"output_hidden_states": false,
"output_scores": false,
"pad_token_id": 163839,
"prefix": null,
"problem_type": null,
"pruned_heads": {},
"q_lora_rank": 1536,
"qk_nope_head_dim": 128,
"qk_rope_head_dim": 64,… lines 202–245 omitted …
"routed_expert_hidden_size": 3584,
"routed_scaling_factor": 1.0,
"sep_token_id": null,
"suppress_tokens": null,
"task_specific_params": null,
"temperature": 1.0,
"tf_legacy_loss": false,
"tie_encoder_decoder": false,
"tie_word_embeddings": false,