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llama : add support for Nemotron 3 Super (#20411)

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* llama : add support for Nemotron 3 Super

This commit adds support for the Nemotron 3 Super model (120B.A12B)
enabling this model to be converted to GGUF format and run in llama.cpp.

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Matt Clayton <156335168+mattjcly@users.noreply.github.com>
This commit is contained in:
Daniel Bevenius
2026-03-11 19:27:53 +01:00
committed by GitHub
co-authored by Georgi Gerganov Matt Clayton
parent 76ea1c1c46
commit eaf1d7930c
11 changed files with 97 additions and 14 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/llama-arch.cpp
+8
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@@ -185,6 +185,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_EXPERT_GROUP_SCALE, "%s.expert_group_scale" },
{ LLM_KV_EXPERTS_PER_GROUP, "%s.experts_per_group" },
{ LLM_KV_MOE_EVERY_N_LAYERS, "%s.moe_every_n_layers" },
{ LLM_KV_MOE_LATENT_SIZE, "%s.moe_latent_size" },
{ LLM_KV_NEXTN_PREDICT_LAYERS, "%s.nextn_predict_layers" },
{ LLM_KV_NUM_DEEPSTACK_LAYERS, "%s.n_deepstack_layers" },
{ LLM_KV_POOLING_TYPE, "%s.pooling_type" },
@@ -365,6 +366,8 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
{ LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" },
{ LLM_TENSOR_FFN_EXP_PROBS_B, "blk.%d.exp_probs_b" },
{ LLM_TENSOR_FFN_LATENT_DOWN, "blk.%d.ffn_latent_down" },
{ LLM_TENSOR_FFN_LATENT_UP, "blk.%d.ffn_latent_up" },
{ LLM_TENSOR_ATTN_NORM_2, "blk.%d.attn_norm_2" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
@@ -1879,6 +1882,8 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
LLM_TENSOR_FFN_UP_EXPS,
LLM_TENSOR_FFN_DOWN_EXPS,
LLM_TENSOR_FFN_EXP_PROBS_B,
LLM_TENSOR_FFN_LATENT_DOWN,
LLM_TENSOR_FFN_LATENT_UP,
// MoE shared expert layer
LLM_TENSOR_FFN_DOWN_SHEXP,
LLM_TENSOR_FFN_UP_SHEXP,
@@ -2754,6 +2759,9 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
{LLM_TENSOR_NEXTN_HNORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
{LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
{LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
// Nemotron 3 Super
{LLM_TENSOR_FFN_LATENT_DOWN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_FFN_LATENT_UP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
};
LLM_KV::LLM_KV(llm_arch arch, const char * suffix) : arch(arch), suffix(suffix) {}
src/llama-arch.h
+3
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@@ -189,6 +189,7 @@ enum llm_kv {
LLM_KV_EXPERT_GROUP_SCALE,
LLM_KV_EXPERTS_PER_GROUP,
LLM_KV_MOE_EVERY_N_LAYERS,
LLM_KV_MOE_LATENT_SIZE,
LLM_KV_NEXTN_PREDICT_LAYERS,
LLM_KV_NUM_DEEPSTACK_LAYERS,
LLM_KV_POOLING_TYPE,
@@ -385,6 +386,8 @@ enum llm_tensor {
LLM_TENSOR_FFN_GATE_CHEXPS,
LLM_TENSOR_FFN_UP_CHEXPS,
LLM_TENSOR_FFN_EXP_PROBS_B,
LLM_TENSOR_FFN_LATENT_DOWN,
LLM_TENSOR_FFN_LATENT_UP,
LLM_TENSOR_ATTN_Q_NORM,
LLM_TENSOR_ATTN_K_NORM,
LLM_TENSOR_LAYER_OUT_NORM,
src/llama-hparams.h
+1
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@@ -89,6 +89,7 @@ struct llama_hparams {
bool expert_weights_norm = false;
uint32_t expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_NONE;
uint32_t moe_every_n_layers = 0;
uint32_t moe_latent_size = 0;
uint32_t nextn_predict_layers = 0;
float f_norm_eps;
src/llama-model.cpp
+9 -2
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@@ -135,6 +135,7 @@ const char * llm_type_name(llm_type type) {
case LLM_TYPE_100B_A6B: return "100B.A6B";
case LLM_TYPE_102B_A12B: return "102B.A12B";
case LLM_TYPE_106B_A12B: return "106B.A12B";
case LLM_TYPE_120B_A12B: return "120B.A12B";
case LLM_TYPE_122B_A10B: return "122B.A10B";
case LLM_TYPE_196B_A11B: return "196B.A11B";
case LLM_TYPE_230B_A10B: return "230B.A10B";
@@ -1861,10 +1862,12 @@ void llama_model::load_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
ml.get_key(LLM_KV_MOE_LATENT_SIZE, hparams.moe_latent_size, false);
switch (hparams.n_layer) {
case 52: type = LLM_TYPE_31B_A3_5B; break; // Nemotron-H_MOE 31B
case 56: type = LLM_TYPE_9B; break;
case 88: type = LLM_TYPE_120B_A12B; break;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
@@ -5544,6 +5547,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
const int64_t n_ssm_head = hparams.ssm_dt_rank;
const int64_t n_group = hparams.ssm_n_group;
const int64_t d_in_proj = 2*d_inner + 2*n_group*d_state + n_ssm_head;
const int64_t moe_n_embd = hparams.moe_latent_size > 0 ? hparams.moe_latent_size : n_embd;
// embeddings
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -5603,8 +5607,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert }, 0);
// MoE branch
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0);
layer.ffn_latent_down = create_tensor(tn(LLM_TENSOR_FFN_LATENT_DOWN, "weight", i), {n_embd, moe_n_embd}, TENSOR_NOT_REQUIRED);
layer.ffn_latent_up = create_tensor(tn(LLM_TENSOR_FFN_LATENT_UP, "weight", i), {moe_n_embd, n_embd}, TENSOR_NOT_REQUIRED);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, moe_n_embd, n_expert}, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {moe_n_embd, n_ff_exp, n_expert}, 0);
// Shared expert branch
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
src/llama-model.h
+5
View File
@@ -126,6 +126,7 @@ enum llm_type {
LLM_TYPE_100B_A6B,
LLM_TYPE_102B_A12B, // Solar-Open
LLM_TYPE_106B_A12B, // GLM-4.5-Air
LLM_TYPE_120B_A12B, // Nemotron 3 Super
LLM_TYPE_122B_A10B, // Qwen3.5
LLM_TYPE_196B_A11B, // Step3.5-Flash
LLM_TYPE_230B_A10B, // Minimax M2
@@ -294,6 +295,10 @@ struct llama_layer {
struct ggml_tensor * ffn_up_exps_b = nullptr;
struct ggml_tensor * ffn_gate_up_exps_b = nullptr;
// ff MoE latent proj
struct ggml_tensor * ffn_latent_down = nullptr;
struct ggml_tensor * ffn_latent_up = nullptr;
// ff shared expert (shexp)
struct ggml_tensor * ffn_gate_inp_shexp = nullptr;
struct ggml_tensor * ffn_gate_shexp = nullptr;
src/models/nemotron-h.cpp
+18 -4
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@@ -114,9 +114,18 @@ ggml_tensor * llm_build_nemotron_h::build_ffn_layer(ggml_tensor * cur, const lla
LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
} else {
ggml_tensor * ffn_inp = cur;
ggml_tensor * inp_emb = cur;
ggml_tensor * inp_latent = cur;
if (model.layers[il].ffn_latent_down) {
inp_latent = ggml_mul_mat(ctx0, model.layers[il].ffn_latent_down, cur);
}
ggml_tensor * router_logits = build_lora_mm(model.layers[il].ffn_gate_inp, cur);
cb(router_logits, "ffn_moe_logits", il);
ggml_tensor * moe_out =
build_moe_ffn(ffn_inp,
build_moe_ffn(inp_latent,
model.layers[il].ffn_gate_inp,
model.layers[il].ffn_up_exps,
nullptr, // no gate
@@ -126,10 +135,15 @@ ggml_tensor * llm_build_nemotron_h::build_ffn_layer(ggml_tensor * cur, const lla
LLM_FFN_RELU_SQR, hparams.expert_weights_norm,
hparams.expert_weights_scale,
LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID,
il);
il,
router_logits);
cb(moe_out, "ffn_moe_out", il);
ggml_tensor * ffn_shexp = build_ffn(ffn_inp,
if (model.layers[il].ffn_latent_up) {
moe_out = ggml_mul_mat(ctx0, model.layers[il].ffn_latent_up, moe_out);
}
ggml_tensor * ffn_shexp = build_ffn(inp_emb,
model.layers[il].ffn_up_shexp, NULL, NULL,
NULL /* no gate */ , NULL, NULL,
model.layers[il].ffn_down_shexp, NULL, NULL,