Commit 3d10bcd19 for llama.cpp
commit 3d10bcd19785c7b70626d7ded4a2276ef92bc850
Author: Alex <59368173+AlexGabbia@users.noreply.github.com>
Date: Mon Sep 14 13:04:05 2026 +0200
llama: add Maple 20B-A1B ternary MoE architecture (CPU) (#27000)
* gguf-py: add Maple tensor constants
Add MODEL_ARCH.MAPLE, its "maple" name, and the tensor list for the
Maple 20B-A1B ternary MoE architecture: token embeddings, output,
attention with Q/K RMS norms, and per-expert FFN tensors.
* convert: add Maple HF->GGUF converter
Register MapleForCausalLM in the HF architecture map and add the
converter for the Maple 20B-A1B ternary MoE model: 24 layers, 256
experts with 8 active, sliding-window attention (SWA-512) interleaved
with global attention at a 3:1 ratio, partial rotary factor 0.5, and
per-expert weight stacking into merged 3D tensors.
* llama: add Maple architecture (20B-A1B ternary MoE)
Add the Maple 20B-A1B ternary MoE architecture: 24 layers, 256
experts with 8 active, sliding-window attention (SWA-512) interleaved
with global attention at a 3:1 ratio, and ternary TQ1_0/TQ2_0
quantization support.
- register LLM_ARCH_MAPLE between MAMBA2 and JAMBA
- implement llama_model_maple: Q/K RMS norms after projection (GEMMA4
style), rope applied only on SWA layers (nope_on_global_attention),
ISWA KV cache, and MoE FFN with swiglu gate clamp at +7 (DEEPSEEK4
style)
- mark MAPLE as unsupported by the model saver (roundtrip skipped)
* tests: mark Maple as MoE-mandatory
Maple is always-MoE: the model throws when n_expert == 0, so the
test harness must only run the MoE config for LLM_ARCH_MAPLE.
* maple: apply review feedback (n_ff_exp_arr, get_arr, rope params)
- load_arch_hparams: use n_ff_exp_arr + n_ff_exp() accessor (upstream
changed these from a scalar member during the rebase)
- sliding_window_pattern: get_arr, the pattern is mandatory for this arch
- partial_rotary_factor: read only from rope_parameters (base.py mirrors
the top-level key automatically)
- document why TOKEN_EMBD/OUTPUT are forced to F16 (they are the two
dense tensors in Maple, and the reference GGUFs ship them as F16)
- add @ModelBase.example("deepgrove/maple-preview")
* tests: add Maple to the SWA pattern array list
get_arr for maple.attention.sliding_window_pattern requires an array, but
the harness only emitted a per-layer array for the arches in its list, so
test-llama-archs -a maple failed to load the model.
Assisted-by: DeepSeek Harness
* maple: move swiglu_clamp_exp to the converter
The loader prefilled 7.0 and read the key optionally. The converter now
writes it and the loader reads it as required, because llama-graph.cpp
skips the clamp when the limit is 0 and an optional read would silently
run unclamped. The test harness provides the key for the same reason.
Also drops tensor_force_quant: base.py already forces FFN_GATE_INP to F32
and TOKEN_EMBD/OUTPUT to F16 for ternary file types.
Assisted-by: DeepSeek Harness
* convert: fix the LazyBase func signature in the Maple converter
ty flagged the stack() closure: it takes no argument, while LazyBase is
annotated with func: Callable[[Any], Any]. Pass the tensor list through
args instead of closing over it, the same way kimi_k3 does, so the
callable shape matches.
Assisted-by: DeepSeek Harness
diff --git a/conversion/__init__.py b/conversion/__init__.py
index 4d58bcd10..9c5d98438 100644
--- a/conversion/__init__.py
+++ b/conversion/__init__.py
@@ -168,6 +168,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
"Mamba2ForCausalLM": "mamba",
"MambaForCausalLM": "mamba",
"MambaLMHeadModel": "mamba",
+ "MapleForCausalLM": "maple",
"MellumForCausalLM": "mellum",
"MiMoV2FlashForCausalLM": "mimo",
"MiMoV2ForCausalLM": "mimo",
diff --git a/conversion/maple.py b/conversion/maple.py
new file mode 100644
index 000000000..fb0e87804
--- /dev/null
+++ b/conversion/maple.py
@@ -0,0 +1,87 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING, cast
+
+import torch
+
+if TYPE_CHECKING:
+ from torch import Tensor
+
+from .base import LazyTorchTensor, ModelBase, TextModel, gguf
+
+
+@ModelBase.register("MapleForCausalLM")
+@ModelBase.example("deepgrove/maple-preview")
+class MapleModel(TextModel):
+ model_arch = gguf.MODEL_ARCH.MAPLE
+
+ def set_gguf_parameters(self):
+ super().set_gguf_parameters()
+ hparams = self.hparams
+
+ assert hparams["hidden_act"] == "silu"
+ assert hparams.get("num_shared_experts", 0) == 0
+ assert hparams.get("norm_topk_prob", True)
+ assert hparams.get("nope_on_global_attention", False)
+
+ head_dim = hparams.get("head_dim", hparams["hidden_size"] // hparams["num_attention_heads"])
+ partial_rotary_factor = self.rope_parameters.get("partial_rotary_factor", 1.0)
+
+ self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+ self.gguf_writer.add_rope_dimension_count(int(head_dim * partial_rotary_factor))
+ self.gguf_writer.add_sliding_window(hparams["sliding_window"])
+ self.gguf_writer.add_sliding_window_pattern([layer_type == "sliding_attention" for layer_type in hparams["layer_types"]])
+ self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
+ # the reference clamps the MoE SwiGLU gate/up at 7.0 (modeling_maple.py)
+ self.gguf_writer.add_swiglu_clamp_exp([7.0] * self.block_count)
+
+ _experts: list[dict[str, Tensor]] | None = None
+
+ @staticmethod
+ def _stack_experts(tensors: list[Tensor]) -> Tensor:
+ shape = (len(tensors), *tensors[0].shape)
+ dtype = tensors[0].dtype
+ meta = LazyTorchTensor.meta_with_dtype_and_shape(dtype, shape)
+
+ # tensors goes through args, not the closure, so that `func` matches
+ # LazyBase's single-argument shape
+ def stack(ts: list[Tensor]) -> Tensor:
+ result = torch.empty(shape, dtype=dtype)
+ for expert_id, tensor in enumerate(ts):
+ result[expert_id].copy_(LazyTorchTensor.to_eager(tensor))
+ ts.clear()
+ return result
+
+ return cast(torch.Tensor, LazyTorchTensor(meta=meta, args=(tensors,), func=stack))
+
+ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+ if "mlp.experts" in name:
+ n_experts = self.hparams["num_experts"]
+ assert bid is not None
+
+ if self._experts is None:
+ self._experts = [{} for _ in range(self.block_count)]
+
+ self._experts[bid][name] = data_torch
+
+ if len(self._experts[bid]) >= n_experts * 3:
+ for weight_name in ("down_proj", "gate_proj", "up_proj"):
+ tensors = []
+
+ for expert_id in range(n_experts):
+ expert_name = f"model.layers.{bid}.mlp.experts.{expert_id}.{weight_name}.weight"
+ tensors.append(self._experts[bid].pop(expert_name))
+
+ merged_name = f"model.layers.{bid}.mlp.experts.{weight_name}.weight"
+ yield from super().modify_tensors(self._stack_experts(tensors), merged_name, bid)
+ return
+
+ yield from super().modify_tensors(data_torch, name, bid)
+
+ def prepare_tensors(self):
+ super().prepare_tensors()
+
+ if self._experts is not None:
+ experts = [name for layer in self._experts for name in layer]
+ if experts:
+ raise ValueError(f"Unprocessed experts: {experts}")
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index d3a639f37..e54ee5a0f 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -541,6 +541,7 @@ class MODEL_ARCH(IntEnum):
ARWKV7 = auto()
MAMBA = auto()
MAMBA2 = auto()
+ MAPLE = auto()
JAMBA = auto()
XVERSE = auto()
COMMAND_R = auto()
@@ -1295,6 +1296,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.ARWKV7: "arwkv7",
MODEL_ARCH.MAMBA: "mamba",
MODEL_ARCH.MAMBA2: "mamba2",
+ MODEL_ARCH.MAPLE: "maple",
MODEL_ARCH.JAMBA: "jamba",
MODEL_ARCH.XVERSE: "xverse",
MODEL_ARCH.COMMAND_R: "command-r",
@@ -3487,6 +3489,23 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.SSM_NORM,
MODEL_TENSOR.SSM_OUT,
],
+ MODEL_ARCH.MAPLE: [
+ MODEL_TENSOR.TOKEN_EMBD,
+ MODEL_TENSOR.OUTPUT_NORM,
+ MODEL_TENSOR.OUTPUT,
+ MODEL_TENSOR.ATTN_NORM,
+ MODEL_TENSOR.ATTN_Q,
+ MODEL_TENSOR.ATTN_Q_NORM,
+ MODEL_TENSOR.ATTN_K,
+ MODEL_TENSOR.ATTN_K_NORM,
+ MODEL_TENSOR.ATTN_V,
+ MODEL_TENSOR.ATTN_OUT,
+ MODEL_TENSOR.FFN_NORM,
+ MODEL_TENSOR.FFN_GATE_INP,
+ MODEL_TENSOR.FFN_GATE_EXP,
+ MODEL_TENSOR.FFN_DOWN_EXP,
+ MODEL_TENSOR.FFN_UP_EXP,
+ ],
MODEL_ARCH.JAMBA: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index b5efb7206..0fac27efc 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -62,6 +62,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_STARCODER2, "starcoder2" },
{ LLM_ARCH_MAMBA, "mamba" },
{ LLM_ARCH_MAMBA2, "mamba2" },
+ { LLM_ARCH_MAPLE, "maple" },
{ LLM_ARCH_JAMBA, "jamba" },
{ LLM_ARCH_FALCON_H1, "falcon-h1" },
{ LLM_ARCH_XVERSE, "xverse" },
diff --git a/src/llama-arch.h b/src/llama-arch.h
index f1d173a57..6e67f5d65 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -67,6 +67,7 @@ enum llm_arch {
LLM_ARCH_STARCODER2,
LLM_ARCH_MAMBA,
LLM_ARCH_MAMBA2,
+ LLM_ARCH_MAPLE,
LLM_ARCH_JAMBA,
LLM_ARCH_FALCON_H1,
LLM_ARCH_XVERSE,
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 5855393ef..fd4290cf0 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -2225,7 +2225,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
const float limit = hparams.swiglu_clamp_exp[il];
constexpr float eps = 1e-6f;
if (limit > eps) {
- if (arch == LLM_ARCH_DEEPSEEK4 || (arch == LLM_ARCH_DFLASH && hparams.dsv4_hc_mult > 0) || arch == LLM_ARCH_HY_V4) {
+ if (arch == LLM_ARCH_MAPLE || arch == LLM_ARCH_DEEPSEEK4 || (arch == LLM_ARCH_DFLASH && hparams.dsv4_hc_mult > 0) || arch == LLM_ARCH_HY_V4) {
cur = ggml_swiglu_clamp(ctx0, cur, up, limit);
} else {
up = ggml_clamp(ctx0, up, -limit, limit);
diff --git a/src/llama-model-saver.cpp b/src/llama-model-saver.cpp
index 66f8bdec3..59a8ff84f 100644
--- a/src/llama-model-saver.cpp
+++ b/src/llama-model-saver.cpp
@@ -33,6 +33,7 @@ bool llama_model_saver_supports_arch(llm_arch arch) {
case LLM_ARCH_LAGUNA:
case LLM_ARCH_GRANITE_SWA:
case LLM_ARCH_DOTS3NOTE: // TODO: need to handle SWA pattern and MLA+SWA config
+ case LLM_ARCH_MAPLE:
return false;
default:
return true;
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index f9e9a8bcb..3b2536283 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -162,6 +162,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
return new llama_model_mamba(params);
case LLM_ARCH_MAMBA2:
return new llama_model_mamba2(params);
+ case LLM_ARCH_MAPLE:
+ return new llama_model_maple(params);
case LLM_ARCH_JAMBA:
return new llama_model_jamba(params);
case LLM_ARCH_XVERSE:
@@ -3019,6 +3021,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_SPARK2_5:
case LLM_ARCH_TALKIE:
case LLM_ARCH_MELLUM:
+ case LLM_ARCH_MAPLE:
return LLAMA_ROPE_TYPE_NEOX;
case LLM_ARCH_DFLASH:
diff --git a/src/models/maple.cpp b/src/models/maple.cpp
new file mode 100644
index 000000000..7604b7dfe
--- /dev/null
+++ b/src/models/maple.cpp
@@ -0,0 +1,150 @@
+#include "models.h"
+
+void llama_model_maple::load_arch_hparams(llama_model_loader & ml) {
+ hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
+ ml.get_key_or_arr(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_arr, hparams.n_layer_all);
+
+ ml.get_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl);
+
+ hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
+ hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
+ ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
+
+ ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP, hparams.swiglu_clamp_exp, hparams.n_layer_all);
+
+ switch (hparams.n_layer()) {
+ case 24: type = LLM_TYPE_20B; break;
+ default: type = LLM_TYPE_UNKNOWN;
+ }
+}
+
+void llama_model_maple::load_arch_tensors(llama_model_loader &) {
+ LLAMA_LOAD_LOCALS;
+
+ const int64_t n_ff_exp = hparams.n_ff_exp();
+ const int64_t head_dim = hparams.n_embd_head_k();
+
+ tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+ output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+ output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
+
+ if (n_expert == 0) {
+ throw std::runtime_error("n_expert must be > 0 for Maple");
+ }
+ if (n_expert_used == 0) {
+ throw std::runtime_error("n_expert_used must be > 0 for Maple");
+ }
+
+ for (int i = 0; i < n_layer; ++i) {
+ auto & layer = layers[i];
+
+ layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+ create_tensor_qkv(layer, i, n_embd, n_head * head_dim, n_head_kv * head_dim, n_head_kv * head_dim, 0);
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * head_dim, n_embd}, 0);
+
+ layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {head_dim}, 0);
+ layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {head_dim}, 0);
+ layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+ layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+ layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
+ 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);
+ }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_maple::build_arch_graph(const llm_graph_params & params) const {
+ return std::make_unique<graph>(*this, params);
+}
+
+llama_model_maple::graph::graph(const llama_model & model, const llm_graph_params & params) :
+ llm_graph_context(params) {
+ const int64_t n_embd_head = hparams.n_embd_head_k();
+
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_v());
+
+ ggml_tensor * inpL = build_inp_embd(model.tok_embd);
+ ggml_tensor * inp_pos = build_inp_pos();
+ auto * inp_attn = build_attn_inp_kv_iswa();
+ ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+ for (int il = 0; il < n_layer; ++il) {
+ ggml_tensor * inpSA = inpL;
+
+ ggml_tensor * cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
+ cb(cur, "attn_norm", il);
+
+ {
+ auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, n_embd_head, n_head, n_head_kv, il);
+
+ Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
+ Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
+ cb(Qcur, "Qcur_normed", il);
+ cb(Kcur, "Kcur_normed", il);
+
+ if (hparams.is_swa(il)) {
+ const int64_t n_rot_l = hparams.n_rot(il);
+ const float freq_base_l = model.get_rope_freq_base(cparams, il);
+ const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
+
+ Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot_l, rope_type, n_ctx_orig, freq_base_l,
+ freq_scale_l, ext_factor, attn_factor, beta_fast, beta_slow);
+ Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot_l, rope_type, n_ctx_orig, freq_base_l,
+ freq_scale_l, ext_factor, attn_factor, beta_fast, beta_slow);
+ }
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ cur = build_attn(inp_attn,
+ model.layers[il].wo, nullptr, model.layers[il].wo_s,
+ Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
+ cb(cur, "attn_out", il);
+ }
+
+ if (il == n_layer - 1 && inp_out_ids) {
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ cur = build_norm(ffn_inp, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = build_moe_ffn(cur,
+ model.layers[il].ffn_gate_inp,
+ model.layers[il].ffn_up_exps,
+ model.layers[il].ffn_gate_exps,
+ model.layers[il].ffn_down_exps,
+ nullptr,
+ n_expert, n_expert_used,
+ LLM_FFN_SILU, true,
+ 1.0f,
+ LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+ il);
+ cb(cur, "ffn_moe_out", il);
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = build_cvec(cur, il);
+ cb(cur, "l_out", il);
+
+ inpL = cur;
+ }
+
+ ggml_tensor * cur = build_norm(inpL, model.output_norm, nullptr, LLM_NORM_RMS, -1);
+ cb(cur, "result_norm", -1);
+ res->t_embd = cur;
+
+ cur = build_lora_mm(model.output, cur, model.output_s);
+ cb(cur, "result_output", -1);
+ res->t_logits = cur;
+
+ ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/models.h b/src/models/models.h
index 87195fddd..da519dcfd 100644
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -945,6 +945,19 @@ struct llama_model_mamba2 : public llama_model_base {
};
+struct llama_model_maple : public llama_model_base {
+ llama_model_maple(const struct llama_model_params & params) : llama_model_base(params) {}
+ void load_arch_hparams(llama_model_loader & ml) override;
+ void load_arch_tensors(llama_model_loader & ml) override;
+
+ struct graph : public llm_graph_context {
+ graph(const llama_model & model, const llm_graph_params & params);
+ };
+
+ std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
struct llama_model_jamba : public llama_model_base {
llama_model_jamba(const struct llama_model_params & params) : llama_model_base(params) {}
void load_arch_hparams(llama_model_loader & ml) override;
diff --git a/tests/test-llama-archs.cpp b/tests/test-llama-archs.cpp
index 018ff1f42..90a6a7162 100644
--- a/tests/test-llama-archs.cpp
+++ b/tests/test-llama-archs.cpp
@@ -239,7 +239,8 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
// SWA pattern: every 5th layer is full attention (matches E2B layer_types)
ms.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, uint32_t(5));
} else if (arch == LLM_ARCH_COHERE2MOE || arch == LLM_ARCH_MIMO2 || arch == LLM_ARCH_STEP35 || arch == LLM_ARCH_SPARK2_5 ||
- arch == LLM_ARCH_MUSE_GLIMMER || arch == LLM_ARCH_GRANITE_SWA || arch == LLM_ARCH_DOTS3NOTE) {
+ arch == LLM_ARCH_MUSE_GLIMMER || arch == LLM_ARCH_GRANITE_SWA || arch == LLM_ARCH_DOTS3NOTE ||
+ arch == LLM_ARCH_MAPLE) {
std::vector<uint32_t> pattern;
pattern.reserve(n_layer);
for (uint32_t il = 0; il < n_layer; il++) {
@@ -323,6 +324,11 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
ms.add_kv(LLM_KV_EXPERT_WEIGHTS_SCALE, 1.0f);
ms.add_kv(LLM_KV_EXPERT_WEIGHTS_NORM, true);
}
+
+ if (arch == LLM_ARCH_MAPLE) {
+ ms.add_kv(LLM_KV_SWIGLU_CLAMP_EXP, 7.0f);
+ }
+
ms.add_kv(LLM_KV_TOKENIZER_MODEL, "no_vocab");
// ms.add_kv(LLM_KV_DENSE_2_FEAT_OUT, n_embd);
// ms.add_kv(LLM_KV_DENSE_3_FEAT_IN, n_embd);
@@ -505,6 +511,7 @@ static bool moe_mandatory(const llm_arch arch) {
case LLM_ARCH_MISTRAL4:
case LLM_ARCH_MELLUM:
case LLM_ARCH_LAGUNA:
+ case LLM_ARCH_MAPLE:
return true;
default:
return false;