Commit a37cc4610 for llama.cpp
commit a37cc46104eb50900d5ab4a9ba02a0abdac039db
Author: alexey-rsi <alexey@recursive.com>
Date: Wed Oct 21 23:26:22 2026 +0200
models : add Flint model (FlintForCausalLM) (#30928)
* Add Flint model (FlintForCausalLM). Assisted by Codex
* cleanup
* one more
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
diff --git a/conversion/__init__.py b/conversion/__init__.py
index 369ce7afd..8921f53e4 100644
--- a/conversion/__init__.py
+++ b/conversion/__init__.py
@@ -20,6 +20,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
"NEOChatModel": "sensenova_u1",
"AfmoeForCausalLM": "afmoe",
"LagunaForCausalLM": "laguna",
+ "FlintForCausalLM": "flint",
"ApertusForCausalLM": "llama",
"ArceeForCausalLM": "llama",
"ArcticForCausalLM": "arctic",
diff --git a/conversion/base.py b/conversion/base.py
index 2cbaed700..f3bbe7b0f 100644
--- a/conversion/base.py
+++ b/conversion/base.py
@@ -1701,7 +1701,7 @@ class TextModel(ModelBase):
self.gguf_writer.add_expert_group_used_count(n_group_used)
logger.info(f"gguf: expert groups used count = {n_group_used}")
- if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func", "moe_router_activation", "moe_router_activation_func", "expert_selection_fn", "router_score_func"], optional=True)) is not None:
+ if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func", "moe_router_activation", "moe_router_activation_func", "expert_selection_fn", "router_score_func", "router_score_function"], optional=True)) is not None:
if score_func == "sigmoid":
self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
elif score_func == "softmax":
@@ -1939,6 +1939,9 @@ class TextModel(ModelBase):
if chkhsh == "52df12b4c8d4176e7481aab4b6e8454d1fd0a210a04a574f6d4e067d10e23c3e":
# ref: https://huggingface.co/CohereLabs/North-Mini-Code-1.0
res = "cohere2moe"
+ if chkhsh == "d5420e601e979078c670bc503d9ac411c311fa76b76a76267c31e0d6ba816637":
+ # ref: https://huggingface.co/RecursiveSI/flint
+ res = "flint"
if chkhsh == "e636dc30a262dcc0d8c323492e32ae2b70728f4df7dfe9737d9f920a282b8aea":
# ref: https://huggingface.co/Qwen/Qwen1.5-7B
res = "qwen2"
diff --git a/conversion/flint.py b/conversion/flint.py
new file mode 100644
index 000000000..a69314a9b
--- /dev/null
+++ b/conversion/flint.py
@@ -0,0 +1,38 @@
+from __future__ import annotations
+
+from .base import ModelBase, gguf
+from .qwen import Qwen2MoeModel
+
+
+@ModelBase.register("FlintForCausalLM")
+@ModelBase.example("RecursiveSI/flint")
+class FlintModel(Qwen2MoeModel):
+ model_arch = gguf.MODEL_ARCH.FLINT
+
+ def set_gguf_parameters(self):
+ super().set_gguf_parameters()
+ dense = self.hparams["mlp_only_layers"]
+ if dense != list(range(len(dense))):
+ raise ValueError("Flint dense layers must precede the MoE layers")
+ self.gguf_writer.add_leading_dense_block_count(len(dense))
+ self.gguf_writer.add_expert_shared_count(
+ self.hparams["shared_expert_intermediate_size"] // self.hparams["moe_intermediate_size"]
+ )
+ self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
+ self.gguf_writer.add_expert_weights_scale(1.0)
+ self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
+ self.gguf_writer.add_sliding_window_pattern([
+ kind == "sliding_attention" for kind in self.hparams["layer_types"]
+ ])
+ self.gguf_writer.add_rope_pattern([
+ i not in self.hparams["no_rope_layer_indices"] for i in range(self.block_count)
+ ])
+
+ def modify_tensors(self, data_torch, name, bid):
+ if name.endswith(".softmax_offset"):
+ name += ".weight"
+ if name.endswith(".gate.expert_bias"):
+ if not self.hparams["router_expert_bias"]:
+ return
+ name += ".bias"
+ yield from super().modify_tensors(data_torch, name, bid)
diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
index d3aa3c380..7d8c4fda6 100755
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -101,6 +101,7 @@ models = [
{"name": "command-r", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", },
{"name": "tiny_aya", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereLabs/tiny-aya-base", },
{"name": "cohere2moe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0", },
+ {"name": "flint", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/RecursiveSI/flint", },
{"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", },
{"name": "olmo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", },
{"name": "dbrx", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", },
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 619a15fc9..51a16ef52 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -577,6 +577,7 @@ class MODEL_ARCH(IntEnum):
SENSENOVA_U1 = auto()
QWEN3 = auto()
QWEN3MOE = auto()
+ FLINT = auto()
QWEN3NEXT = auto()
HRM_TEXT = auto()
BEAM = auto()
@@ -1394,6 +1395,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.QWEN2VL: "qwen2vl",
MODEL_ARCH.SENSENOVA_U1: "sensenova_u1",
MODEL_ARCH.QWEN3: "qwen3",
+ MODEL_ARCH.FLINT: "flint",
MODEL_ARCH.QWEN3MOE: "qwen3moe",
MODEL_ARCH.QWEN3NEXT: "qwen3next",
MODEL_ARCH.HRM_TEXT: "hrm_text",
@@ -2974,6 +2976,33 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_DOWN_EXP,
MODEL_TENSOR.FFN_UP_EXP,
],
+
+ MODEL_ARCH.FLINT: [
+ MODEL_TENSOR.TOKEN_EMBD,
+ MODEL_TENSOR.OUTPUT_NORM,
+ MODEL_TENSOR.OUTPUT,
+ MODEL_TENSOR.ATTN_NORM,
+ MODEL_TENSOR.ATTN_QKV,
+ 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_TENSOR.ATTN_SINKS,
+ MODEL_TENSOR.FFN_GATE,
+ MODEL_TENSOR.FFN_UP,
+ MODEL_TENSOR.FFN_DOWN,
+ MODEL_TENSOR.FFN_GATE_SHEXP,
+ MODEL_TENSOR.FFN_UP_SHEXP,
+ MODEL_TENSOR.FFN_DOWN_SHEXP,
+ MODEL_TENSOR.FFN_EXP_PROBS_B,
+ ],
MODEL_ARCH.QWEN3NEXT: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 3586d6528..79c9add78 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -429,6 +429,7 @@ class TensorNameMap:
),
MODEL_TENSOR.ATTN_SINKS: (
+ "model.layers.{bid}.self_attn.softmax_offset", # flint
"model.layers.{bid}.self_attn.sinks", # openai-moe
"model.layers.{bid}.self_attn.attention_sink_bias", # mimov2
"model.layers.{bid}.self_attn.learnable_sink_param", # hy-v4
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index 1c11536b5..62b012c5c 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -35,6 +35,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_QWEN2VL, "qwen2vl" },
{ LLM_ARCH_SENSENOVA_U1, "sensenova_u1" },
{ LLM_ARCH_QWEN3, "qwen3" },
+ { LLM_ARCH_FLINT, "flint" },
{ LLM_ARCH_QWEN3MOE, "qwen3moe" },
{ LLM_ARCH_QWEN3NEXT, "qwen3next" },
{ LLM_ARCH_QWEN3VL, "qwen3vl" },
diff --git a/src/llama-arch.h b/src/llama-arch.h
index afceb8ea6..6713db6b5 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -41,6 +41,7 @@ enum llm_arch {
LLM_ARCH_SENSENOVA_U1,
LLM_ARCH_QWEN3,
LLM_ARCH_QWEN3MOE,
+ LLM_ARCH_FLINT,
LLM_ARCH_QWEN3NEXT,
LLM_ARCH_QWEN3VL,
LLM_ARCH_QWEN3VLMOE,
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 8c31b5130..f19368693 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -1684,7 +1684,10 @@ llm_graph_amax_src1 llm_graph_context::build_amax_src1_f16(
constexpr float k_ln_2 = 0.6931471805599453f;
ggml_tensor * e;
- e = ggml_scale(ctx0, amax, 1.0f/32768.0f);
+ // note: this can technically be:
+ // e = ggml_scale(ctx0, amax, 1.0f/32768.0f);
+ // but we leave x32 headroom for the F16 sums of 32 values in Q8 activation blocks
+ e = ggml_scale(ctx0, amax, 1.0f/1024.0f);
e = ggml_log (ctx0, e);
e = ggml_scale(ctx0, e, k_log2_e);
e = ggml_ceil (ctx0, e);
@@ -2505,7 +2508,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
// ref: https://github.com/ggml-org/llama.cpp/pull/30656#issuecomment-6269597922
llm_graph_amax_src1 amax_src1 = { nullptr, nullptr };
- if (arch == LLM_ARCH_MISTRAL4 || arch == LLM_ARCH_MELLUM) {
+ if (arch == LLM_ARCH_MISTRAL4 || arch == LLM_ARCH_MELLUM || arch == LLM_ARCH_FLINT) {
amax_src1 = build_amax_src1_f16(cur);
cur = amax_src1.res;
}
diff --git a/src/llama-graph.h b/src/llama-graph.h
index 9c5f3aea9..a49f5432d 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -1122,7 +1122,7 @@ struct llm_graph_context {
ggml_tensor * slots = nullptr) const;
// rescale `cur` (the src1 of a matmul) by a power of two, computed from its maximum absolute
- // when the values already fit in F16, the factors are exactly 1.0f
+ // keep both individual values and Q8 activation block sums within F16 range
llm_graph_amax_src1 build_amax_src1_f16(
ggml_tensor * cur) const;
diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
index b7dfadef4..6f0e3f7f7 100644
--- a/src/llama-kv-cache.cpp
+++ b/src/llama-kv-cache.cpp
@@ -882,7 +882,10 @@ bool llama_kv_cache::update(llama_context * lctx, bool do_shift, const stream_co
LLAMA_LOG_DEBUG("%s: applying K-shift\n", __func__);
// apply K-shift if needed
- if (hparams.rope_type != LLAMA_ROPE_TYPE_NONE) {
+ if (hparams.rope_type != LLAMA_ROPE_TYPE_NONE &&
+ std::any_of(layers.begin(), layers.end(), [&](const auto & layer) {
+ return hparams.has_rope(layer.il);
+ })) {
ggml_backend_sched_reset(sched);
auto * res = lctx->get_gf_res_reserve();
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 3797c567d..60199d6d9 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -114,6 +114,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
return new llama_model_sensenova_u1(params);
case LLM_ARCH_QWEN3:
return new llama_model_qwen3(params);
+ case LLM_ARCH_FLINT:
+ return new llama_model_flint(params);
case LLM_ARCH_QWEN3MOE:
return new llama_model_qwen3moe(params);
case LLM_ARCH_QWEN3VL:
@@ -3571,6 +3573,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_QWEN2MOE:
case LLM_ARCH_QWEN3:
case LLM_ARCH_VLLM_DECISION2:
+ case LLM_ARCH_FLINT:
case LLM_ARCH_QWEN3MOE:
case LLM_ARCH_LLADA_MOE:
case LLM_ARCH_RND1:
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index a3d5dedef..05c0f96a2 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -281,6 +281,13 @@ struct llm_tokenizer_bpe : llm_tokenizer {
llm_tokenizer_bpe(const llama_vocab & vocab) {
GGML_ASSERT(vocab.get_type() == LLAMA_VOCAB_TYPE_BPE);
switch (vocab.get_pre_type()) {
+ case LLAMA_VOCAB_PRE_TYPE_FLINT:
+ regex_exprs = {
+ "\\p{N}{1,3}",
+ "[一-龥-ゟ゠-ヿ]+",
+ "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?| ?[\\p{P}\\p{S}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+ };
+ break;
case LLAMA_VOCAB_PRE_TYPE_LLAMA3:
regex_exprs = {
// original regex from tokenizer.json
@@ -2417,6 +2424,9 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
LLAMA_LOG_WARN("%s: ************************************ \n", __func__);
LLAMA_LOG_WARN("%s: \n", __func__);
pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+ } else if (tokenizer_pre == "flint") {
+ pre_type = LLAMA_VOCAB_PRE_TYPE_FLINT;
+ clean_spaces = false;
} else if (tokenizer_pre == "default") {
pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
} else if (tokenizer_pre == "minicpm5") {
@@ -3147,7 +3157,6 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
// this is currently determined based on the token text, which is obviously not ideal
// ref: https://github.com/ggml-org/llama.cpp/issues/9606
special_eog_ids.clear();
-
if (special_fim_pad_id != LLAMA_TOKEN_NULL && special_eog_ids.count(special_fim_pad_id) == 0) {
special_eog_ids.insert(special_fim_pad_id);
}
@@ -3181,6 +3190,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|| t.first == "[EOT]" // Kimi-K2
|| t.first == "[EOS]" // Kimi-K2
|| t.first == "<|end_of_text|>"
+ || (pre_type == LLAMA_VOCAB_PRE_TYPE_FLINT && (t.first == "<|user|>" || t.first == "<|observation|>"))
|| t.first == "<end_of_utterance>" // smoldocling
|| t.first == "<eos>" // gemma4
|| t.first == "<turn|>" // gemma4
diff --git a/src/llama-vocab.h b/src/llama-vocab.h
index 415acd22b..a87c0de42 100644
--- a/src/llama-vocab.h
+++ b/src/llama-vocab.h
@@ -70,6 +70,7 @@ enum llama_vocab_pre_type {
LLAMA_VOCAB_PRE_TYPE_UFAKZEKA = 59,
LLAMA_VOCAB_PRE_TYPE_MMBERT = 60,
LLAMA_VOCAB_PRE_TYPE_K2_HORIZON = 61,
+ LLAMA_VOCAB_PRE_TYPE_FLINT = 62,
};
struct LLM_KV;
diff --git a/src/models/flint.cpp b/src/models/flint.cpp
new file mode 100644
index 000000000..e7e6f6b79
--- /dev/null
+++ b/src/models/flint.cpp
@@ -0,0 +1,194 @@
+#include "models.h"
+
+void llama_model_flint::load_arch_hparams(llama_model_loader & ml) {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
+ ml.get_key_or_arr(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_arr, hparams.n_layer_all);
+ ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
+ ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
+ ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm);
+ ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
+
+ ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp);
+ ml.get_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl);
+ ml.get_arr(LLM_KV_ATTENTION_ROPE_PATTERN, hparams.rope_pattern);
+ ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
+ hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+ type = LLM_TYPE_UNKNOWN;
+}
+
+void llama_model_flint::load_arch_tensors(llama_model_loader &) {
+ LLAMA_LOAD_LOCALS;
+
+ const int64_t n_ff_exp = hparams.n_ff_exp();
+
+ 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);
+
+ 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_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+ layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+ layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+
+ layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, 0);
+
+ layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+ if (i < (int) hparams.n_layer_dense_lead) {
+ layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+ layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
+ layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
+ } else {
+ layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+ layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
+
+ if (n_expert == 0) {
+ throw std::runtime_error("n_expert must be > 0");
+ }
+ if (n_expert_used == 0) {
+ throw std::runtime_error("n_expert_used must be > 0");
+ }
+
+ // MoE branch
+ 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);
+
+ // Shared expert branch
+ layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
+ layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { hparams.n_ff_shexp, n_embd}, 0);
+ layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
+ }
+ }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_flint::build_arch_graph(const llm_graph_params & params) const {
+ return std::make_unique<graph>(*this, params);
+}
+
+llama_model_flint::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_v();
+
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+ GGML_ASSERT(n_embd_head == n_rot);
+
+ ggml_tensor * cur;
+ ggml_tensor * inpL;
+
+ inpL = build_inp_embd(model.tok_embd);
+
+ // inp_pos - contains the positions
+ 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) {
+ res->t_layer_inp[il] = inpL;
+ ggml_tensor * inpSA = inpL;
+
+ // norm
+ cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+ cb(cur, "attn_norm", il);
+
+ // self_attention
+ {
+ // compute Q and K and RoPE them
+ 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);
+ if (hparams.has_rope(il)) {
+ Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
+ freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
+ Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
+ freq_base, freq_scale, 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, model.layers[il].wo_b, model.layers[il].wo_s,
+ Qcur, Kcur, Vcur, nullptr, model.layers[il].attn_sinks, nullptr, 1.0f / sqrtf(float(n_embd_head)), 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);
+
+ // MoE branch
+ cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+ cb(cur, "ffn_norm", il);
+
+ if ((uint32_t) il < hparams.n_layer_dense_lead) {
+ cur = build_ffn(cur,
+ model.layers[il].ffn_up, NULL, model.layers[il].ffn_up_s,
+ model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_s,
+ model.layers[il].ffn_down, NULL, model.layers[il].ffn_down_s,
+ NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+ cb(cur, "ffn_out", il);
+ } else {
+ ggml_tensor * moe_out = 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,
+ model.layers[il].ffn_exp_probs_b,
+ n_expert, n_expert_used,
+ LLM_FFN_SILU, hparams.expert_weights_norm,
+ hparams.expert_weights_scale,
+ (llama_expert_gating_func_type) hparams.expert_gating_func,
+ il, nullptr, nullptr,
+ model.layers[il].ffn_up_exps_s,
+ model.layers[il].ffn_gate_exps_s,
+ model.layers[il].ffn_down_exps_s);
+ cb(moe_out, "ffn_moe_out", il);
+
+ {
+ ggml_tensor * ffn_shexp =
+ build_ffn(cur,
+ model.layers[il].ffn_up_shexp, NULL, model.layers[il].ffn_up_shexp_s,
+ model.layers[il].ffn_gate_shexp, NULL, model.layers[il].ffn_gate_shexp_s,
+ model.layers[il].ffn_down_shexp, NULL, model.layers[il].ffn_down_shexp_s,
+ NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+ cb(ffn_shexp, "ffn_shexp", il);
+
+ cur = ggml_add(ctx0, moe_out, ffn_shexp);
+ cb(cur, "ffn_out", il);
+ }
+ }
+ cur = ggml_add(ctx0, cur, ffn_inp);
+
+ cur = build_cvec(cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+ cur = inpL;
+
+ cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+ cb(cur, "result_norm", -1);
+ res->t_embd = cur;
+
+ // lm_head
+ cur = build_lora_mm(model.output, cur, model.output_s);
+
+ cb(cur, "result_output", -1);
+ res->t_logits = cur;
+}
diff --git a/src/models/models.h b/src/models/models.h
index 47b0203da..bdcc1a4fa 100644
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -693,6 +693,19 @@ struct llama_model_qwen3moe : public llama_model_base {
};
+struct llama_model_flint : public llama_model_base {
+ llama_model_flint(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_qwen3vl : public llama_model_base {
llama_model_qwen3vl(const struct llama_model_params & params) : llama_model_base(params) {}
void load_arch_hparams(llama_model_loader & ml) override;
diff --git a/tests/fusion/MTL.csv b/tests/fusion/MTL.csv
index e6cd43a82..56044b350 100644
--- a/tests/fusion/MTL.csv
+++ b/tests/fusion/MTL.csv
@@ -71,6 +71,9 @@ falcon ,0 ,any ,ADD+ADD , 2
falcon ,0 ,any ,NORM+MUL+ADD , 5
falcon-h1 ,0 ,any ,ADD+ADD , 2
falcon-h1 ,0 ,any ,RMS_NORM+MUL , 9
+flint ,0 ,any ,ADD+ADD , 1
+flint ,0 ,any ,MUL+ADD , 2
+flint ,0 ,any ,RMS_NORM+MUL , 9
gemma ,0 ,any ,RMS_NORM+MUL , 5
gemma2 ,0 ,any ,RMS_NORM+MUL , 5
gemma2 ,0 ,any ,RMS_NORM+MUL+ADD , 4
diff --git a/tests/test-llama-archs.cpp b/tests/test-llama-archs.cpp
index c86b1315a..1e0b75511 100644
--- a/tests/test-llama-archs.cpp
+++ b/tests/test-llama-archs.cpp
@@ -361,13 +361,16 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
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_MUSE_SPARK || arch == LLM_ARCH_GRANITE_SWA || arch == LLM_ARCH_DOTS3NOTE ||
- arch == LLM_ARCH_KOLIBRI1 || arch == LLM_ARCH_MAPLE) {
+ arch == LLM_ARCH_KOLIBRI1 || arch == LLM_ARCH_MAPLE || arch == LLM_ARCH_FLINT) {
std::vector<uint32_t> pattern;
pattern.reserve(n_layer);
for (uint32_t il = 0; il < n_layer; il++) {
pattern.push_back(il % 2);
}
ms.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, pattern);
+ if (arch == LLM_ARCH_FLINT) {
+ ms.add_kv(LLM_KV_ATTENTION_ROPE_PATTERN, pattern);
+ }
} else {
ms.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, uint32_t(2));
}
@@ -525,6 +528,9 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
ms.add_kv(LLM_KV_EXPERT_GATING_FUNC, gating_func); // sigmoid : softmax : sqrtsoftplus
ms.add_kv(LLM_KV_EXPERT_GROUP_SCALE, 1.0f);
ms.add_kv(LLM_KV_EXPERTS_PER_GROUP, uint32_t(1));
+ if (arch == LLM_ARCH_FLINT) {
+ ms.add_kv(LLM_KV_EXPERT_WEIGHTS_NORM, true);
+ }
if (arch == LLM_ARCH_K2_HORIZON) {
ms.add_kv(LLM_KV_ATTENTION_VALUE_EXPERT_COUNT, uint32_t(2));
ms.add_kv(LLM_KV_ATTENTION_VALUE_EXPERT_USED_COUNT, uint32_t(2));
@@ -754,6 +760,7 @@ static bool check_causal_attn_toggle(
static bool moe_mandatory(const llm_arch arch) {
switch (arch) {
+ case LLM_ARCH_FLINT:
case LLM_ARCH_LLAMA4:
case LLM_ARCH_MAION_CODER:
case LLM_ARCH_COHERE2MOE: