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>

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",

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"

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)

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", },

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,

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

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"          },

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,

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;
         }

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;
 

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();

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:

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

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;

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;
+}

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;

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

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: