danielhanchen · Oct 21, 2026 · Oct 21, 2026
diff --git a/conversion/__init__.py b/conversion/__init__.py
@@ -19,6 +19,7 @@
 TEXT_MODEL_MAP: dict[str, str] = {
     "AfmoeForCausalLM": "afmoe",
     "LagunaForCausalLM": "laguna",
+    "FlintForCausalLM": "flint",
     "ApertusForCausalLM": "llama",
     "ArceeForCausalLM": "llama",
     "ArcticForCausalLM": "arctic",

diff --git a/conversion/base.py b/conversion/base.py
@@ -1701,7 +1701,7 @@ def set_gguf_parameters(self):
             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 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         if chkhsh == "52df12b4c8d4176e7481aab4b6e8454d1fd0a210a04a574f6d4e067d10e23c3e":
             # ref: https://huggingface.co/CohereLabs/North-Mini-Code-1.0
             res = "cohere2moe"
+        if chkhsh == "d5420e601e979078c670bc503d9ac411c311fa76b76a76267c31e0d6ba816637":
+            # ref: https://huggingface.co/RecursiveSI/flint-preview
+            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
@@ -0,0 +1,38 @@
+from __future__ import annotations
+
+from .base import ModelBase, gguf
+from .qwen import Qwen2MoeModel
+
+
+@ModelBase.register("FlintForCausalLM")
+@ModelBase.example("RecursiveSI/flint-preview")
+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(True)
+        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
@@ -101,6 +101,7 @@ class TOKENIZER_TYPE(IntEnum):
     {"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-preview", },
     {"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
@@ -575,6 +575,7 @@ class MODEL_ARCH(IntEnum):
     QWEN2VL          = auto()
     QWEN3            = auto()
     QWEN3MOE         = auto()
+    FLINT            = auto()
     QWEN3NEXT        = auto()
     HRM_TEXT         = auto()
     BEAM             = auto()
@@ -1391,6 +1392,7 @@ class MODEL_TENSOR(IntEnum):
     MODEL_ARCH.QWEN2MOE:         "qwen2moe",
     MODEL_ARCH.QWEN2VL:          "qwen2vl",
     MODEL_ARCH.QWEN3:            "qwen3",
+    MODEL_ARCH.FLINT:            "flint",
     MODEL_ARCH.QWEN3MOE:         "qwen3moe",
     MODEL_ARCH.QWEN3NEXT:        "qwen3next",
     MODEL_ARCH.HRM_TEXT:         "hrm_text",
@@ -2953,6 +2955,33 @@ class MODEL_TENSOR(IntEnum):
         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
@@ -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
@@ -34,6 +34,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_QWEN2MOE,         "qwen2moe"         },
     { LLM_ARCH_QWEN2VL,          "qwen2vl"          },
     { 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
@@ -40,6 +40,7 @@ enum llm_arch {
     LLM_ARCH_QWEN2VL,
     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
@@ -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
@@ -1121,7 +1121,7 @@ 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
@@ -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
@@ -112,6 +112,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
             return new llama_model_qwen2moe(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:
@@ -3569,6 +3571,7 @@ 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
@@ -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
@@ -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             = 63,
 };
 
 struct LLM_KV;