Merge 89ee8644a2 into b83111815e

* Support Step3.5-Flash

* fix: norm.weight + 1 (HF zero_centered=true)

* step35: simplify GGUF conversion + drop redundant rope KVs

* Address review feedback

* rename limits -> clamp

* Apply suggestions from code review

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Apply suggestion from @CISC

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* rename swiglu limits -> swiglu clamp in LLM_KV

* avoid CI fail

* Apply suggestions from code review

* Apply suggestions from code review

* disabled KV shifting for LLM_ARCH_STEP35

* Apply suggestions from code review

* mistakenly removed cmath

* add model size && apply missed suggestion

* assert partial_rotary_factors

* fix CI errors:

* load freq_base_swa

---------

Co-authored-by: lvyichen <lvyichen@stepfun.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

common/speculative.cpp

@@ -805,6 +805,42 @@ enum common_speculative_type common_speculative_type_from_name(const std::string
     return it->second;
 }
 
+bool common_speculative_is_compat(llama_context * ctx_tgt) {
+    auto * mem = llama_get_memory(ctx_tgt);
+    if (mem == nullptr) {
+        return false;
+    }
+
+    bool res = true;
+
+    llama_memory_clear(mem, true);
+
+    // eval 2 tokens to check if the context is compatible
+    std::vector<llama_token> tmp;
+    tmp.push_back(0);
+    tmp.push_back(0);
+
+    int ret = llama_decode(ctx_tgt, llama_batch_get_one(tmp.data(), tmp.size()));
+    if (ret != 0) {
+        LOG_ERR("%s: llama_decode() failed: %d\n", __func__, ret);
+        res = false;
+        goto done;
+    }
+
+    // try to remove the last tokens
+    if (!llama_memory_seq_rm(mem, 0, 1, -1)) {
+        LOG_WRN("%s: the target context does not support partial sequence removal\n", __func__);
+        res = false;
+        goto done;
+    }
+
+done:
+    llama_memory_clear(mem, true);
+    llama_synchronize(ctx_tgt);
+
+    return res;
+}
+
 // initialization of the speculative decoding system
 //
 common_speculative * common_speculative_init(

common/speculative.h

@@ -14,6 +14,10 @@ enum common_speculative_type common_speculative_type_from_name(const std::string
 // convert type to string
 std::string common_speculative_type_to_str(enum common_speculative_type type);
 
+// check if the llama_context is compatible for speculative decoding
+// note: clears the memory of the context
+bool common_speculative_is_compat(llama_context * ctx_tgt);
+
 common_speculative * common_speculative_init(
         common_params_speculative & params,
         llama_context             * ctx_tgt);

convert_hf_to_gguf.py

@@ -920,7 +920,7 @@ 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_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"], optional=True)) is not None:
             if score_func == "sigmoid":
                 self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
             elif score_func == "softmax":
@@ -7912,6 +7912,135 @@ class MimoV2Model(TextModel):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
+@ModelBase.register("Step3p5ForCausalLM")
+class Step35Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.STEP35
+
+    def set_gguf_parameters(self):
+        rope_theta = self.hparams.get("rope_theta")
+        if isinstance(rope_theta, list):
+            self.hparams["rope_theta"] = float(rope_theta[0])
+            self.hparams["local_rope_theta"] = float(rope_theta[1])
+            self.rope_parameters["rope_theta"] = self.hparams["rope_theta"]
+            self.rope_parameters["sliding_attention"] = {"rope_theta": self.hparams["local_rope_theta"]}
+
+        super().set_gguf_parameters()
+
+        layer_types = self.hparams.get("layer_types") or []
+        partial_rotary_factors = self.hparams.get("partial_rotary_factors") or []
+        attn_other = self.hparams.get("attention_other_setting") or {}
+
+        n_head_base = self.hparams["num_attention_heads"]
+        n_kv_base = self.hparams["num_attention_groups"]
+
+        n_head_swa = attn_other.get("num_attention_heads", n_head_base)
+        n_kv_swa = attn_other.get("num_attention_groups", n_kv_base)
+
+        layer_types = layer_types[: self.block_count]
+        partial_rotary_factors = partial_rotary_factors[: self.block_count]
+        assert [1.0 if lt == "sliding_attention" else 0.5 for lt in layer_types] == partial_rotary_factors
+        head_arr = [n_head_swa if lt == "sliding_attention" else n_head_base for lt in layer_types]
+        kv_arr = [n_kv_swa if lt == "sliding_attention" else n_kv_base for lt in layer_types]
+        swa_pat = [lt == "sliding_attention" for lt in layer_types]
+
+        self.gguf_writer.add_head_count(head_arr)
+        self.gguf_writer.add_head_count_kv(kv_arr)
+
+        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
+        self.gguf_writer.add_sliding_window_pattern(swa_pat)
+
+        self.gguf_writer.add_value_length(self.hparams["head_dim"])
+
+        # MoE params
+        self.gguf_writer.add_expert_count(self.hparams["moe_num_experts"])
+        self.gguf_writer.add_expert_used_count(self.hparams["moe_top_k"])
+        self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
+        self.gguf_writer.add_expert_shared_feed_forward_length(self.hparams["share_expert_dim"])
+
+        if (moe_router_scaling_factor := self.hparams.get("moe_router_scaling_factor")) is not None:
+            self.gguf_writer.add_expert_weights_scale(moe_router_scaling_factor)
+        if (norm_expert_weight := self.hparams.get("norm_expert_weight")) is not None:
+            self.gguf_writer.add_expert_weights_norm(norm_expert_weight)
+
+        # leading dense blocks
+        leading_dense = 0
+        moe_layers_enum = self.hparams.get("moe_layers_enum")
+        if isinstance(moe_layers_enum, str) and moe_layers_enum.strip():
+            moe_layers = sorted(int(i) for i in moe_layers_enum.strip().split(","))
+            if moe_layers:
+                leading_dense = max(0, moe_layers[0])
+        self.gguf_writer.add_leading_dense_block_count(leading_dense)
+        self.gguf_writer.add_moe_every_n_layers(int(self.hparams.get("moe_every_n_layer", 1)))
+
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("rms_norm_eps", 1e-5))
+
+        # Optional per-layer SwiGLU clamps.
+        if (limits := self.hparams.get("swiglu_limits")) is not None:
+            limits_f = [0.0 if v is None else float(v) for v in limits[: self.block_count]]
+            self.gguf_writer.add_swiglu_clamp_exp(limits_f)
+        if (limits_shared := self.hparams.get("swiglu_limits_shared")) is not None:
+            limits_shared_f = [0.0 if v is None else float(v) for v in limits_shared[: self.block_count]]
+            self.gguf_writer.add_swiglu_clamp_shexp(limits_shared_f)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
+        # remove mtp layers
+        if (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None:
+            il = int(m.group(1))
+            n_main = int(self.hparams.get("num_hidden_layers", self.block_count))
+            if il >= n_main:
+                return
+        if name.endswith("norm.weight"):
+            data_torch += 1.0
+        # Map router bias (expert selection bias) to a GGUF bias tensor
+        if name.endswith(".moe.router_bias"):
+            name += ".bias"
+
+        if name.endswith((".self_attn.g_proj.weight", ".moe.gate.weight", ".moe.up_proj.weight", ".moe.gate_proj.weight", ".moe.down_proj.weight")):
+            data_torch = data_torch.squeeze().contiguous()
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        # Step35 can optionally use Llama-3 style RoPE scaling (HF: rope_scaling.rope_type == "llama3").
+        # llama.cpp represents this via a single extra tensor: "rope_freqs.weight" (aka MODEL_TENSOR.ROPE_FREQS).
+        rope_params = self.rope_parameters.get("full_attention", self.rope_parameters)
+        rope_type = rope_params.get("rope_type") or ""
+        if rope_type.lower() != "llama3":
+            return
+
+        # Step35 configs can carry per-layer rope_theta as a list; for llama3 rope factors we use the base value.
+        rope_theta = self.hparams.get("rope_theta", 10000.0)
+        if isinstance(rope_theta, list):
+            rope_theta = rope_theta[0]
+        base = float(rope_theta)
+        if (dim := self.hparams.get("head_dim")) is None:
+            dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+        dim = int(dim)
+
+        freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+
+        factor = float(rope_params.get("factor", 8.0))
+        low_freq_factor = float(rope_params.get("low_freq_factor", 1.0))
+        high_freq_factor = float(rope_params.get("high_freq_factor", 4.0))
+        old_context_len = int(rope_params.get("original_max_position_embeddings", self.hparams.get("original_max_position_embeddings", 8192)))
+
+        low_freq_wavelen = old_context_len / low_freq_factor
+        high_freq_wavelen = old_context_len / high_freq_factor
+
+        rope_factors: list[float] = []
+        for freq in freqs:
+            wavelen = 2 * math.pi / float(freq)
+            if wavelen < high_freq_wavelen:
+                rope_factors.append(1.0)
+            elif wavelen > low_freq_wavelen:
+                rope_factors.append(factor)
+            else:
+                smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+                rope_factors.append(1.0 / ((1.0 - smooth) / factor + smooth))
+
+        yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
+
+
 @ModelBase.register("PanguEmbeddedForCausalLM")
 class PanguEmbeddedModel(TextModel):
     model_arch = gguf.MODEL_ARCH.PANGU_EMBED

docs/ops.md

@@ -22,7 +22,7 @@ Legend:
 |                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |

docs/ops/SYCL.csv

@@ -77,8 +77,8 @@
 "SYCL0","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL"
 "SYCL0","FLOOR","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
 "SYCL0","FLOOR","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
-"SYCL0","CEIL","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
-"SYCL0","CEIL","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
+"SYCL0","CEIL","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL"
+"SYCL0","CEIL","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL"
 "SYCL0","ROUND","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
 "SYCL0","ROUND","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
 "SYCL0","TRUNC","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
@@ -161,8 +161,8 @@
 "SYCL0","GELU_ERF","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL"
 "SYCL0","FLOOR","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
 "SYCL0","FLOOR","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
-"SYCL0","CEIL","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
-"SYCL0","CEIL","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
+"SYCL0","CEIL","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL"
+"SYCL0","CEIL","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL"
 "SYCL0","ROUND","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"
 "SYCL0","ROUND","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL"
 "SYCL0","TRUNC","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL"

ggml/src/ggml-sycl/element_wise.cpp

@@ -836,16 +836,9 @@ static inline void ggml_sycl_op_floor(ggml_backend_sycl_context & ctx, ggml_tens
 }
 
 static inline void ggml_sycl_op_ceil(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
-        [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
-            const int num_blocks = ceil_div(k_elements, 256);
-            stream->parallel_for(
-                sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(256),
-                                  sycl::range<1>(256)),
-                [=](sycl::nd_item<1> item_ct1) {
-                    unary_op_ceil_kernel(src, dst_ptr, k_elements, item_ct1);
-                });
-        });
+    ggml_sycl_detail::ggml_sycl_op_unary(ctx, dst, [](auto x) {
+        return op_ceil(x);
+    });
 }
 
 static inline void ggml_sycl_op_round(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {

ggml/src/ggml-sycl/ggml-sycl.cpp

@@ -4591,9 +4591,9 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
                 case GGML_UNARY_OP_EXP:
                 case GGML_UNARY_OP_SOFTPLUS:
                 case GGML_UNARY_OP_ELU:
+                case GGML_UNARY_OP_CEIL:
                     return true;
                 case GGML_UNARY_OP_FLOOR:
-                case GGML_UNARY_OP_CEIL:
                 case GGML_UNARY_OP_ROUND:
                 case GGML_UNARY_OP_TRUNC:
 #if defined (GGML_SYCL_F16)

ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp

@@ -465,4 +465,73 @@ inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_unary_shader(
     return result;
 }
 
+/** Binary **/
+
+struct ggml_webgpu_binary_pipeline_key {
+    int  type;
+    int  op;
+    bool inplace;
+    bool overlap;
+
+    bool operator==(const ggml_webgpu_binary_pipeline_key & other) const {
+        return type == other.type && op == other.op && inplace == other.inplace && overlap == other.overlap;
+    }
+};
+
+struct ggml_webgpu_binary_pipeline_key_hash {
+    size_t operator()(const ggml_webgpu_binary_pipeline_key & key) const {
+        size_t seed = 0;
+        ggml_webgpu_hash_combine(seed, key.type);
+        ggml_webgpu_hash_combine(seed, key.op);
+        ggml_webgpu_hash_combine(seed, key.inplace);
+        ggml_webgpu_hash_combine(seed, key.overlap);
+        return seed;
+    }
+};
+
+struct ggml_webgpu_binary_shader_lib_context {
+    ggml_webgpu_binary_pipeline_key key;
+    uint32_t                        max_wg_size;
+};
+
+inline ggml_webgpu_processed_shader ggml_webgpu_preprocess_binary_shader(
+    pre_wgsl::Preprocessor &                      preprocessor,
+    const char *                                  shader_src,
+    const ggml_webgpu_binary_shader_lib_context & context) {
+    std::vector<std::string> defines;
+    std::string              op_name = ggml_op_name((ggml_op) context.key.op);
+    std::string              variant = op_name;
+
+    defines.push_back(std::string("OP_") + op_name);
+
+    switch (context.key.type) {
+        case GGML_TYPE_F32:
+            defines.push_back("TYPE_F32");
+            variant += "_f32";
+            break;
+        case GGML_TYPE_F16:
+            defines.push_back("TYPE_F16");
+            variant += "_f16";
+            break;
+        default:
+            GGML_ABORT("Unsupported type for binary shader");
+    }
+
+    if (context.key.inplace) {
+        defines.push_back("INPLACE");
+        variant += "_inplace";
+    } else if (context.key.overlap) {
+        defines.push_back("OVERLAP");
+        variant += "_overlap";
+    }
+
+    defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
+    ggml_webgpu_processed_shader result;
+    result.wgsl                                      = preprocessor.preprocess(shader_src, defines);
+    result.variant                                   = variant;
+    ggml_webgpu_generic_shader_decisions * decisions = new ggml_webgpu_generic_shader_decisions();
+    decisions->wg_size                               = context.max_wg_size;
+    result.decisions                                 = decisions;
+    return result;
+}
 #endif  // GGML_WEBGPU_SHADER_LIB_HPP

ggml/src/ggml-webgpu/ggml-webgpu.cpp

@@ -348,13 +348,12 @@ struct webgpu_context_struct {
 
     std::unordered_map<ggml_webgpu_set_rows_pipeline_key, webgpu_pipeline, ggml_webgpu_set_rows_pipeline_key_hash>
                                                   set_rows_pipelines;
-    std::map<int, std::map<int, webgpu_pipeline>> get_rows_pipelines;                 // src_type, vectorized
+    std::map<int, std::map<int, webgpu_pipeline>> get_rows_pipelines;  // src_type, vectorized
 
-    std::map<int, std::map<int, webgpu_pipeline>> cpy_pipelines;                      // src_type, dst_type
-    std::map<int, std::map<int, webgpu_pipeline>> add_pipelines;                      // type, inplace
-    std::map<int, std::map<int, webgpu_pipeline>> sub_pipelines;                      // type, inplace
-    std::map<int, std::map<int, webgpu_pipeline>> mul_pipelines;                      // type, inplace
-    std::map<int, std::map<int, webgpu_pipeline>> div_pipelines;                      // type, inplace
+    std::map<int, std::map<int, webgpu_pipeline>> cpy_pipelines;       // src_type, dst_type
+
+    std::unordered_map<ggml_webgpu_binary_pipeline_key, webgpu_pipeline, ggml_webgpu_binary_pipeline_key_hash>
+        binary_pipelines;
 
     std::map<int, webgpu_pipeline>                               rms_norm_pipelines;  // inplace
     std::map<int, std::map<int, std::map<int, webgpu_pipeline>>> rope_pipelines;      // type, ff, inplace
@@ -823,6 +822,28 @@ static bool ggml_webgpu_tensor_equal(ggml_tensor * a, ggml_tensor * b) {
            (ggml_webgpu_tensor_offset(a) == ggml_webgpu_tensor_offset(b));
 }
 
+// Used to determine if two tensors share the same buffer and their byte ranges overlap,
+static bool ggml_webgpu_tensor_overlap(ggml_tensor * a, ggml_tensor * b) {
+    return (ggml_webgpu_tensor_buf(a).Get() == ggml_webgpu_tensor_buf(b).Get()) &&
+           ggml_webgpu_tensor_offset(a) < (ggml_webgpu_tensor_offset(b) + ggml_nbytes(b)) &&
+           ggml_webgpu_tensor_offset(b) < (ggml_webgpu_tensor_offset(a) + ggml_nbytes(a));
+}
+
+struct binary_overlap_flags {
+    bool inplace;  // src0 == dst
+    bool overlap;  // src1 == dst
+};
+
+static binary_overlap_flags ggml_webgpu_detect_binary_overlap(ggml_tensor * src0,
+                                                              ggml_tensor * src1,
+                                                              ggml_tensor * dst) {
+    binary_overlap_flags flags = {};
+    flags.inplace              = ggml_webgpu_tensor_equal(src0, dst);
+    flags.overlap              = ggml_webgpu_tensor_overlap(src1, dst);
+
+    return flags;
+}
+
 static webgpu_command ggml_webgpu_cpy(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) {
     uint32_t ne = (uint32_t) ggml_nelements(dst);
 
@@ -1375,14 +1396,42 @@ static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * s
     return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
 }
 
-static webgpu_command ggml_webgpu_binary_op(webgpu_context &  ctx,
-                                            ggml_tensor *     src0,
-                                            ggml_tensor *     src1,
-                                            ggml_tensor *     dst,
-                                            webgpu_pipeline & pipeline,
-                                            bool              inplace) {
+static webgpu_command ggml_webgpu_binary_op(webgpu_context & ctx,
+                                            ggml_tensor *    src0,
+                                            ggml_tensor *    src1,
+                                            ggml_tensor *    dst) {
+    binary_overlap_flags flags = ggml_webgpu_detect_binary_overlap(src0, src1, dst);
+
+    ggml_webgpu_binary_pipeline_key pipeline_key = {
+        .type    = dst->type,
+        .op      = dst->op,
+        .inplace = flags.inplace,
+        .overlap = flags.overlap,
+    };
+    ggml_webgpu_binary_shader_lib_context shader_lib_ctx = {
+        .key = pipeline_key, .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup
+    };
+
+    webgpu_pipeline pipeline;
+    auto            it = ctx->binary_pipelines.find(pipeline_key);
+    if (it != ctx->binary_pipelines.end()) {
+        pipeline = it->second;
+    } else {
+        ggml_webgpu_processed_shader processed =
+            ggml_webgpu_preprocess_binary_shader(ctx->p, wgsl_binary, shader_lib_ctx);
+        pipeline =
+            ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str());
+        pipeline.context = processed.decisions;
+        ctx->binary_pipelines.emplace(pipeline_key, pipeline);
+    }
+
+    ggml_webgpu_generic_shader_decisions decisions =
+        *static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context);
+
+    uint32_t ne = (uint32_t) ggml_nelements(dst);
+
     std::vector<uint32_t> params = {
-        (uint32_t) ggml_nelements(dst),
+        ne,
         (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)),
         (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)),
         (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
@@ -1399,24 +1448,30 @@ static webgpu_command ggml_webgpu_binary_op(webgpu_context &  ctx,
         (uint32_t) src1->ne[3],
     };
 
-    std::vector<wgpu::BindGroupEntry> entries = {
-        { .binding = 0,
-         .buffer  = ggml_webgpu_tensor_buf(src0),
-         .offset  = ggml_webgpu_tensor_align_offset(ctx, src0),
-         .size    = ggml_webgpu_tensor_binding_size(ctx, src0) },
-        { .binding = 1,
-         .buffer  = ggml_webgpu_tensor_buf(src1),
-         .offset  = ggml_webgpu_tensor_align_offset(ctx, src1),
-         .size    = ggml_webgpu_tensor_binding_size(ctx, src1) }
-    };
-    if (!inplace) {
+    std::vector<wgpu::BindGroupEntry> entries;
+
+    entries.push_back({
+        .binding = 0,
+        .buffer  = ggml_webgpu_tensor_buf(src0),
+        .offset  = ggml_webgpu_tensor_align_offset(ctx, src0),
+        .size    = ggml_webgpu_tensor_binding_size(ctx, src0),
+    });
+
+    entries.push_back({
+        .binding = 1,
+        .buffer  = ggml_webgpu_tensor_buf(src1),
+        .offset  = ggml_webgpu_tensor_align_offset(ctx, src1),
+        .size    = ggml_webgpu_tensor_binding_size(ctx, src1),
+    });
+
+    if (!flags.inplace && !flags.overlap) {
         entries.push_back({ .binding = 2,
                             .buffer  = ggml_webgpu_tensor_buf(dst),
                             .offset  = ggml_webgpu_tensor_align_offset(ctx, dst),
                             .size    = ggml_webgpu_tensor_binding_size(ctx, dst) });
     }
 
-    uint32_t wg_x = CEIL_DIV(ggml_nelements(dst), WEBGPU_MAX_WG_SIZE);
+    uint32_t wg_x = CEIL_DIV(ne, decisions.wg_size);
     return ggml_backend_webgpu_build(ctx->global_ctx, ctx->param_buf_pool, pipeline, params, entries, wg_x);
 }
 
@@ -2038,25 +2093,10 @@ static std::optional<webgpu_command> ggml_webgpu_encode_node(webgpu_context ctx,
             return std::nullopt;
 #endif
         case GGML_OP_ADD:
-            {
-                int inplace = ggml_webgpu_tensor_equal(src0, node);
-                return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->add_pipelines[node->type][inplace], inplace);
-            }
         case GGML_OP_SUB:
-            {
-                int inplace = ggml_webgpu_tensor_equal(src0, node);
-                return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->sub_pipelines[node->type][inplace], inplace);
-            }
         case GGML_OP_MUL:
-            {
-                int inplace = ggml_webgpu_tensor_equal(src0, node);
-                return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->mul_pipelines[node->type][inplace], inplace);
-            }
         case GGML_OP_DIV:
-            {
-                int inplace = ggml_webgpu_tensor_equal(src0, node);
-                return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->div_pipelines[node->type][inplace], inplace);
-            }
+            return ggml_webgpu_binary_op(ctx, src0, src1, node);
         case GGML_OP_RMS_NORM:
             return ggml_webgpu_rms_norm(ctx, src0, node);
         case GGML_OP_ROPE:
@@ -2665,58 +2705,6 @@ static void ggml_webgpu_init_cpy_pipeline(webgpu_context & webgpu_ctx) {
         ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_cpy_f16_f16, "cpy_f16_f16", constants);
 }
 
-static void ggml_webgpu_init_add_pipeline(webgpu_context & webgpu_ctx) {
-    std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
-
-    webgpu_ctx->add_pipelines[GGML_TYPE_F32][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f32, "add_f32", constants);
-    webgpu_ctx->add_pipelines[GGML_TYPE_F16][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f16, "add_f16", constants);
-    webgpu_ctx->add_pipelines[GGML_TYPE_F32][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f32_inplace, "add_f32_inplace", constants);
-    webgpu_ctx->add_pipelines[GGML_TYPE_F16][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f16_inplace, "add_f16_inplace", constants);
-}
-
-static void ggml_webgpu_init_sub_pipeline(webgpu_context & webgpu_ctx) {
-    std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
-
-    webgpu_ctx->sub_pipelines[GGML_TYPE_F32][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f32, "sub_f32", constants);
-    webgpu_ctx->sub_pipelines[GGML_TYPE_F16][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f16, "sub_f16", constants);
-    webgpu_ctx->sub_pipelines[GGML_TYPE_F32][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f32_inplace, "sub_f32_inplace", constants);
-    webgpu_ctx->sub_pipelines[GGML_TYPE_F16][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f16_inplace, "sub_f16_inplace", constants);
-}
-
-static void ggml_webgpu_init_mul_pipeline(webgpu_context & webgpu_ctx) {
-    std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
-
-    webgpu_ctx->mul_pipelines[GGML_TYPE_F32][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f32, "mul_f32", constants);
-    webgpu_ctx->mul_pipelines[GGML_TYPE_F16][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f16, "mul_f16", constants);
-    webgpu_ctx->mul_pipelines[GGML_TYPE_F32][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f32_inplace, "mul_f32_inplace", constants);
-    webgpu_ctx->mul_pipelines[GGML_TYPE_F16][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f16_inplace, "mul_f16_inplace", constants);
-}
-
-static void ggml_webgpu_init_div_pipeline(webgpu_context & webgpu_ctx) {
-    std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE);
-
-    webgpu_ctx->div_pipelines[GGML_TYPE_F32][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f32, "div_f32", constants);
-    webgpu_ctx->div_pipelines[GGML_TYPE_F16][0] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f16, "div_f16", constants);
-    webgpu_ctx->div_pipelines[GGML_TYPE_F32][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f32_inplace, "div_f32_inplace", constants);
-    webgpu_ctx->div_pipelines[GGML_TYPE_F16][1] =
-        ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f16_inplace, "div_f16_inplace", constants);
-}
-
 static void ggml_webgpu_init_rms_norm_pipeline(webgpu_context & webgpu_ctx) {
     std::vector<wgpu::ConstantEntry> constants = ggml_webgpu_wg_size_entry(WEBGPU_ROW_SPLIT_WG_SIZE);
 
@@ -3018,10 +3006,6 @@ static webgpu_context initialize_webgpu_context(ggml_backend_dev_t dev) {
     ggml_webgpu_init_mul_mat_pipeline(webgpu_ctx);
     ggml_webgpu_init_get_rows_pipeline(webgpu_ctx);
     ggml_webgpu_init_cpy_pipeline(webgpu_ctx);
-    ggml_webgpu_init_add_pipeline(webgpu_ctx);
-    ggml_webgpu_init_sub_pipeline(webgpu_ctx);
-    ggml_webgpu_init_mul_pipeline(webgpu_ctx);
-    ggml_webgpu_init_div_pipeline(webgpu_ctx);
     ggml_webgpu_init_rms_norm_pipeline(webgpu_ctx);
     ggml_webgpu_init_rope_pipeline(webgpu_ctx);
     ggml_webgpu_init_glu_pipeline(webgpu_ctx);

ggml/src/ggml-webgpu/wgsl-shaders/bin_op.tmpl.wgsl

@@ -1,188 +0,0 @@
-#define(VARIANTS)
-
-[
-  {
-    "SHADER_NAME": "add_f32",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "+"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "add_f16",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "+"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "add_f32_inplace",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "+"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "add_f16_inplace",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "+"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "mul_f32",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "*"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "mul_f16",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "*"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "mul_f32_inplace",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "*"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "mul_f16_inplace",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "*"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "sub_f32",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "-"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "sub_f16",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "-"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "sub_f32_inplace",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "-"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "sub_f16_inplace",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "-"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "div_f32",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "/"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "div_f16",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "/"
-    },
-    "DECLS": ["NOT_INPLACE"]
-  },
-  {
-    "SHADER_NAME": "div_f32_inplace",
-    "REPLS": {
-      "TYPE" : "f32",
-      "OP": "/"
-    },
-    "DECLS": ["INPLACE"]
-  },
-  {
-    "SHADER_NAME": "div_f16_inplace",
-    "REPLS": {
-      "TYPE" : "f16",
-      "OP": "/"
-    },
-    "DECLS": ["INPLACE"]
-  }
-]
-
-#end(VARIANTS)
-
-#define(DECLS)
-
-#decl(NOT_INPLACE)
-
-fn update(dst_i: u32, src0_i: u32, src1_i: u32) {
-    dst[dst_i] = src0[src0_i] {{OP}} src1[src1_i];
-}
-
-@group(0) @binding(2)
-var<storage, read_write> dst: array<{{TYPE}}>;
-
-@group(0) @binding(3)
-var<uniform> params: Params;
-
-#enddecl(NOT_INPLACE)
-
-#decl(INPLACE)
-
-fn update(dst_i: u32, src0_i: u32, src1_i: u32) {
-    src0[dst_i] = src0[src0_i] {{OP}} src1[src1_i];
-}
-
-@group(0) @binding(2)
-var<uniform> params: Params;
-
-#enddecl(INPLACE)
-
-#end(DECLS)
-
-
-#define(SHADER)
-
-enable f16;
-
-#include "binary_head.tmpl"
-
-@group(0) @binding(0)
-var<storage, read_write> src0: array<{{TYPE}}>;
-
-@group(0) @binding(1)
-var<storage, read_write> src1: array<{{TYPE}}>;
-
-DECLS
-
-override wg_size: u32;
-@compute @workgroup_size(wg_size)
-fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
-    if (gid.x < params.ne) {
-        update(params.offset_dst + gid.x, params.offset_src0 + gid.x, params.offset_src1 + src1_index(gid.x));
-    }
-}
-
-#end(SHADER)

ggml/src/ggml-webgpu/wgsl-shaders/binary.wgsl

@@ -0,0 +1,107 @@
+enable f16;
+
+struct Params {
+    ne: u32,
+
+    // offsets in elements
+    offset_src0: u32,
+    offset_src1: u32,
+    offset_dst: u32,
+
+    stride_src1_0: u32,
+    stride_src1_1: u32,
+    stride_src1_2: u32,
+    stride_src1_3: u32,
+
+    a_ne0: u32,
+    a_ne1: u32,
+    a_ne2: u32,
+
+    b_ne0: u32,
+    b_ne1: u32,
+    b_ne2: u32,
+    b_ne3: u32,
+};
+
+fn src1_index(_i: u32) -> u32 {
+    var i = _i;
+    let a_i3 = i / (params.a_ne2 * params.a_ne1 * params.a_ne0);
+    i = i % (params.a_ne2 * params.a_ne1 * params.a_ne0);
+    let a_i2 = i / (params.a_ne1 * params.a_ne0);
+    i = i % (params.a_ne1 * params.a_ne0);
+    let a_i1 = i / params.a_ne0;
+    let a_i0 = i % params.a_ne0;
+
+    // handle repetition of b
+    // index loops back to the beginning and repeats after elements are exhausted = modulo
+    let b_i0 = a_i0 % params.b_ne0;
+    let b_i1 = a_i1 % params.b_ne1;
+    let b_i2 = a_i2 % params.b_ne2;
+    let b_i3 = a_i3 % params.b_ne3;
+
+    // compute index for position in b's flat array
+    return b_i0 * params.stride_src1_0 +
+           b_i1 * params.stride_src1_1 +
+           b_i2 * params.stride_src1_2 +
+           b_i3 * params.stride_src1_3;
+}
+
+#ifdef TYPE_F32
+#define DataType f32
+#endif
+#ifdef TYPE_F16
+#define DataType f16
+#endif
+
+@group(0) @binding(0)
+var<storage, read_write> src0: array<DataType>;
+
+@group(0) @binding(1)
+var<storage, read_write> src1 : array<DataType>;
+
+#ifdef INPLACE
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+#elif defined(OVERLAP)
+@group(0) @binding(2)
+var<uniform> params: Params;
+
+#else
+@group(0) @binding(2)
+var<storage, read_write> dst: array<DataType>;
+
+@group(0) @binding(3)
+var<uniform> params: Params;
+#endif
+
+fn op(a: DataType, b: DataType) -> DataType {
+#ifdef OP_ADD
+    return a + b;
+#elif defined(OP_SUB)
+    return a - b;
+#elif defined(OP_MUL)
+    return a * b;
+#elif defined(OP_DIV)
+    return a / b;
+#endif
+}
+
+fn update(dst_i: u32, src0_i: u32, src1_i: u32){
+    let result = op(src0[src0_i], src1[src1_i]);
+
+#ifdef INPLACE
+    src0[dst_i] = result;
+#elif defined(OVERLAP)
+    src1[dst_i] = result;
+#else
+    dst[dst_i] = result;
+#endif
+}
+
+@compute @workgroup_size(WG_SIZE)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
+    if (gid.x < params.ne) {
+        update(params.offset_dst + gid.x, params.offset_src0 + gid.x, params.offset_src1 + src1_index(gid.x));
+    }
+}

ggml/src/ggml-webgpu/wgsl-shaders/binary_head.tmpl

@@ -1,45 +0,0 @@
-struct Params {
-    ne: u32,
-
-    // offsets in elements
-    offset_src0: u32,
-    offset_src1: u32,
-    offset_dst: u32,
-
-    stride_src1_0: u32,
-    stride_src1_1: u32,
-    stride_src1_2: u32,
-    stride_src1_3: u32,
-
-    a_ne0: u32,
-    a_ne1: u32,
-    a_ne2: u32,
-
-    b_ne0: u32,
-    b_ne1: u32,
-    b_ne2: u32,
-    b_ne3: u32,
-};
-
-fn src1_index(_i: u32) -> u32 {
-    var i = _i;
-    let a_i3 = i / (params.a_ne2 * params.a_ne1 * params.a_ne0);
-    i = i % (params.a_ne2 * params.a_ne1 * params.a_ne0);
-    let a_i2 = i / (params.a_ne1 * params.a_ne0);
-    i = i % (params.a_ne1 * params.a_ne0);
-    let a_i1 = i / params.a_ne0;
-    let a_i0 = i % params.a_ne0;
-
-    // handle repetition of b
-    // index loops back to the beginning and repeats after elements are exhausted = modulo
-    let b_i0 = a_i0 % params.b_ne0;
-    let b_i1 = a_i1 % params.b_ne1;
-    let b_i2 = a_i2 % params.b_ne2;
-    let b_i3 = a_i3 % params.b_ne3;
-
-    // compute index for position in b's flat array
-    return b_i0 * params.stride_src1_0 +
-           b_i1 * params.stride_src1_1 +
-           b_i2 * params.stride_src1_2 +
-           b_i3 * params.stride_src1_3;
-}

gguf-py/gguf/constants.py

@@ -146,6 +146,8 @@ class Keys:
         ALTUP_ACTIVE_IDX                  = "{arch}.altup.active_idx"
         ALTUP_NUM_INPUTS                  = "{arch}.altup.num_inputs"
         EMBD_LENGTH_PER_LAYER_INP         = "{arch}.embedding_length_per_layer_input"
+        SWIGLU_CLAMP_EXP                  = "{arch}.swiglu_clamp_exp"
+        SWIGLU_CLAMP_SHEXP                = "{arch}.swiglu_clamp_shexp"
         DENSE_FEAT_IN_SIZE                = "{arch}.{dense}_feat_in"
         DENSE_FEAT_OUT_SIZE               = "{arch}.{dense}_feat_out"
 
@@ -179,20 +181,20 @@ class Keys:
         TEMPERATURE_SCALE            = "{arch}.attention.temperature_scale"
 
     class Rope:
-        DIMENSION_COUNT          = "{arch}.rope.dimension_count"
-        DIMENSION_SECTIONS       = "{arch}.rope.dimension_sections"
-        FREQ_BASE                = "{arch}.rope.freq_base"
-        FREQ_BASE_SWA            = "{arch}.rope.freq_base_swa"
-        SCALING_TYPE             = "{arch}.rope.scaling.type"
-        SCALING_FACTOR           = "{arch}.rope.scaling.factor"
-        SCALING_ATTN_FACTOR      = "{arch}.rope.scaling.attn_factor"
-        SCALING_ORIG_CTX_LEN     = "{arch}.rope.scaling.original_context_length"
-        SCALING_FINETUNED        = "{arch}.rope.scaling.finetuned"
-        SCALING_YARN_LOG_MUL     = "{arch}.rope.scaling.yarn_log_multiplier"
-        SCALING_YARN_EXT_FACTOR  = "{arch}.rope.scaling.yarn_ext_factor"
-        SCALING_YARN_ATTN_FACTOR = "{arch}.rope.scaling.yarn_attn_factor"
-        SCALING_YARN_BETA_FAST   = "{arch}.rope.scaling.yarn_beta_fast"
-        SCALING_YARN_BETA_SLOW   = "{arch}.rope.scaling.yarn_beta_slow"
+        DIMENSION_COUNT           = "{arch}.rope.dimension_count"
+        DIMENSION_SECTIONS        = "{arch}.rope.dimension_sections"
+        FREQ_BASE                 = "{arch}.rope.freq_base"
+        FREQ_BASE_SWA             = "{arch}.rope.freq_base_swa"
+        SCALING_TYPE              = "{arch}.rope.scaling.type"
+        SCALING_FACTOR            = "{arch}.rope.scaling.factor"
+        SCALING_ATTN_FACTOR       = "{arch}.rope.scaling.attn_factor"
+        SCALING_ORIG_CTX_LEN      = "{arch}.rope.scaling.original_context_length"
+        SCALING_FINETUNED         = "{arch}.rope.scaling.finetuned"
+        SCALING_YARN_LOG_MUL      = "{arch}.rope.scaling.yarn_log_multiplier"
+        SCALING_YARN_EXT_FACTOR   = "{arch}.rope.scaling.yarn_ext_factor"
+        SCALING_YARN_ATTN_FACTOR  = "{arch}.rope.scaling.yarn_attn_factor"
+        SCALING_YARN_BETA_FAST    = "{arch}.rope.scaling.yarn_beta_fast"
+        SCALING_YARN_BETA_SLOW    = "{arch}.rope.scaling.yarn_beta_slow"
 
     class Split:
         LLM_KV_SPLIT_NO            = "split.no"
@@ -462,6 +464,7 @@ class MODEL_ARCH(IntEnum):
     PANGU_EMBED      = auto()
     MISTRAL3         = auto()
     MIMO2            = auto()
+    STEP35           = auto()
     LLAMA_EMBED      = auto()
     MAINCODER        = auto()
     KIMI_LINEAR      = auto()
@@ -892,6 +895,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.PANGU_EMBED:      "pangu-embedded",
     MODEL_ARCH.MISTRAL3:         "mistral3",
     MODEL_ARCH.MIMO2:            "mimo2",
+    MODEL_ARCH.STEP35:           "step35",
     MODEL_ARCH.LLAMA_EMBED:      "llama-embed",
     MODEL_ARCH.MAINCODER:        "maincoder",
     MODEL_ARCH.KIMI_LINEAR:      "kimi-linear",
@@ -3364,6 +3368,32 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_UP_EXP,
         MODEL_TENSOR.FFN_EXP_PROBS_B,
     ],
+    MODEL_ARCH.STEP35: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        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_GATE,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_EXP_PROBS_B,
+    ],
     MODEL_ARCH.LLAMA_EMBED: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -3753,12 +3783,12 @@ KEY_ATTENTION_LAYERNORM_EPS     = Keys.Attention.LAYERNORM_EPS
 KEY_ATTENTION_LAYERNORM_RMS_EPS = Keys.Attention.LAYERNORM_RMS_EPS
 
 # RoPE
-KEY_ROPE_DIMENSION_COUNT      = Keys.Rope.DIMENSION_COUNT
-KEY_ROPE_FREQ_BASE            = Keys.Rope.FREQ_BASE
-KEY_ROPE_SCALING_TYPE         = Keys.Rope.SCALING_TYPE
-KEY_ROPE_SCALING_FACTOR       = Keys.Rope.SCALING_FACTOR
-KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN
-KEY_ROPE_SCALING_FINETUNED    = Keys.Rope.SCALING_FINETUNED
+KEY_ROPE_DIMENSION_COUNT           = Keys.Rope.DIMENSION_COUNT
+KEY_ROPE_FREQ_BASE                 = Keys.Rope.FREQ_BASE
+KEY_ROPE_SCALING_TYPE              = Keys.Rope.SCALING_TYPE
+KEY_ROPE_SCALING_FACTOR            = Keys.Rope.SCALING_FACTOR
+KEY_ROPE_SCALING_ORIG_CTX_LEN      = Keys.Rope.SCALING_ORIG_CTX_LEN
+KEY_ROPE_SCALING_FINETUNED         = Keys.Rope.SCALING_FINETUNED
 
 # SSM
 KEY_SSM_CONV_KERNEL    = Keys.SSM.CONV_KERNEL

gguf-py/gguf/gguf_writer.py

@@ -824,6 +824,12 @@ class GGUFWriter:
     def add_expert_gating_func(self, value: ExpertGatingFuncType) -> None:
         self.add_uint32(Keys.LLM.EXPERT_GATING_FUNC.format(arch=self.arch), value.value)
 
+    def add_swiglu_clamp_exp(self, values: Sequence[float]) -> None:
+        self.add_array(Keys.LLM.SWIGLU_CLAMP_EXP.format(arch=self.arch), values)
+
+    def add_swiglu_clamp_shexp(self, values: Sequence[float]) -> None:
+        self.add_array(Keys.LLM.SWIGLU_CLAMP_SHEXP.format(arch=self.arch), values)
+
     def add_expert_group_scale(self, value: float) -> None:
         self.add_float32(Keys.LLM.EXPERT_GROUP_SCALE.format(arch=self.arch), value)
 

gguf-py/gguf/tensor_mapping.py

@@ -359,6 +359,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.ATTN_GATE: (
             "model.layers.{bid}.self_attn.gate_proj", # afmoe
+            "model.layers.{bid}.self_attn.g_proj",    # step3.5 head-wise attention gate
         ),
 
         # Feed-forward norm
@@ -423,6 +424,7 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.router.gate",               # afmoe
             "layers.{bid}.gate",                                # mistral-large
             "backbone.layers.{bid}.mixer.gate",                 # nemotron-h-moe
+            "model.layers.{bid}.moe.gate",                      # step3.5
         ),
 
         MODEL_TENSOR.FFN_GATE_INP_SHEXP: (
@@ -439,6 +441,7 @@ class TensorNameMap:
             "backbone.layers.{bid}.mixer.gate.e_score_correction",          # nemotron-h-moe
             "model.layers.{bid}.mlp.e_score_correction",                    # exaone-moe
             "model.layers.{bid}.block_sparse_moe.gate.e_score_correction",  # kimi
+            "model.layers.{bid}.moe.router_bias",                           # step3.5 expert selection bias
         ),
 
         # Feed-forward up
@@ -493,6 +496,7 @@ class TensorNameMap:
             "model.layers.{bid}.feed_forward.experts.up_proj",      # llama4
             "encoder.layers.{bid}.mlp.experts.mlp.w1",              # nomic-bert-moe
             "model.layers.{bid}.block_sparse_moe.experts.up", # smallthinker
+            "model.layers.{bid}.moe.up_proj",                       # step3.5
         ),
 
         MODEL_TENSOR.FFN_UP_SHEXP: (
@@ -504,6 +508,7 @@ class TensorNameMap:
             "layers.{bid}.shared_experts.w3",                        # mistral-large
             "backbone.layers.{bid}.mixer.shared_experts.up_proj",    # nemotron-h-moe
             "model.layers.{bid}.block_sparse_moe.shared_experts.up_proj", # kimi
+            "model.layers.{bid}.share_expert.up_proj",               # step3.5
         ),
 
         MODEL_TENSOR.FFN_UP_CHEXP: (
@@ -543,6 +548,7 @@ class TensorNameMap:
             "model.layers.{bid}.block_sparse_moe.experts.w1",           # phimoe (merged)
             "model.layers.{bid}.feed_forward.experts.gate_proj",        # llama4
             "model.layers.{bid}.block_sparse_moe.experts.gate",         # smallthinker
+            "model.layers.{bid}.moe.gate_proj",                         # step3.5
         ),
 
         MODEL_TENSOR.FFN_GATE_SHEXP: (
@@ -552,6 +558,7 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.shared_mlp.gate_proj",             # hunyuan
             "layers.{bid}.shared_experts.w1",                          # mistral-large
             "model.layers.{bid}.block_sparse_moe.shared_experts.gate_proj", # kimi
+            "model.layers.{bid}.share_expert.gate_proj",               # step3.5
         ),
 
         MODEL_TENSOR.FFN_GATE_CHEXP: (
@@ -606,6 +613,7 @@ class TensorNameMap:
             "model.layers.{bid}.feed_forward.experts.down_proj",    # llama4
             "encoder.layers.{bid}.mlp.experts.mlp.w2",              # nomic-bert-moe
             "model.layers.{bid}.block_sparse_moe.experts.down",     # smallthinker
+            "model.layers.{bid}.moe.down_proj",                     # step3.5
         ),
 
         MODEL_TENSOR.FFN_DOWN_SHEXP: (
@@ -617,6 +625,7 @@ class TensorNameMap:
             "layers.{bid}.shared_experts.w2",                          # mistral-large
             "backbone.layers.{bid}.mixer.shared_experts.down_proj",    # nemotron-h-moe
             "model.layers.{bid}.block_sparse_moe.shared_experts.down_proj", # kimi
+            "model.layers.{bid}.share_expert.down_proj",               # step3.5
         ),
 
         MODEL_TENSOR.FFN_DOWN_CHEXP: (

gguf-py/pyproject.toml

@@ -23,7 +23,7 @@ numpy = ">=1.17"
 tqdm = ">=4.27"
 pyyaml = ">=5.1"
 requests = ">=2.25"
-sentencepiece = { version = ">=0.1.98,<=0.2.0", optional = true }
+sentencepiece = { version = ">=0.1.98,<0.3.0", optional = true }
 PySide6 = { version = "^6.9", python = ">=3.9,<3.14", optional = true }
 
 [tool.poetry.dev-dependencies]

pyproject.toml

@@ -17,7 +17,7 @@ classifiers = [
 [tool.poetry.dependencies]
 python = ">=3.9"
 numpy = "^1.25.0"
-sentencepiece = ">=0.1.98,<=0.2.0"
+sentencepiece = ">=0.1.98,<0.3.0"
 transformers = ">=4.35.2,<5.0.0"
 protobuf = ">=4.21.0,<5.0.0"
 gguf = { path = "./gguf-py" }

requirements/requirements-convert_legacy_llama.txt

@@ -1,5 +1,5 @@
 numpy~=1.26.4
-sentencepiece~=0.2.0
+sentencepiece>=0.1.98,<0.3.0
 
 transformers>=4.57.1,<5.0.0
 

src/CMakeLists.txt

@@ -135,6 +135,7 @@ add_library(llama
             models/stablelm.cpp
             models/starcoder.cpp
             models/starcoder2.cpp
+            models/step35-iswa.cpp
             models/t5-dec.cpp
             models/t5-enc.cpp
             models/wavtokenizer-dec.cpp

src/llama-arch.cpp

@@ -117,7 +117,8 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_RND1,             "rnd1"             },
     { LLM_ARCH_PANGU_EMBED,      "pangu-embedded"   },
     { LLM_ARCH_MISTRAL3,         "mistral3"         },
-    { LLM_ARCH_MIMO2,            "mimo2"           },
+    { LLM_ARCH_MIMO2,            "mimo2"            },
+    { LLM_ARCH_STEP35,           "step35"           },
     { LLM_ARCH_LLAMA_EMBED,      "llama-embed"      },
     { LLM_ARCH_MAINCODER,        "maincoder"        },
     { LLM_ARCH_KIMI_LINEAR,      "kimi-linear"      },
@@ -162,6 +163,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        "%s.expert_feed_forward_length"        },
     { LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, "%s.expert_shared_feed_forward_length" },
     { LLM_KV_EXPERT_CHUNK_FEED_FORWARD_LENGTH,  "%s.expert_chunk_feed_forward_length"  },
+    { LLM_KV_SWIGLU_CLAMP_EXP,                  "%s.swiglu_clamp_exp"                  },
+    { LLM_KV_SWIGLU_CLAMP_SHEXP,                "%s.swiglu_clamp_shexp"                },
     { LLM_KV_USE_PARALLEL_RESIDUAL,             "%s.use_parallel_residual"             },
     { LLM_KV_TENSOR_DATA_LAYOUT,                "%s.tensor_data_layout"                },
     { LLM_KV_EXPERT_COUNT,                      "%s.expert_count"                      },
@@ -220,21 +223,21 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_KEY_LENGTH_MLA,               "%s.attention.key_length_mla"               },
     { LLM_KV_ATTENTION_VALUE_LENGTH_MLA,             "%s.attention.value_length_mla"             },
 
-    { LLM_KV_ROPE_DIMENSION_COUNT,          "%s.rope.dimension_count"                 },
-    { LLM_KV_ROPE_DIMENSION_SECTIONS,       "%s.rope.dimension_sections"              },
-    { LLM_KV_ROPE_FREQ_BASE,                "%s.rope.freq_base"                       },
-    { LLM_KV_ROPE_FREQ_BASE_SWA,            "%s.rope.freq_base_swa"                   },
-    { LLM_KV_ROPE_SCALE_LINEAR,             "%s.rope.scale_linear"                    },
-    { LLM_KV_ROPE_SCALING_TYPE,             "%s.rope.scaling.type"                    },
-    { LLM_KV_ROPE_SCALING_FACTOR,           "%s.rope.scaling.factor"                  },
-    { LLM_KV_ROPE_SCALING_ATTN_FACTOR,      "%s.rope.scaling.attn_factor"             },
-    { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,     "%s.rope.scaling.original_context_length" },
-    { LLM_KV_ROPE_SCALING_FINETUNED,        "%s.rope.scaling.finetuned"               },
-    { LLM_KV_ROPE_SCALING_YARN_LOG_MUL,     "%s.rope.scaling.yarn_log_multiplier"     },
-    { LLM_KV_ROPE_SCALING_YARN_EXT_FACTOR,  "%s.rope.scaling.yarn_ext_factor"         },
-    { LLM_KV_ROPE_SCALING_YARN_ATTN_FACTOR, "%s.rope.scaling.yarn_attn_factor"        },
-    { LLM_KV_ROPE_SCALING_YARN_BETA_FAST,   "%s.rope.scaling.yarn_beta_fast"          },
-    { LLM_KV_ROPE_SCALING_YARN_BETA_SLOW,   "%s.rope.scaling.yarn_beta_slow"          },
+    { LLM_KV_ROPE_DIMENSION_COUNT,           "%s.rope.dimension_count"                 },
+    { LLM_KV_ROPE_DIMENSION_SECTIONS,        "%s.rope.dimension_sections"              },
+    { LLM_KV_ROPE_FREQ_BASE,                 "%s.rope.freq_base"                       },
+    { LLM_KV_ROPE_FREQ_BASE_SWA,             "%s.rope.freq_base_swa"                   },
+    { LLM_KV_ROPE_SCALE_LINEAR,              "%s.rope.scale_linear"                    },
+    { LLM_KV_ROPE_SCALING_TYPE,              "%s.rope.scaling.type"                    },
+    { LLM_KV_ROPE_SCALING_FACTOR,            "%s.rope.scaling.factor"                  },
+    { LLM_KV_ROPE_SCALING_ATTN_FACTOR,       "%s.rope.scaling.attn_factor"             },
+    { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,      "%s.rope.scaling.original_context_length" },
+    { LLM_KV_ROPE_SCALING_FINETUNED,         "%s.rope.scaling.finetuned"               },
+    { LLM_KV_ROPE_SCALING_YARN_LOG_MUL,      "%s.rope.scaling.yarn_log_multiplier"     },
+    { LLM_KV_ROPE_SCALING_YARN_EXT_FACTOR,   "%s.rope.scaling.yarn_ext_factor"         },
+    { LLM_KV_ROPE_SCALING_YARN_ATTN_FACTOR,  "%s.rope.scaling.yarn_attn_factor"        },
+    { LLM_KV_ROPE_SCALING_YARN_BETA_FAST,    "%s.rope.scaling.yarn_beta_fast"          },
+    { LLM_KV_ROPE_SCALING_YARN_BETA_SLOW,    "%s.rope.scaling.yarn_beta_slow"          },
 
     { LLM_KV_SPLIT_NO,            "split.no"            },
     { LLM_KV_SPLIT_COUNT,         "split.count"         },
@@ -2279,6 +2282,35 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_FFN_UP_EXPS,
                 LLM_TENSOR_FFN_EXP_PROBS_B,
             };
+        case LLM_ARCH_STEP35:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ROPE_FACTORS_LONG,
+                LLM_TENSOR_ROPE_FACTORS_SHORT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_GATE,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_EXP_PROBS_B,
+            };
         case LLM_ARCH_GPTJ:
         case LLM_ARCH_UNKNOWN:
             return {

src/llama-arch.h

@@ -122,6 +122,7 @@ enum llm_arch {
     LLM_ARCH_PANGU_EMBED,
     LLM_ARCH_MISTRAL3,
     LLM_ARCH_MIMO2,
+    LLM_ARCH_STEP35,
     LLM_ARCH_LLAMA_EMBED,
     LLM_ARCH_MAINCODER,
     LLM_ARCH_KIMI_LINEAR,
@@ -166,6 +167,8 @@ enum llm_kv {
     LLM_KV_EXPERT_FEED_FORWARD_LENGTH,
     LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH,
     LLM_KV_EXPERT_CHUNK_FEED_FORWARD_LENGTH,
+    LLM_KV_SWIGLU_CLAMP_EXP,
+    LLM_KV_SWIGLU_CLAMP_SHEXP,
     LLM_KV_USE_PARALLEL_RESIDUAL,
     LLM_KV_TENSOR_DATA_LAYOUT,
     LLM_KV_EXPERT_COUNT,

src/llama-graph.cpp

@@ -13,6 +13,8 @@
 #include <cassert>
 #include <cmath>
 #include <cstring>
+#include <numeric>
+#include <sstream>
 #include <unordered_set>
 
 void llm_graph_input_embd::set_input(const llama_ubatch * ubatch) {
@@ -1014,6 +1016,26 @@ ggml_tensor * llm_graph_context::build_ffn(
     switch (type_op) {
         case LLM_FFN_SILU:
             if (gate && type_gate == LLM_FFN_PAR) {
+                // Step35: HF clamps gate (after SiLU) and up before multiplication
+                if (arch == LLM_ARCH_STEP35 && il >= 0) {
+                    const float limit = hparams.swiglu_clamp_shexp[il];
+                    constexpr float eps = 1e-6f;
+                    if (limit > eps) {
+                        ggml_tensor * gate_act = ggml_silu(ctx0, cur);
+                        cb(gate_act, "ffn_silu", il);
+                        gate_act = ggml_clamp(ctx0, gate_act, -INFINITY, limit);
+                        cb(gate_act, "ffn_silu_clamped", il);
+
+                        tmp = ggml_clamp(ctx0, tmp, -limit, limit);
+                        cb(tmp, "ffn_up_clamped", il);
+
+                        cur = ggml_mul(ctx0, gate_act, tmp);
+                        cb(cur, "ffn_swiglu_limited", il);
+                        type_gate = LLM_FFN_SEQ;
+                        break;
+                    }
+                }
+
                 cur = ggml_swiglu_split(ctx0, cur, tmp);
                 cb(cur, "ffn_swiglu", il);
                 type_gate = LLM_FFN_SEQ;
@@ -1316,6 +1338,25 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
     switch (type_op) {
         case LLM_FFN_SILU:
             if (gate_exps) {
+                // Step35: per-layer clamp for routed experts
+                if (arch == LLM_ARCH_STEP35 && il >= 0) {
+                    const float limit = hparams.swiglu_clamp_exp[il];
+                    constexpr float eps = 1e-6f;
+                    if (limit > eps) {
+                        ggml_tensor * gate_act = ggml_silu(ctx0, cur);
+                        cb(gate_act, "ffn_moe_silu", il);
+                        gate_act = ggml_clamp(ctx0, gate_act, -INFINITY, limit);
+                        cb(gate_act, "ffn_moe_silu_clamped", il);
+
+                        up = ggml_clamp(ctx0, up, -limit, limit);
+                        cb(up, "ffn_moe_up_clamped", il);
+
+                        cur = ggml_mul(ctx0, gate_act, up);
+                        cb(cur, "ffn_moe_swiglu_limited", il);
+                        break;
+                    }
+                }
+
                 cur = ggml_swiglu_split(ctx0, cur, up);
                 cb(cur, "ffn_moe_swiglu", il);
             } else {

src/llama-hparams.h

@@ -206,6 +206,11 @@ struct llama_hparams {
     enum llama_rope_type         rope_type               = LLAMA_ROPE_TYPE_NONE;
     enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;
 
+
+    // Step35: optional per-layer clamps for (Swi)GLU
+    std::array<float, LLAMA_MAX_LAYERS> swiglu_clamp_exp; // clamping for expert FFN
+    std::array<float, LLAMA_MAX_LAYERS> swiglu_clamp_shexp; // shared expert
+
     // this value n_pattern means that every nth layer is dense (i.e. non-SWA)
     // dense_first means whether the pattern is start with a dense layer
     // note that if n_pattern == 0, all layers are SWA

src/llama-kv-cache-iswa.cpp

@@ -218,7 +218,9 @@ llama_memory_context_ptr llama_kv_cache_iswa::init_update(llama_context * lctx,
 }
 
 bool llama_kv_cache_iswa::get_can_shift() const {
-    return kv_base->get_size() == kv_swa->get_size();
+    return kv_base->get_can_shift() &&
+           kv_swa->get_can_shift() &&
+           kv_base->get_size() == kv_swa->get_size();
 }
 
 void llama_kv_cache_iswa::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {

src/llama-kv-cache.cpp

@@ -974,6 +974,10 @@ void llama_kv_cache::apply_ubatch(const slot_info & sinfo, const llama_ubatch &
 }
 
 bool llama_kv_cache::get_can_shift() const {
+    // Step35 uses per-layer RoPE dims; K-shift assumes a single global n_rot.
+    if (model.arch == LLM_ARCH_STEP35) {
+        return false;
+    }
     return true;
 }
 

src/llama-model.cpp

@@ -130,6 +130,7 @@ 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_196B_A11B:     return "196B.A11B";
         case LLM_TYPE_230B_A10B:     return "230B.A10B";
         case LLM_TYPE_235B_A22B:     return "235B.A22B";
         case LLM_TYPE_300B_A47B:     return "300B.A47B";
@@ -560,6 +561,8 @@ void llama_model::load_hparams(llama_model_loader & ml) {
     std::fill(hparams.xielu_alpha_p.begin(), hparams.xielu_alpha_p.end(), 0.0f);
     std::fill(hparams.xielu_beta.begin(), hparams.xielu_beta.end(), 0.0f);
     std::fill(hparams.xielu_eps.begin(), hparams.xielu_eps.end(), 0.0f);
+    std::fill(hparams.swiglu_clamp_exp.begin(),   hparams.swiglu_clamp_exp.end(),   0.0f);
+    std::fill(hparams.swiglu_clamp_shexp.begin(), hparams.swiglu_clamp_shexp.end(), 0.0f);
 
     ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH,  hparams.n_ff_arr,   hparams.n_layer, false);
     ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer, false);
@@ -2482,6 +2485,35 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_STEP35:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+
+                // MoE + SWA parameters
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
+                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,                hparams.expert_gating_func, false);
+                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, false);
+
+                // Step35 uses sigmoid gating by default (if not set in GGUF)
+                if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
+                    hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
+                }
+
+                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,  hparams.n_swa);
+                ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,        hparams.rope_freq_base_train_swa);
+                ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
+                ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP,   hparams.swiglu_clamp_exp,   hparams.n_layer, false);
+                ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_SHEXP, hparams.swiglu_clamp_shexp, hparams.n_layer, false);
+
+                switch (hparams.n_layer) {
+                    case 45: type = LLM_TYPE_196B_A11B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
         default: throw std::runtime_error("unsupported model architecture");
     }
 
@@ -7107,6 +7139,72 @@ 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}, TENSOR_NOT_REQUIRED);
                     }
                 } break;
+            case LLM_ARCH_STEP35:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    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);
+
+                    // STEP35 supports per-layer partial RoPE dims; rope factors are stored as a single shared tensor
+                    // ("rope_freqs.weight") and ggml uses only the first (n_rot_l/2) entries per layer.
+                    uint32_t n_rot_max = 0;
+                    for (int i = 0; i < n_layer; ++i) {
+                        n_rot_max = std::max(n_rot_max, hparams.n_rot);
+                    }
+                    if (n_rot_max == 0) {
+                        n_rot_max = n_rot;
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        const uint32_t n_head_l      = hparams.n_head(i);
+                        const uint32_t n_embd_k_gqa  = hparams.n_embd_k_gqa(i);
+                        const uint32_t n_embd_v_gqa  = hparams.n_embd_v_gqa(i);
+
+                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, TENSOR_NOT_REQUIRED);
+                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, TENSOR_NOT_REQUIRED);
+
+                        // optional rope factors (llama3) / longrope tensors
+                        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
+                            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                        } else {
+                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                        }
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head_l}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_v * n_head_l, n_embd}, 0);
+
+                        // head-wise attention gate (Step35 self_attn.g_proj)
+                        layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head_l}, TENSOR_NOT_REQUIRED);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        // dense MLP (leading dense blocks)
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
+
+                        // MoE routed experts + selection bias (router_bias)
+                        const int64_t n_ff_exp = hparams.n_ff_exp;
+                        layer.ffn_gate_inp      = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_gate_exps     = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp,   n_expert}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_down_exps     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_up_exps       = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff_exp,   n_expert}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_exp_probs_b   = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
+
+                        // shared expert MLP
+                        layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, hparams.n_ff_shexp}, TENSOR_NOT_REQUIRED);
+                        layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, TENSOR_NOT_REQUIRED);
+                    }
+                } break;
             case LLM_ARCH_MAINCODER:
                 {
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -8257,6 +8355,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
             {
                 llm = std::make_unique<llm_build_kimi_linear>(*this, params);
             } break;
+        case LLM_ARCH_STEP35:
+            {
+                llm = std::make_unique<llm_build_step35_iswa>(*this, params);
+            } break;
         default:
             GGML_ABORT("fatal error");
     }
@@ -8502,6 +8604,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_AFMOE:
         case LLM_ARCH_QWEN3NEXT:
         case LLM_ARCH_MIMO2:
+        case LLM_ARCH_STEP35:
             return LLAMA_ROPE_TYPE_NEOX;
 
         case LLM_ARCH_QWEN2VL:

src/llama-model.h

@@ -123,6 +123,7 @@ enum llm_type {
     LLM_TYPE_100B_A6B,
     LLM_TYPE_102B_A12B, // Solar-Open
     LLM_TYPE_106B_A12B, // GLM-4.5-Air
+    LLM_TYPE_196B_A11B, // Step3.5-Flash
     LLM_TYPE_230B_A10B, // Minimax M2
     LLM_TYPE_235B_A22B,
     LLM_TYPE_300B_A47B, // Ernie MoE big

src/models/models.h

@@ -583,6 +583,10 @@ struct llm_build_starcoder : public llm_graph_context {
     llm_build_starcoder(const llama_model & model, const llm_graph_params & params);
 };
 
+struct llm_build_step35_iswa : public llm_graph_context {
+    llm_build_step35_iswa(const llama_model & model, const llm_graph_params & params);
+};
+
 struct llm_build_t5_dec : public llm_graph_context {
     llm_build_t5_dec(const llama_model & model, const llm_graph_params & params);
 };

src/models/step35-iswa.cpp

@@ -0,0 +1,168 @@
+#include "models.h"
+
+llm_build_step35_iswa::llm_build_step35_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    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;
+
+        const uint32_t n_head_l    = hparams.n_head(il);
+        const uint32_t n_head_kv_l = hparams.n_head_kv(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);
+
+        cur = inpL;
+
+        // dump pre-attn RMSNorm input to pinpoint layer boundary issues
+        cb(cur, "attn_norm_in", il);
+
+        // self-attention
+        {
+            cur = build_norm(cur, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head_l,    n_tokens);
+            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv_l, n_tokens);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head_v, n_head_kv_l, n_tokens);
+
+            // Q/K per-head RMSNorm (Step35 q_norm / k_norm)
+            if (model.layers[il].attn_q_norm) {
+                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
+                cb(Qcur, "Qcur_normed", il);
+            }
+            if (model.layers[il].attn_k_norm) {
+                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
+                cb(Kcur, "Kcur_normed", il);
+            }
+
+            // RoPE (partial rotary factors per layer)
+            const bool is_swa = hparams.is_swa(il);
+            ggml_tensor * rope_factors = is_swa ? nullptr : model.get_rope_factors(cparams, il);
+            const int64_t n_rot_l = is_swa ? hparams.n_rot : (hparams.n_rot / 2);
+            Qcur = ggml_rope_ext(
+                ctx0, Qcur, inp_pos, rope_factors,
+                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, rope_factors,
+                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_pos", il);
+            cb(Kcur, "Kcur_pos", il);
+
+            const float kq_scale = 1.0f / sqrtf(float(n_embd_head_k));
+            ggml_tensor * attn_out = build_attn(inp_attn,
+                    nullptr, nullptr,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+            cb(attn_out, "attn_out", il);
+            // head-wise attention gate: sigmoid(g_proj(x)) in torch
+            if (model.layers[il].wqkv_gate) {
+                ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, cur); // [n_head_l, n_tokens]
+                cb(gate, "attn_gate", il);
+
+                gate = ggml_sigmoid(ctx0, gate);
+                cb(gate, "attn_gate_sigmoid", il);
+
+                // reshape + broadcast to [n_embd_head_v, n_head_l, n_tokens]
+                ggml_tensor * attn_3d = ggml_reshape_3d(ctx0, attn_out, n_embd_head_v, n_head_l, n_tokens);
+                ggml_tensor * gate_3d = ggml_reshape_3d(ctx0, gate,       1,          n_head_l, n_tokens);
+                cb(gate_3d, "attn_gate_3d", il);
+
+                attn_3d = ggml_mul(ctx0, attn_3d, gate_3d);
+                cb(attn_3d, "attn_gated_3d", il);
+
+                attn_out = ggml_reshape_2d(ctx0, attn_3d, n_embd_head_v * n_head_l, n_tokens);
+                cb(attn_out, "attn_gated", il);
+            }
+
+            // output projection
+            cur = build_lora_mm(model.layers[il].wo, attn_out);
+            cb(cur, "attn_proj", 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);
+
+        // feed-forward
+        if (model.layers[il].ffn_gate_inp == nullptr) {
+            // dense MLP
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   nullptr,
+                    model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, nullptr,
+                    model.layers[il].ffn_down, model.layers[il].ffn_down_b, nullptr,
+                    nullptr,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        } else {
+            // MoE routed experts
+            const bool  norm_w  = hparams.expert_weights_norm;
+            const float w_scale = hparams.expert_weights_scale;
+            const bool  scale_w = w_scale != 0.0f;
+            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,
+                    norm_w, scale_w, w_scale,
+                    (llama_expert_gating_func_type) hparams.expert_gating_func,
+                    il);
+            cb(moe_out, "ffn_moe_out", il);
+
+            // shared expert MLP (always added on MoE layers in Step35)
+            ggml_tensor * sh_out = build_ffn(cur,
+                    model.layers[il].ffn_up_shexp,   nullptr, nullptr,
+                    model.layers[il].ffn_gate_shexp, nullptr, nullptr,
+                    model.layers[il].ffn_down_shexp, nullptr, nullptr,
+                    nullptr,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(sh_out, "ffn_shared_out", il);
+
+            cur = ggml_add(ctx0, moe_out, sh_out);
+            cb(cur, "ffn_out", il);
+        }
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}

src/unicode.cpp

@@ -497,49 +497,26 @@ static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string &
     return bpe_offsets;
 }
 
-// use std::wregex to split the text
-static std::vector<size_t> unicode_regex_split_stl(const std::wstring & wtext, const std::wstring & regex_expr, const std::vector<size_t> & offsets) {
-    std::wregex expr(regex_expr, std::regex_constants::optimize | std::regex_constants::nosubs);
+template <typename CharT>
+static std::vector<size_t> unicode_regex_split_stl(const std::basic_string<CharT> & text, const std::basic_string<CharT> & regex, const std::vector<size_t> & offsets) {
+    using BidirIt = typename std::basic_string<CharT>::const_iterator;
+#ifdef _MSC_VER
+    // Bypass bug in MSVC: https://github.com/ggml-org/llama.cpp/issues/17830
+    constexpr auto regex_flags = std::regex_constants::ECMAScript;
+#else
+    constexpr auto regex_flags = std::regex_constants::optimize | std::regex_constants::nosubs;
+#endif
+    std::basic_regex<CharT> expr(regex, regex_flags);
     std::vector<size_t> bpe_offsets; // store the offset of each word
     bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size
     size_t start = 0;
     for (auto offset : offsets) {
-        std::wcregex_iterator it(wtext.data() + start, wtext.data() + start + offset, expr);
-        std::wcregex_iterator end;
+        std::regex_iterator<BidirIt> it(text.begin() + start, text.begin() + start + offset, expr);
+        std::regex_iterator<BidirIt> end;
 
         int64_t start_idx = 0;
         while (it != end) {
-            std::wcmatch match = *it;
-            if (match.position() > start_idx) {
-                bpe_offsets.emplace_back(match.position() - start_idx);
-            }
-            bpe_offsets.emplace_back(match.length());
-            start_idx = match.position() + match.length();
-            ++it;
-        }
-
-        if (start_idx < (int64_t) offset) {
-            bpe_offsets.emplace_back(offset - start_idx);
-        }
-        start += offset;
-    }
-
-    return bpe_offsets;
-}
-
-// use std::regex to split the text
-static std::vector<size_t> unicode_regex_split_stl(const std::string & text, const std::string & regex_expr, const std::vector<size_t> & offsets) {
-    std::regex expr(regex_expr, std::regex_constants::optimize | std::regex_constants::nosubs);
-    std::vector<size_t> bpe_offsets; // store the offset of each word
-    bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size
-    size_t start = 0;
-    for (auto offset : offsets) {
-        std::cregex_iterator it(text.data() + start, text.data() + start + offset, expr);
-        std::cregex_iterator end;
-
-        int64_t start_idx = 0;
-        while (it != end) {
-            std::cmatch match = *it;
+            std::match_results<BidirIt> match = *it;
             if (match.position() > start_idx) {
                 bpe_offsets.emplace_back(match.position() - start_idx);
             }

tests/test-backend-ops.cpp

@@ -8231,6 +8231,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
                                                 for (ggml_prec prec : {GGML_PREC_F32, GGML_PREC_DEFAULT}) {
                                                     if (hsk != 128 && prec == GGML_PREC_DEFAULT) continue;
                                                     for (ggml_type type_KV : {GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) {
+                                                        if (type_KV != GGML_TYPE_F16 && hsk != 64 && hsk != 72) continue;
                                                         test_cases.emplace_back(new test_flash_attn_ext(
                                                                     hsk, hsv, nh, {nr2, nr3}, kv, nb, mask, sinks, max_bias, logit_softcap, prec, type_KV));
                                                         // run fewer test cases permuted

tools/server/server-context.cpp

@@ -740,6 +740,11 @@ private:
 
         slots.clear();
 
+        const bool can_spec = common_speculative_is_compat(ctx);
+        if (!can_spec) {
+            SRV_WRN("%s", "speculative decoding not supported by this context\n");
+        }
+
         // initialize slots
         for (int i = 0; i < params_base.n_parallel; i++) {
             server_slot slot;
@@ -752,7 +757,7 @@ private:
             slot.prompt.tokens.has_mtmd = mctx != nullptr;
 
             // try speculative decoding
-            {
+            if (can_spec) {
                 slot.spec = common_speculative_init(params_base.speculative, slot.ctx);
                 if (slot.spec) {
                     if (mctx) {

tools/server/webui/src/lib/components/app/models/ModelsSelector.svelte

@@ -47,7 +47,13 @@
 		upToMessageId
 	}: Props = $props();
 
-	let options = $derived(modelOptions());
+	let options = $derived(
+		modelOptions().filter((option) => {
+			// Exclude models that are marked as not for webui use
+			const modelProps = modelsStore.getModelProps(option.model);
+			return modelProps?.webui !== false;
+		})
+	);
 	let loading = $derived(modelsLoading());
 	let updating = $derived(modelsUpdating());
 	let activeId = $derived(selectedModelId());
@@ -330,6 +336,10 @@
 			return options.find((option) => option.id === activeId);
 		}
 
+		if (options.length === 1) {
+			// Only one option - show it
+			return options[0];
+		}
 		// No selection - return undefined to show "Select model"
 		return undefined;
 	}