diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 843c00a8969..e138c2eebd1 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -160,8 +160,6 @@ def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, self.ftype = gguf.LlamaFileType.MOSTLY_F16 logger.info("heuristics unable to detect tensor dtype, defaulting to --outtype f16") - self.dequant_model() - # Configure GGUF Writer self.gguf_writer = gguf.GGUFWriter(path=None, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file, split_max_tensors=split_max_tensors, split_max_size=split_max_size, dry_run=dry_run, small_first_shard=small_first_shard) @@ -527,6 +525,8 @@ def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: return () def prepare_tensors(self): + self.dequant_model() + # Handle empty tensor_map for models with block_count=0 (like MobileNetV5) if self.tensor_map.mapping: max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,") @@ -1812,7 +1812,7 @@ class MmprojModel(ModelBase): preprocessor_config: dict[str, Any] global_config: dict[str, Any] - n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth", "encoder_layers"] + n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth", "encoder_layers", "vt_num_hidden_layers"] has_vision_encoder: bool = True # by default has_audio_encoder: bool = False @@ -1867,7 +1867,15 @@ def __init__(self, *args, **kwargs): preprocessor_config_path = self.dir_model / "preprocessor_config.json" if preprocessor_config_path.is_file(): with open(preprocessor_config_path, "r", encoding="utf-8") as f: - self.preprocessor_config = json.load(f) + cfg = json.load(f) + # move media_proc_cfg to root level for compat + if "media_proc_cfg" in cfg: + cfg = { + **cfg, + **cfg["media_proc_cfg"], + } + # merge configs + self.preprocessor_config = {**self.preprocessor_config, **cfg} # prefer processor_config.json if possible processor_config_path = self.dir_model / "processor_config.json" @@ -1916,10 +1924,10 @@ def set_gguf_parameters(self): self.image_size = self.find_vparam(["image_size"]) self.gguf_writer.add_vision_image_size(self.image_size) self.gguf_writer.add_vision_patch_size(self.find_vparam(["patch_size"])) - self.gguf_writer.add_vision_embedding_length(self.find_vparam(["hidden_size"])) - self.gguf_writer.add_vision_feed_forward_length(self.find_vparam(["intermediate_size"])) + self.gguf_writer.add_vision_embedding_length(self.find_vparam(["hidden_size", "vt_hidden_size"])) + self.gguf_writer.add_vision_feed_forward_length(self.find_vparam(["intermediate_size", "vt_intermediate_size"])) self.gguf_writer.add_vision_block_count(self.find_vparam(self.n_block_keys)) - self.gguf_writer.add_vision_head_count(self.find_vparam(["num_attention_heads", "num_heads"])) + self.gguf_writer.add_vision_head_count(self.find_vparam(["num_attention_heads", "num_heads", "vt_num_attention_heads"])) # preprocessor config image_mean = _MISTRAL_COMMON_DATASET_MEAN if self.is_mistral_format else self.preprocessor_config["image_mean"] @@ -7579,6 +7587,7 @@ def prepare_tensors(self): "DeepseekV2ForCausalLM", "DeepseekV3ForCausalLM", "KimiVLForConditionalGeneration", + "KimiK25ForConditionalGeneration", "YoutuForCausalLM", "YoutuVLForConditionalGeneration", ) @@ -7697,8 +7706,8 @@ def set_gguf_parameters(self): _experts: list[dict[str, Tensor]] | None = None def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: - # skip vision tensors and remove "language_model." for Kimi-VL - if "vision_tower" in name or "multi_modal_projector" in name: + # skip vision tensors and remove "language_model." for Kimi-VL and Kimi-K2.5 + if "vision_tower" in name or "multi_modal_projector" in name or "mm_projector" in name: return if name.startswith("siglip2.") or name.startswith("merger."): return @@ -11060,6 +11069,104 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter yield from super().modify_tensors(data_torch, name, bid) +@ModelBase.register("KimiK25ForConditionalGeneration") +class KimiK25Model(MmprojModel): + """Kimi-K2.5 with MoonViT3d vision encoder""" + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + assert self.hparams_vision is not None, "Kimi-K2.5 requires vision_config in model config" + + self.merge_kernel_size = tuple(self.hparams_vision.get("merge_kernel_size", [2, 2])) + self.patch_size = self.hparams_vision.get("patch_size", 14) + + # Set image_size for compatibility with base class + # Use position embedding dimensions as image_size reference + pos_emb_h = self.hparams_vision.get("init_pos_emb_height", 64) + self.hparams_vision["image_size"] = pos_emb_h * self.patch_size + + def set_gguf_parameters(self): + # Base class MmprojModel.set_gguf_parameters() already writes: + # - vision_block_count, vision_head_count, vision_embedding_length + # - vision_feed_forward_length, vision_patch_size, image_mean, image_std + # via find_vparam() which handles the vt_* prefixed keys in Kimi-K2.5's config + super().set_gguf_parameters() + assert self.hparams_vision is not None + + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.KIMIK25) + + # Position embedding parameters (for interpolation) + self.gguf_writer.add_uint32("vision.pos_emb_height", self.hparams_vision.get("init_pos_emb_height", 64)) + self.gguf_writer.add_uint32("vision.pos_emb_width", self.hparams_vision.get("init_pos_emb_width", 64)) + self.gguf_writer.add_uint32("vision.pos_emb_time", self.hparams_vision.get("init_pos_emb_time", 4)) + + # Projector parameters + self.gguf_writer.add_vision_use_gelu(self.hparams_vision.get("projector_hidden_act", "gelu") == "gelu") + self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("projector_ln_eps", 1e-5)) + self.gguf_writer.add_vision_projector_scale_factor(self.merge_kernel_size[0]) + + # Image size limits + # Note: in_patch_limit is for images, in_patch_limit_each_frame is for video (not supported yet) + in_patch_limit = self.preprocessor_config.get("in_patch_limit", 16384) + min_patches = 8 # reasonable minimum + pixels_per_patch = self.patch_size ** 2 + self.gguf_writer.add_vision_min_pixels(min_patches * pixels_per_patch) + self.gguf_writer.add_vision_max_pixels(in_patch_limit * pixels_per_patch) + + @staticmethod + def permute(weights: Tensor, n_head: int) -> Tensor: + out_dim, in_dim = weights.shape + head_dim = out_dim // n_head + w = weights.reshape(n_head, head_dim // 4, 2, 2, in_dim) + w = w.permute(0, 2, 1, 3, 4) + return w.reshape(out_dim, in_dim) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # Only process vision and projector tensors + is_vision = any(x in name for x in ["vision_tower", "mm_projector"]) + + if not is_vision: + return + + assert self.hparams_vision is not None + n_head = self.hparams_vision.get("num_attention_heads", 16) + + # Permute Q/K weights/biases from interleaved to split RoPE format + # This allows using build_rope_2d at runtime without post-permutation. + if "wqkv" in name: + out_dim = data_torch.shape[0] + qkv_dim = out_dim // 3 + head_dim = qkv_dim // n_head + + if "weight" in name: + wq, wk, wv = data_torch[:qkv_dim, :], data_torch[qkv_dim:2*qkv_dim, :], data_torch[2*qkv_dim:, :] + wq = self.permute(wq, n_head) + wk = self.permute(wk, n_head) + data_torch = torch.cat([wq, wk, wv], dim=0) + elif "bias" in name: + bq, bk, bv = data_torch[:qkv_dim], data_torch[qkv_dim:2*qkv_dim], data_torch[2*qkv_dim:] + bq = bq.reshape(n_head, head_dim // 4, 2, 2).permute(0, 2, 1, 3).reshape(-1) + bk = bk.reshape(n_head, head_dim // 4, 2, 2).permute(0, 2, 1, 3).reshape(-1) + data_torch = torch.cat([bq, bk, bv], dim=0) + + + # Temporal embeddings: (T, 1, C) → (T, C) + if "pos_emb.time_weight" in name: + T, _, C = data_torch.shape + data_torch = data_torch.reshape(T, C) + + # PatchMergerMLP tensor name mapping + # proj.0.weight → proj.linear_1.weight + # proj.2.weight → proj.linear_2.weight + if "mm_projector.proj.0." in name: + name = name.replace(".proj.0.", ".proj.linear_1.") + elif "mm_projector.proj.2." in name: + name = name.replace(".proj.2.", ".proj.linear_2.") + + yield from super().modify_tensors(data_torch, name, bid) + + @ModelBase.register("CogVLMForCausalLM") class CogVLMVisionModel(MmprojModel): diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 3af4fffe957..54b321f9206 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -3704,6 +3704,7 @@ class VisionProjectorType: VOXTRAL = "voxtral" LFM2 = "lfm2" KIMIVL = "kimivl" + KIMIK25 = "kimik25" LIGHTONOCR = "lightonocr" COGVLM = "cogvlm" JANUS_PRO = "janus_pro" diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 167ade78033..548b0359644 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -1296,6 +1296,7 @@ class TensorNameMap: MODEL_TENSOR.V_MMPROJ: ( "multi_modal_projector.linear_{bid}", + "mm_projector.proj.linear_{bid}", # Kimi-K2.5 "visual.merger.mlp.{bid}", # qwen2vl "merger.mlp.{bid}", ), @@ -1357,6 +1358,7 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_ATTN_QKV: ( "visual.blocks.{bid}.attn.qkv", # qwen3vl "model.vision.transformer.layers.{bid}.attention.query_key_value", # cogvlm + "vision_tower.encoder.blocks.{bid}.wqkv" # Kimi-K2.5 ), MODEL_TENSOR.V_ENC_ATTN_Q: ( @@ -1531,6 +1533,7 @@ class TensorNameMap: "multi_modal_projector.norm", "multi_modal_projector.layer_norm", "multi_modal_projector.pre_norm", + "mm_projector.pre_norm", # Kimi-K2.5 "pre_mm_projector_norm", "model.vision.linear_proj.norm1", # cogvlm "merger.ln_q", diff --git a/tools/mtmd/CMakeLists.txt b/tools/mtmd/CMakeLists.txt index 751440af323..02d71f224e2 100644 --- a/tools/mtmd/CMakeLists.txt +++ b/tools/mtmd/CMakeLists.txt @@ -19,6 +19,7 @@ add_library(mtmd models/glm4v.cpp models/internvl.cpp models/kimivl.cpp + models/kimik25.cpp models/llama4.cpp models/llava.cpp models/minicpmv.cpp diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h index ad232178bf4..3bc93ead867 100644 --- a/tools/mtmd/clip-impl.h +++ b/tools/mtmd/clip-impl.h @@ -235,6 +235,7 @@ enum projector_type { PROJECTOR_TYPE_LFM2A, PROJECTOR_TYPE_GLM4V, PROJECTOR_TYPE_YOUTUVL, + PROJECTOR_TYPE_KIMIK25, PROJECTOR_TYPE_UNKNOWN, }; @@ -268,6 +269,7 @@ static std::map PROJECTOR_TYPE_NAMES = { { PROJECTOR_TYPE_LFM2A, "lfm2a"}, { PROJECTOR_TYPE_GLM4V, "glm4v"}, { PROJECTOR_TYPE_YOUTUVL, "youtuvl"}, + { PROJECTOR_TYPE_KIMIK25, "kimik25"}, }; static projector_type clip_projector_type_from_string(const std::string & str) { diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 9fa5afc390e..dae17c6fb04 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -655,6 +655,11 @@ ggml_tensor * clip_graph::build_rope_2d( const int64_t n_head = cur->ne[1]; const int64_t n_pos = cur->ne[2]; + // Ensure input is contiguous (needed when using merged QKV with ggml_view) + if (!ggml_is_contiguous(cur)) { + cur = ggml_cont(ctx0, cur); + } + // for example, if we have cur tensor of shape (n_dim=8, n_head, n_pos) // we will have a list of 4 inv_freq: 1e-0, 1e-1, 1e-2, 1e-3 // first half of cur will use 1e-0, 1e-2 (even) @@ -825,6 +830,10 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 { builder = std::make_unique(ctx, img); } break; + case PROJECTOR_TYPE_KIMIK25: + { + builder = std::make_unique(ctx, img); + } break; case PROJECTOR_TYPE_COGVLM: { builder = std::make_unique(ctx, img); @@ -1139,6 +1148,22 @@ struct clip_model_loader { hparams.set_limit_image_tokens(8, 1024); hparams.set_warmup_n_tokens(256); // avoid OOM on warmup } break; + case PROJECTOR_TYPE_KIMIK25: + { + hparams.rope_theta = 10000.0f; + get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false); + + int min_pixels = 0, max_pixels = 0; + get_u32(KEY_IMAGE_MIN_PIXELS, min_pixels, false); + get_u32(KEY_IMAGE_MAX_PIXELS, max_pixels, false); + if (min_pixels > 0 && max_pixels > 0) { + hparams.image_min_pixels = min_pixels; + hparams.image_max_pixels = max_pixels; + hparams.warmup_image_size = static_cast(std::sqrt(max_pixels)); + } else { + hparams.set_limit_image_tokens(2, 4096); + } + } break; case PROJECTOR_TYPE_GEMMA3: { // default value (used by all model sizes in gemma 3 family) @@ -1668,6 +1693,7 @@ struct clip_model_loader { model.mm_2_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias")); } break; case PROJECTOR_TYPE_KIMIVL: + case PROJECTOR_TYPE_KIMIK25: { model.mm_input_norm_w = get_tensor(TN_MM_INP_NORM); model.mm_input_norm_b = get_tensor(TN_MM_INP_NORM_B); @@ -3039,6 +3065,23 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str res_imgs->entries.push_back(std::move(res)); } break; + case PROJECTOR_TYPE_KIMIK25: + { + GGML_ASSERT(params.image_min_pixels > 0 && params.image_max_pixels > 0); + const clip_image_size target_size = img_tool::calc_size_preserved_ratio( + original_size, + params.patch_size * params.n_merge, + params.image_min_pixels, + params.image_max_pixels); + const std::array pad_color = {0, 0, 0}; + + clip_image_u8 resized_img; + img_tool::resize(*img, resized_img, target_size, img_tool::RESIZE_ALGO_BICUBIC, true, pad_color); + clip_image_f32_ptr res(clip_image_f32_init()); + normalize_image_u8_to_f32(resized_img, *res, params.image_mean, params.image_std); + res_imgs->entries.push_back(std::move(res)); + } break; + case PROJECTOR_TYPE_MLP: case PROJECTOR_TYPE_MLP_NORM: case PROJECTOR_TYPE_LDP: @@ -3247,6 +3290,7 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im } break; case PROJECTOR_TYPE_LFM2: case PROJECTOR_TYPE_KIMIVL: + case PROJECTOR_TYPE_KIMIK25: { // dynamic size int out_patch_size = params.patch_size * ctx->model.hparams.n_merge; @@ -3588,6 +3632,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima } break; case PROJECTOR_TYPE_PIXTRAL: case PROJECTOR_TYPE_KIMIVL: + case PROJECTOR_TYPE_KIMIK25: case PROJECTOR_TYPE_LIGHTONOCR: { // set the 2D positions @@ -3724,6 +3769,47 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings)); } + // Debug: dump final embeddings if MTMD_DEBUG_EMBEDDINGS is set + if (std::getenv("MTMD_DEBUG_EMBEDDINGS") != nullptr) { + const int64_t n_embd = embeddings->ne[0]; + const int64_t n_tokens = embeddings->ne[1]; + std::vector emb_data(n_embd * n_tokens); + ggml_backend_tensor_get(embeddings, emb_data.data(), 0, ggml_nbytes(embeddings)); + + LOG_INF("\n=== MTMD_DEBUG_EMBEDDINGS ===\n"); + LOG_INF("Shape: [%lld, %lld]\n", (long long)n_embd, (long long)n_tokens); + + // Print first few values of first token + LOG_INF("Token 0 (first 16 values): "); + for (int i = 0; i < std::min((int64_t)16, n_embd); i++) { + LOG_INF("%.6f ", emb_data[i]); + } + LOG_INF("\n"); + + // Print last few values of first token + if (n_embd > 16) { + LOG_INF("Token 0 (last 16 values): "); + for (int64_t i = n_embd - 16; i < n_embd; i++) { + LOG_INF("%.6f ", emb_data[i]); + } + LOG_INF("\n"); + } + + // Compute and print statistics + float sum = 0.0f, sum_sq = 0.0f, min_val = emb_data[0], max_val = emb_data[0]; + for (size_t i = 0; i < emb_data.size(); i++) { + sum += emb_data[i]; + sum_sq += emb_data[i] * emb_data[i]; + min_val = std::min(min_val, emb_data[i]); + max_val = std::max(max_val, emb_data[i]); + } + float mean = sum / emb_data.size(); + float variance = (sum_sq / emb_data.size()) - (mean * mean); + LOG_INF("Stats: mean=%.6f, std=%.6f, min=%.6f, max=%.6f, sum=%.6f\n", + mean, sqrtf(variance), min_val, max_val, sum); + LOG_INF("=== END MTMD_DEBUG_EMBEDDINGS ===\n\n"); + } + return true; } @@ -3770,6 +3856,7 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { return ctx->model.mm_2_w->ne[1]; case PROJECTOR_TYPE_LFM2: case PROJECTOR_TYPE_KIMIVL: + case PROJECTOR_TYPE_KIMIK25: return ctx->model.mm_2_w->ne[1]; case PROJECTOR_TYPE_COGVLM: return ctx->model.mm_4h_to_h_w->ne[1]; diff --git a/tools/mtmd/models/kimik25.cpp b/tools/mtmd/models/kimik25.cpp new file mode 100644 index 00000000000..cf9f27f63af --- /dev/null +++ b/tools/mtmd/models/kimik25.cpp @@ -0,0 +1,101 @@ +#include "models.h" +#include +#include + +// note: this is similar to clip_graph::resize_position_embeddings, major difference is having +// the w/h in ne[1] and ne[2] instead of assuming with sqrt. Could try storing the tensor in 2D instead +// with a w*h? Also the permute is a bit different at (2, 1, 0, 3) instead of (2, 0, 1, 3). +ggml_tensor * clip_graph_kimik25::resize_position_embeddings_3d(uint32_t interpolation_mode) { + ggml_tensor * pos_embd = model.position_embeddings; + const int height = img.ny / patch_size; + const int width = img.nx / patch_size; + const uint32_t mode = interpolation_mode; + + GGML_ASSERT(pos_embd); + + const int64_t stored_c = pos_embd->ne[0]; // C = 1152 + const int64_t orig_w = pos_embd->ne[1]; // W = 64 + const int64_t orig_h = pos_embd->ne[2]; // H = 64 + + GGML_ASSERT(stored_c == n_embd); + + if (height == (int)orig_h && width == (int)orig_w) { + // No interpolation needed, just flatten to [C, H*W] + return ggml_cont_2d(ctx0, pos_embd, n_embd, width * height); + } + + pos_embd = ggml_permute(ctx0, pos_embd, 2, 1, 0, 3); + pos_embd = ggml_interpolate(ctx0, pos_embd, height, width, n_embd, 1, mode); + pos_embd = ggml_permute(ctx0, pos_embd, 2, 1, 0, 3); + pos_embd = ggml_cont_2d(ctx0, pos_embd, n_embd, width * height); + return pos_embd; +} + +ggml_cgraph * clip_graph_kimik25::build() { + ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(pos_h, "pos_h"); + ggml_set_input(pos_h); + + ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(pos_w, "pos_w"); + ggml_set_input(pos_w); + + ggml_tensor * learned_pos_embd = resize_position_embeddings_3d(GGML_SCALE_MODE_BICUBIC); + + // Kimi-K2.5 uses interleaved 2D RoPE pattern natively, but + // Q / K are permuted during conversion to use split format. + auto add_pos = [&](ggml_tensor * cur, const clip_layer &) { + cur = build_rope_2d(ctx0, cur, pos_w, pos_h, hparams.rope_theta, false); + return cur; + }; + + ggml_tensor * inp = build_inp(); + + // I don't know why, but doing this in the build_vit lead to the ggml_add not occurring? + // Doing it manually here does work. + inp = ggml_add(ctx0, inp, learned_pos_embd); + + ggml_tensor * cur = build_vit( + inp, n_patches, + NORM_TYPE_NORMAL, + hparams.ffn_op, + nullptr, + add_pos); + + cb(cur, "vit_out", -1); + + { + // patch_merger + const int scale_factor = model.hparams.n_merge; + cur = build_patch_merge_permute(cur, scale_factor); + + // projection norm + int proj_inp_dim = cur->ne[0]; + int n_merged_patches = cur->ne[1]; + cur = ggml_view_2d(ctx0, cur, + n_embd, n_merged_patches * scale_factor * scale_factor, + ggml_row_size(cur->type, n_embd), 0); + cur = ggml_norm(ctx0, cur, hparams.eps); + cur = ggml_mul(ctx0, cur, model.mm_input_norm_w); + cur = ggml_add(ctx0, cur, model.mm_input_norm_b); + cur = ggml_view_2d(ctx0, cur, + proj_inp_dim, n_merged_patches, + ggml_row_size(cur->type, proj_inp_dim), 0); + cb(cur, "proj_inp_normed", -1); + + // projection mlp + cur = build_ffn(cur, + model.mm_1_w, model.mm_1_b, + nullptr, nullptr, + model.mm_2_w, model.mm_2_b, + FFN_GELU, + -1); + + cb(cur, "proj_out", -1); + } + + // build the graph + ggml_build_forward_expand(gf, cur); + + return gf; +} diff --git a/tools/mtmd/models/models.h b/tools/mtmd/models/models.h index 9970980c7bc..c4c67ace627 100644 --- a/tools/mtmd/models/models.h +++ b/tools/mtmd/models/models.h @@ -109,3 +109,10 @@ struct clip_graph_mobilenetv5 : clip_graph { ggml_tensor * inp, const mobilenetv5_block & block); }; + +struct clip_graph_kimik25 : clip_graph { + clip_graph_kimik25(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; + + ggml_tensor * resize_position_embeddings_3d(uint32_t interpolation_mode); +};