diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp
index 474cabdfc..2bfe43a28 100644
--- a/src/llama-model-loader.cpp
+++ b/src/llama-model-loader.cpp
@@ -4,6 +4,7 @@
 #include "ggml.h"
 #include "gguf.h"
 #include "llama-hparams.h"
+#include "llama-ollama-compat.h"
 
 #include <algorithm>
 #include <array>
@@ -551,6 +552,7 @@ llama_model_loader::llama_model_loader(
         }
 
         get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false);
+        if (llama_ollama_compat::translate_metadata(this, metadata, ctx, arch_name, fname.c_str())) use_mmap = false;
         llm_kv = LLM_KV(llm_arch_from_string(arch_name));
 
         files.emplace_back(new llama_file(fname.c_str(), "rb", use_direct_io));
@@ -575,6 +577,9 @@ llama_model_loader::llama_model_loader(
         // so we build a unified tensors index for weights.
         for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
             std::string tensor_name = std::string(cur->name);
+            if (llama_ollama_compat::should_skip_tensor(this, tensor_name.c_str())) {
+                continue;
+            }
             // make sure there is no duplicated tensor names
             if (weights_map.find(tensor_name) != weights_map.end()) {
                 throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", ggml_get_name(cur)));
@@ -685,6 +690,9 @@ llama_model_loader::llama_model_loader(
         // Save tensors data offset info of the main file.
         for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
             std::string tensor_name = std::string(cur->name);
+            if (llama_ollama_compat::should_skip_tensor(this, tensor_name.c_str())) {
+                continue;
+            }
             // make sure there is no duplicated tensor names
             if (weights_map.find(tensor_name) != weights_map.end()) {
                 throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", ggml_get_name(cur)));
@@ -1384,6 +1392,7 @@ void llama_model_loader::get_mapping_range(size_t * first, size_t * last, void *
 
 void llama_model_loader::load_data_for(struct ggml_tensor * cur) const {
     const auto & w = require_weight(ggml_get_name(cur));
+    if (llama_ollama_compat::maybe_load_text_tensor(this, cur, w.offs)) return;
 
     if (use_mmap) {
         const auto & mapping = mappings.at(w.idx);
@@ -1534,6 +1543,7 @@ bool llama_model_loader::load_all_data(
         }
 
         size_t n_size = ggml_nbytes(cur);
+        if (llama_ollama_compat::maybe_load_text_tensor(this, cur, weight->offs)) continue;
 
         if (use_mmap) {
             const auto & mapping = mappings.at(weight->idx);
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index 7bd486030..6c3b23da0 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -10,6 +10,8 @@
 #include "ggml-backend.h"
 #include "gguf.h"
 
+#include "llama-ollama-compat.h"
+
 #include <algorithm>
 #include <cassert>
 #include <cmath>
@@ -1070,6 +1072,11 @@ struct clip_model_loader {
 
         ctx_meta.reset(meta);
 
+        // If this is an Ollama-format monolithic GGUF (text + embedded
+        // vision), translate its metadata and tensor names into the
+        // upstream mmproj shape so the rest of this loader runs unchanged.
+        llama_ollama_compat::translate_clip_metadata(ctx_gguf.get(), meta);
+
         const int n_tensors = gguf_get_n_tensors(ctx_gguf.get());
 
         // print gguf info
@@ -2822,6 +2829,7 @@ struct clip_model_loader {
                     auto it_off = tensor_offset.find(t->name);
                     GGML_ASSERT(it_off != tensor_offset.end() && "no offset for tensor");
                     const size_t offset = it_off->second;
+                    if (llama_ollama_compat::maybe_load_tensor(cur, fname.c_str(), offset, buft)) continue;
                     fin.seekg(offset, std::ios::beg);
                     if (!fin) {
                         throw std::runtime_error(string_format("%s: failed to seek for tensor %s\n", __func__, t->name));
@@ -4489,6 +4497,15 @@ bool clip_image_batch_encode(clip_ctx * ctx, int n_threads, const clip_image_f32
 }
 
 int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
+    const auto projector_type = PROJECTOR_TYPE_NAMES.find(ctx->model.proj_type);
+    if (projector_type != PROJECTOR_TYPE_NAMES.end()) {
+        if (int n = llama_ollama_compat::maybe_clip_mmproj_embd(
+                projector_type->second.c_str(),
+                ctx->model.hparams.projection_dim); n > 0) {
+            return n;
+        }
+    }
+
     switch (ctx->model.proj_type) {
         case PROJECTOR_TYPE_LDP:
             return ctx->model.mm_model_block_1_block_2_1_b->ne[0];
