diff --git a/README.md b/README.md
index 5f7933c13..4577862e7 100644
--- a/README.md
+++ b/README.md
@@ -88,7 +88,7 @@ Typically finetunes of the base models below are supported as well.
 - [x] [Bitnet b1.58 models](https://huggingface.co/1bitLLM)
 - [x] [Flan T5](https://huggingface.co/models?search=flan-t5)
 - [x] [Open Elm models](https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca)
-- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b)
+- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b) + [GLMEdge-1.5b](https://huggingface.co/THUDM/glm-edge-1.5b-chat) + [GLMEdge-4b](https://huggingface.co/THUDM/glm-edge-4b-chat)
 - [x] [SmolLM](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966)
 - [x] [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
 - [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a)
@@ -109,6 +109,7 @@ Typically finetunes of the base models below are supported as well.
 - [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM)
 - [x] [Moondream](https://huggingface.co/vikhyatk/moondream2)
 - [x] [Bunny](https://github.com/BAAI-DCAI/Bunny)
+- [x] [GLM-EDGE](https://huggingface.co/models?search=glm-edge)
 
 **Bindings:**
 
diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index cc576b70c..c3931aa2f 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -4011,7 +4011,7 @@ class ChatGLMModel(Model):
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
 
-        if name.endswith(".rotary_pos_emb.inv_freq"):
+        if name.endswith(".rotary_pos_emb.inv_freq") or name.startswith("model.vision."):
             return []
 
         name = name.removeprefix("transformer.")
diff --git a/examples/llava/README-glmedge.md b/examples/llava/README-glmedge.md
new file mode 100644
index 000000000..c36203c9e
--- /dev/null
+++ b/examples/llava/README-glmedge.md
@@ -0,0 +1,43 @@
+# GLMV-EDGE
+
+Currently this implementation supports [glm-edge-v-2b](https://huggingface.co/THUDM/glm-edge-v-2b) and [glm-edge-v-5b](https://huggingface.co/THUDM/glm-edge-v-5b).
+
+## Usage
+Build with cmake or run `make llama-llava-cli` to build it.
+
+After building, run: `./llama-llava-cli` to see the usage. For example:
+
+```sh
+./llama-llava-cli -m model_path/ggml-model-f16.gguf --mmproj model_path/mmproj-model-f16.gguf --image img_path/image.jpg -p "<|system|>\n system prompt <image><|user|>\n prompt <|assistant|>\n"
+```
+
+**note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so.
+**note**: For GPU offloading ensure to use the `-ngl` flag just like usual
+
+## GGUF conversion
+
+1. Clone a GLMV-EDGE model ([2B](https://huggingface.co/THUDM/glm-edge-v-2b) or [5B](https://huggingface.co/THUDM/glm-edge-v-5b)). For example:
+
+```sh
+git clone https://huggingface.co/THUDM/glm-edge-v-5b or https://huggingface.co/THUDM/glm-edge-v-2b
+```
+
+2. Use `glmedge-surgery.py` to split the GLMV-EDGE model to LLM and multimodel projector constituents:
+
+```sh
+python ./examples/llava/glmedge-surgery.py -m ../model_path
+```
+
+4. Use `glmedge-convert-image-encoder-to-gguf.py` to convert the GLMV-EDGE image encoder to GGUF:
+
+```sh
+python ./examples/llava/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path
+```
+
+5. Use `examples/convert_hf_to_gguf.py` to convert the LLM part of GLMV-EDGE to GGUF:
+
+```sh
+python convert_hf_to_gguf.py ../model_path 
+```
+
+Now both the LLM part and the image encoder are in the `model_path` directory.
\ No newline at end of file
diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index aae49c965..b43aa3cc2 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -90,6 +90,7 @@ static std::string format(const char * fmt, ...) {
 #define KEY_HAS_VIS_ENC         "clip.has_vision_encoder"
 #define KEY_HAS_LLAVA_PROJ      "clip.has_llava_projector"
 #define KEY_HAS_MINICPMV_PROJ   "clip.has_minicpmv_projector"
+#define KEY_HAS_GLM_PROJ        "clip.has_glm_projector"
 #define KEY_MINICPMV_VERSION    "clip.minicpmv_version"
 #define KEY_USE_GELU            "clip.use_gelu"
 #define KEY_N_EMBD              "clip.%s.embedding_length"
@@ -145,6 +146,15 @@ static std::string format(const char * fmt, ...) {
 #define TN_MINICPMV_ATTN "resampler.attn.%s.%s"
 #define TN_MINICPMV_LN "resampler.ln_%s.%s"
 
+#define TN_GLM_ADAPER_CONV "adapter.conv.%s"
+#define TN_GLM_ADAPTER_LINEAR "adapter.linear.linear.%s"
+#define TN_GLM_ADAPTER_NORM_1 "adapter.linear.norm1.%s"
+#define TN_GLM_ADAPTER_D_H_2_4H "adapter.linear.dense_h_to_4h.%s"
+#define TN_GLM_ADAPTER_GATE "adapter.linear.gate.%s"
+#define TN_GLM_ADAPTER_D_4H_2_H "adapter.linear.dense_4h_to_h.%s"
+#define TN_GLM_BOI_W "adapter.boi"
+#define TN_GLM_EOI_W "adapter.eoi"
+
 
 enum projector_type {
     PROJECTOR_TYPE_MLP,
@@ -152,6 +162,7 @@ enum projector_type {
     PROJECTOR_TYPE_LDP,
     PROJECTOR_TYPE_LDPV2,
     PROJECTOR_TYPE_RESAMPLER,
+    PROJECTOR_TYPE_ADAPTER,
     PROJECTOR_TYPE_UNKNOWN,
 };
 
@@ -160,6 +171,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_LDP, "ldp" },
     { PROJECTOR_TYPE_LDPV2, "ldpv2"},
     { PROJECTOR_TYPE_RESAMPLER, "resampler"},
+    { PROJECTOR_TYPE_ADAPTER, "adapter"}
 };
 
 
@@ -482,6 +494,12 @@ struct clip_vision_model {
     struct ggml_tensor * mm_4_w = NULL;
     struct ggml_tensor * mm_4_b = NULL;
 
+    //GLMV-Edge projection
+    struct ggml_tensor * mm_model_adapter_conv_w;
+    struct ggml_tensor * mm_model_adapter_conv_b;
+    struct ggml_tensor * boi_w;
+    struct ggml_tensor * eoi_w;
+
     // MobileVLM projection
     struct ggml_tensor * mm_model_mlp_1_w;
     struct ggml_tensor * mm_model_mlp_1_b;
@@ -542,6 +560,7 @@ struct clip_ctx {
     bool has_vision_encoder  = false;
     bool has_llava_projector = false;
     bool has_minicpmv_projector = false;
+    bool has_glm_projector = false;
     int minicpmv_version = 2;
 
     struct clip_vision_model vision_model;
@@ -606,7 +625,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
 
     const int batch_size = imgs->size;
 
-    if (ctx->has_llava_projector || ctx->has_minicpmv_projector) {
+    if (ctx->has_llava_projector || ctx->has_minicpmv_projector || ctx->has_glm_projector) {
         GGML_ASSERT(batch_size == 1);
     }
 
@@ -677,7 +696,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     }
 
     // loop over layers
-    if (ctx->has_minicpmv_projector) {
+    if (ctx->has_minicpmv_projector || ctx->has_glm_projector) {
         n_layer += 1;
     }
     for (int il = 0; il < n_layer - 1; il++) {
@@ -1019,6 +1038,33 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             GGML_ASSERT(false);
         }
     }
+    // glm projector
+    else if(ctx->has_glm_projector){
+        if (ctx->proj_type == PROJECTOR_TYPE_ADAPTER){
+            size_t gridsz = (size_t)sqrt(embeddings->ne[1]);
+            embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings,1,0,2,3));
+            embeddings = ggml_reshape_3d(ctx0,embeddings,gridsz,gridsz,embeddings->ne[1]);
+            embeddings = ggml_conv_2d(ctx0, model.mm_model_adapter_conv_w, embeddings, 2, 2, 0, 0, 1, 1);
+            embeddings = ggml_reshape_3d(ctx0, embeddings,embeddings->ne[0]*embeddings->ne[1] , embeddings->ne[2], batch_size);
+            embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings,1,0,2,3));
+            embeddings = ggml_add(ctx0, embeddings, model.mm_model_adapter_conv_b);
+            //GLU
+            {
+                embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings);
+                embeddings = ggml_norm(ctx0, embeddings, eps);
+                embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_q_w), model.mm_model_ln_q_b);
+                embeddings = ggml_gelu_inplace(ctx0, embeddings);
+                struct ggml_tensor * x = embeddings;
+                embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, embeddings);
+                x = ggml_mul_mat(ctx0,model.mm_model_mlp_1_w,x);
+                embeddings = ggml_silu_inplace(ctx0,embeddings);
+                embeddings = ggml_mul(ctx0,embeddings,x);
+                embeddings = ggml_mul_mat(ctx0,model.mm_model_mlp_3_w,embeddings);
+            }
+        }else{
+            GGML_ABORT("fatel error");
+        }
+    }
 
     // build the graph
     ggml_build_forward_expand(gf, embeddings);
@@ -1190,6 +1236,11 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             new_clip->minicpmv_version = gguf_get_val_i32(ctx, idx);
         }
 
+        idx = gguf_find_key(ctx, KEY_HAS_GLM_PROJ);
+        if (idx != -1) {
+            new_clip->has_glm_projector = gguf_get_val_bool(ctx, idx);
+        }
+
         // GGML_ASSERT(new_clip->has_llava_projector); // see monatis/clip.cpp for image and/or text encoding for semantic search
 
         GGML_ASSERT(new_clip->has_vision_encoder);
@@ -1203,6 +1254,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             LOG_INF("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
             LOG_INF("%s: llava_projector:  %d\n", __func__, new_clip->has_llava_projector);
             LOG_INF("%s: minicpmv_projector:  %d\n", __func__, new_clip->has_minicpmv_projector);
+            LOG_INF("%s: glm_projector:  %d\n", __func__, new_clip->has_glm_projector);
             LOG_INF("%s: model size:     %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
             LOG_INF("%s: metadata size:  %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
         }
@@ -1465,6 +1517,19 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             vision_model.mm_model_ln_post_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "weight"));
             vision_model.mm_model_ln_post_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "bias"));
         }
+        else if(new_clip->proj_type == PROJECTOR_TYPE_ADAPTER){
+            printf("adapter get data\n");
+            vision_model.mm_model_adapter_conv_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPER_CONV, "weight"));
+            vision_model.mm_model_adapter_conv_b = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPER_CONV, "bias"));
+            vision_model.mm_model_mlp_0_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_LINEAR,"weight"));
+            vision_model.mm_model_ln_q_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_NORM_1,"weight"));
+            vision_model.mm_model_ln_q_b = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_NORM_1,"bias"));
+            vision_model.mm_model_mlp_1_w =  get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_D_H_2_4H,"weight"));
+            vision_model.mm_model_mlp_2_w =  get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_GATE,"weight"));
+            vision_model.mm_model_mlp_3_w =  get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_D_4H_2_H,"weight"));
+            vision_model.boi_w = get_tensor(new_clip->ctx_data, TN_GLM_BOI_W);
+            vision_model.eoi_w = get_tensor(new_clip->ctx_data, TN_GLM_EOI_W);
+        }
         else {
             std::string proj_type = PROJECTOR_TYPE_NAMES[new_clip->proj_type];
             throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
@@ -1969,6 +2034,20 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
         return true;
     }
 
+    if(ctx->has_glm_projector){
+        res_imgs->size = 1;
+        res_imgs->data = new clip_image_f32[res_imgs->size];
+        clip_image_u8 resized_image;
+        int32_t sz=ctx->vision_model.hparams.image_size;
+        bicubic_resize(*img, resized_image,sz,sz);
+        clip_image_f32 * res = clip_image_f32_init();
+        //clip_image_save_to_bmp(resized_image, "resized.bmp");
+        normalize_image_u8_to_f32(&resized_image, res, ctx->image_mean, ctx->image_std);
+        res_imgs->data[0] = *res;
+        clip_image_f32_free(res);
+        return true;
+    }
+
     bool pad_to_square = true;
     if (!ctx->has_vision_encoder) {
         LOG_ERR("This gguf file seems to have no vision encoder\n");
@@ -2154,6 +2233,8 @@ void clip_free(clip_ctx * ctx) {
 }
 
 size_t clip_embd_nbytes(const struct clip_ctx * ctx) {
+    if(ctx->has_glm_projector)
+        return (clip_n_patches(ctx)+2) * clip_n_mmproj_embd(ctx) * sizeof(float);
     return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float);
 }
 
@@ -2182,7 +2263,7 @@ int clip_n_patches(const struct clip_ctx * ctx) {
 
     int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
 
-    if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2) {
+    if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2 || ctx->proj_type == PROJECTOR_TYPE_ADAPTER) {
         n_patches /= 4;
     } else if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) {
         if (ctx->minicpmv_version == 2) {
@@ -2307,6 +2388,12 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     if (ctx->has_minicpmv_projector) {
         GGML_ASSERT(batch_size == 1);
     }
+    if(ctx->has_glm_projector){
+        GGML_ASSERT(batch_size == 1);
+        ggml_tensor * boi = ctx->vision_model.boi_w;
+        ggml_backend_tensor_get(boi,vec,0,ggml_nbytes(boi));
+        vec=(float*)(vec+ggml_nelements(boi)); //offset for boi
+    }
 
     // build the inference graph
     ggml_cgraph * gf = clip_image_build_graph(ctx, imgs, ctx->load_image_size, true);
@@ -2430,7 +2517,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
             free(positions_data);
         }
 
-        {
+        if (!ctx->has_glm_projector){
             struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches");
             int* patches_data = (int*)malloc(ggml_nbytes(patches));
             for (int i = 0; i < num_patches; i++) {
@@ -2453,6 +2540,13 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     // copy the embeddings to the location passed by the user
     ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));
 
+    if(ctx->has_glm_projector){
+        //eoi
+        ggml_tensor * eoi = ctx->vision_model.eoi_w;
+        int offset=ggml_nelements(eoi)*clip_n_patches(ctx);
+        ggml_backend_tensor_get(eoi,vec+offset,0,ggml_nbytes(eoi));
+    }
+
     return true;
 }
 
@@ -2610,6 +2704,9 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
             return 3584;
         }
     }
+    if (ctx->proj_type == PROJECTOR_TYPE_ADAPTER){
+        return ctx->vision_model.mm_model_mlp_3_w->ne[1];
+    }
 
     std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type];
     throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
@@ -2621,3 +2718,7 @@ int clip_is_minicpmv(const struct clip_ctx * ctx) {
     }
     return 0;
 }
+
+bool clip_is_glm(const struct clip_ctx * ctx) {
+    return ctx->has_glm_projector;
+}
\ No newline at end of file
diff --git a/examples/llava/clip.h b/examples/llava/clip.h
index 78588bdf1..c92cb5dee 100644
--- a/examples/llava/clip.h
+++ b/examples/llava/clip.h
@@ -87,6 +87,8 @@ CLIP_API bool clip_model_quantize(const char * fname_inp, const char * fname_out
 
 CLIP_API int clip_is_minicpmv(const struct clip_ctx * ctx);
 
+CLIP_API bool clip_is_glm(const struct clip_ctx * ctx);
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/examples/llava/glmedge-convert-image-encoder-to-gguf.py b/examples/llava/glmedge-convert-image-encoder-to-gguf.py
new file mode 100644
index 000000000..ac1911a14
--- /dev/null
+++ b/examples/llava/glmedge-convert-image-encoder-to-gguf.py
@@ -0,0 +1,280 @@
+import argparse
+import os
+import json
+import re
+
+import torch
+import numpy as np
+from gguf import *
+
+TEXT = "clip.text"
+VISION = "clip.vision"
+from transformers import SiglipVisionModel, SiglipVisionConfig
+
+def k(raw_key: str, arch: str) -> str:
+    return raw_key.format(arch=arch)
+
+
+def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: bool) -> bool:
+    if name in (
+        "logit_scale",
+        "text_model.embeddings.position_ids",
+        "vision_model.embeddings.position_ids",
+    ):
+        return True
+    
+    if name in (
+        "vision_model.head.probe",
+        "vision_model.head.attention.in_proj_weight",
+        "vision_model.head.attention.in_proj_bias",
+        "vision_model.head.attention.out_proj.weight",
+        "vision_model.head.attention.out_proj.bias",
+        "vision_model.head.layernorm.weight",
+        "vision_model.head.layernorm.bias",
+        "vision_model.head.mlp.fc1.weight",
+        "vision_model.head.mlp.fc1.bias",
+        "vision_model.head.mlp.fc2.weight",
+        "vision_model.head.mlp.fc2.bias"
+    ):
+        return True
+
+    if name.startswith("v") and not has_vision:
+        return True
+
+    if name.startswith("t") and not has_text:
+        return True
+
+    return False
+
+
+def get_tensor_name(name: str) -> str:
+    if "projection" in name:
+        return name
+    if "mm_projector" in name:
+        name = name.replace("model.mm_projector", "mm")
+        name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1)
+        name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1)
+        return name
+
+    return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln")
+
+
+def bytes_to_unicode():
+    """
+    Returns list of utf-8 byte and a corresponding list of unicode strings.
+    The reversible bpe codes work on unicode strings.
+    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
+    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
+    This is a significant percentage of your normal, say, 32K bpe vocab.
+    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
+    And avoids mapping to whitespace/control characters the bpe code barfs on.
+    """
+    bs = (
+        list(range(ord("!"), ord("~") + 1))
+        + list(range(ord("¡"), ord("¬") + 1))
+        + list(range(ord("®"), ord("ÿ") + 1))
+    )
+    cs = bs[:]
+    n = 0
+    for b in range(2**8):
+        if b not in bs:
+            bs.append(b)
+            cs.append(2**8 + n)
+            n += 1
+    cs = [chr(n) for n in cs]
+    return dict(zip(bs, cs))
+
+
+ap = argparse.ArgumentParser()
+ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True)
+ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16")
+ap.add_argument("--text-only", action="store_true", required=False,
+                help="Save a text-only model. It can't be used to encode images")
+ap.add_argument("--vision-only", action="store_true", required=False,
+                help="Save a vision-only model. It can't be used to encode texts")
+ap.add_argument("--clip-model-is-vision", action="store_true", required=False,
+                help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
+ap.add_argument("--clip-model-is-openclip", action="store_true", required=False,
+                help="The clip model is from openclip (for ViT-SO400M type))")
+ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
+ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2","adapter"], default="adapter")
+ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
+# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
+# Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5
+default_image_mean = [0.5, 0.5, 0.5]
+default_image_std = [0.5, 0.5, 0.5]
+ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
+ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
+
+# with proper
+args = ap.parse_args()
+
+
+if args.text_only and args.vision_only:
+    print("--text-only and --image-only arguments cannot be specified at the same time.")
+    exit(1)
+
+if args.use_f32:
+    print("WARNING: Weights for the convolution op is always saved in f16, as the convolution op in GGML does not support 32-bit kernel weights yet.")
+
+# output in the same directory as the model if output_dir is None
+dir_model = args.model_dir
+
+if args.clip_model_is_vision or not os.path.exists(dir_model + "/vocab.json") or args.clip_model_is_openclip:
+    vocab = None
+    tokens = None
+else:
+    with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
+        vocab = json.load(f)
+        tokens = [key for key in vocab]
+
+with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
+    config = json.load(f)
+    if args.clip_model_is_vision:
+        v_hparams = config
+        t_hparams = None
+    else:
+        v_hparams = config["vision_config"]
+        t_hparams = None
+
+# possible data types
+#   ftype == 0 -> float32
+#   ftype == 1 -> float16
+#
+# map from ftype to string
+ftype_str = ["f32", "f16"]
+
+ftype = 1
+if args.use_f32:
+    ftype = 0
+
+vision_config = SiglipVisionConfig(**v_hparams)
+model = SiglipVisionModel(vision_config)
+model.load_state_dict(torch.load(os.path.join(dir_model, "glm.clip")))
+
+fname_middle = None
+has_text_encoder = False
+has_vision_encoder = True
+has_glm_projector = True
+if args.text_only:
+    fname_middle = "text-"
+    has_vision_encoder = False
+elif args.llava_projector is not None:
+    fname_middle = "mmproj-"
+    has_text_encoder = False
+    has_glm_projector = True
+elif args.vision_only:
+    fname_middle = "vision-"
+    has_text_encoder = False
+else:
+    fname_middle = ""
+
+output_dir = args.output_dir if args.output_dir is not None else dir_model
+os.makedirs(output_dir, exist_ok=True)
+output_prefix = os.path.basename(output_dir).replace("ggml_", "")
+fname_out = os.path.join(output_dir, f"{fname_middle}model-{ftype_str[ftype]}.gguf")
+fout = GGUFWriter(path=fname_out, arch="clip")
+
+fout.add_bool("clip.has_text_encoder", has_text_encoder)
+fout.add_bool("clip.has_vision_encoder", has_vision_encoder)
+fout.add_bool("clip.has_glm_projector", has_glm_projector)
+fout.add_file_type(ftype)
+model_name = config["_name_or_path"] if "_name_or_path" in config else os.path.basename(dir_model)
+fout.add_name(model_name)
+if has_glm_projector:
+    fout.add_description("image encoder for glm4v")
+    fout.add_string("clip.projector_type", "adapter")
+else:
+    fout.add_description("two-tower CLIP model")
+
+if has_text_encoder:
+    assert t_hparams is not None
+    assert tokens is not None
+    # text_model hparams
+    fout.add_uint32(k(KEY_CONTEXT_LENGTH, TEXT), t_hparams["max_position_embeddings"])
+    fout.add_uint32(k(KEY_EMBEDDING_LENGTH, TEXT), t_hparams["hidden_size"])
+    fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, TEXT), t_hparams["intermediate_size"])
+    fout.add_uint32("clip.text.projection_dim", t_hparams.get("projection_dim", config["projection_dim"]))
+    fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, TEXT), t_hparams["num_attention_heads"])
+    fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, TEXT), t_hparams["layer_norm_eps"])
+    fout.add_uint32(k(KEY_BLOCK_COUNT, TEXT), t_hparams["num_hidden_layers"])
+    fout.add_token_list(tokens)
+
+if has_vision_encoder:
+    # vision_model hparams
+    fout.add_uint32("clip.vision.image_size", v_hparams["image_size"])
+    fout.add_uint32("clip.vision.patch_size", v_hparams["patch_size"])
+    fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), v_hparams["hidden_size"])
+    fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), v_hparams["intermediate_size"])
+    fout.add_uint32("clip.vision.projection_dim", 0)
+    fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), v_hparams["num_attention_heads"])
+    fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
+    fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), v_hparams["num_hidden_layers"])
+
+    image_mean = args.image_mean if args.image_mean is not None else default_image_mean
+    image_std = args.image_std if args.image_std is not None else default_image_std
+    fout.add_array("clip.vision.image_mean", image_mean)
+    fout.add_array("clip.vision.image_std", image_std)
+
+fout.add_bool("clip.use_gelu", True)
+
+
+if has_glm_projector:
+    # model.vision_model.encoder.layers.pop(-1)  # pyright: ignore[reportAttributeAccessIssue] 
+    projector = torch.load(args.llava_projector)
+    for name, data in projector.items():
+        name = get_tensor_name(name)
+        # pw and dw conv ndim==4
+        if data.ndim == 2 or data.ndim == 4:
+            data = data.squeeze().numpy().astype(np.float16)
+        else:
+            data = data.squeeze().numpy().astype(np.float32)
+        if name.startswith("vision."):
+            name=name.replace("vision.","")
+        fout.add_tensor(name, data)
+        print(f"Projector {name} - {data.dtype} - shape = {data.shape}")
+        # print(f"Projector {name} tensors added\n")
+
+state_dict = model.state_dict()  # pyright: ignore[reportAttributeAccessIssue]
+for name, data in state_dict.items():
+    if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_glm_projector):
+        # we don't need this
+        print(f"skipping parameter: {name}")
+        continue
+
+    name = get_tensor_name(name)
+    data = data.squeeze().numpy()
+
+    n_dims = len(data.shape)
+
+    # ftype == 0 -> float32, ftype == 1 -> float16
+    ftype_cur = 0
+    if n_dims == 4:
+        print(f"tensor {name} is always saved in f16")
+        data = data.astype(np.float16)
+        ftype_cur = 1
+    elif ftype == 1:
+        if name[-7:] == ".weight" and n_dims == 2:
+            # print("  Converting to float16")
+            data = data.astype(np.float16)
+            ftype_cur = 1
+        else:
+            # print("  Converting to float32")
+            data = data.astype(np.float32)
+            ftype_cur = 0
+    else:
+        if data.dtype != np.float32:
+            # print("  Converting to float32")
+            data = data.astype(np.float32)
+            ftype_cur = 0
+    print(f"siglip {name} - {data.dtype} - shape = {data.shape}")
+    # print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}")
+    fout.add_tensor(name, data)
+
+
+fout.write_header_to_file()
+fout.write_kv_data_to_file()
+fout.write_tensors_to_file()
+fout.close()
+
+print("Done. Output file: " + fname_out)
\ No newline at end of file
diff --git a/examples/llava/glmedge-surgery.py b/examples/llava/glmedge-surgery.py
new file mode 100644
index 000000000..7d7dc6837
--- /dev/null
+++ b/examples/llava/glmedge-surgery.py
@@ -0,0 +1,33 @@
+import argparse
+import os
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+ap = argparse.ArgumentParser()
+ap.add_argument("-m", "--model", help="Path to GLM model")
+args = ap.parse_args()
+
+# find the model part that includes the the multimodal projector weights
+model = AutoModel.from_pretrained(args.model, trust_remote_code=True, local_files_only=True)
+checkpoint = model.state_dict()
+
+# get a list of mm tensor names
+mm_tensors = [k for k, v in checkpoint.items() if k.startswith("vision.adapter.")]
+
+# store these tensors in a new dictionary and torch.save them
+projector = {name: checkpoint[name].float() for name in mm_tensors}
+torch.save(projector, f"{args.model}/glm.projector")
+
+clip_tensors = [k for k, v in checkpoint.items() if k.startswith("vision.vit.model.vision_model.")]
+if len(clip_tensors) > 0:
+    clip = {name.replace("vision.vit.model.", ""): checkpoint[name].float() for name in clip_tensors}
+    torch.save(clip, f"{args.model}/glm.clip")
+
+    # added tokens should be removed to be able to convert Mistral models
+    if os.path.exists(f"{args.model}/added_tokens.json"):
+        with open(f"{args.model}/added_tokens.json", "w") as f:
+            f.write("{}\n")
+
+print("Done!")
+print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.")
+print(f"Also, use {args.model}glm.projector to prepare a glm-encoder.gguf file.")
diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp
index be6988540..59354aafa 100644
--- a/examples/llava/llava.cpp
+++ b/examples/llava/llava.cpp
@@ -299,6 +299,20 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
         clip_add_load_image_size(ctx_clip, load_image_size);
         LOG_INF("%s: load_image_size %d %d\n", __func__, load_image_size->width, load_image_size->height);
     }
+    else if (clip_is_glm(ctx_clip)){
+        struct clip_image_size * load_image_size = clip_image_size_init();
+        load_image_size->width = img_res_v.data[0].nx;
+        load_image_size->height = img_res_v.data[0].ny;
+        clip_add_load_image_size(ctx_clip, load_image_size);
+        
+        bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd);
+        int pos = int(load_image_size->width/clip_patch_size(ctx_clip)/2);
+        *n_img_pos = (pos * pos + 2);
+        if (!encoded) {
+            LOG_ERR("Unable to encode image \n");
+            return false;
+        }
+    }
     else if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) {
         // flat / default llava-1.5 type embedding
         *n_img_pos = clip_n_patches(ctx_clip);
@@ -383,6 +397,9 @@ bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, co
     if (clip_is_minicpmv(ctx_clip)) {
         num_max_patches = 10;
     }
+    if (clip_is_glm(ctx_clip)) {
+        num_max_patches = 1;
+    }
     float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*num_max_patches); // TODO: base on gridsize/llava model
     if (!image_embd) {
         LOG_ERR("Unable to allocate memory for image embeddings\n");