implment vision model architecture, gguf convertor

examples/llava/clip-impl.h

@@ -26,6 +26,8 @@
 #define KEY_HAS_QWEN2VL_MERGER  "clip.has_qwen2vl_merger"
 #define KEY_USE_GELU            "clip.use_gelu"
 #define KEY_USE_SILU            "clip.use_silu"
+#define KEY_USE_GLU_MLP         "clip.use_glu_mlp"
+#define KEY_USE_RMS_NORM        "clip.use_rms_norm"
 #define KEY_N_EMBD              "clip.%s.embedding_length"
 #define KEY_N_FF                "clip.%s.feed_forward_length"
 #define KEY_N_BLOCK             "clip.%s.block_count"
@@ -44,6 +46,8 @@
 #define KEY_MM_PATCH_MERGE_TYPE   "clip.vision.mm_patch_merge_type"
 #define KEY_IMAGE_GRID_PINPOINTS  "clip.vision.image_grid_pinpoints"
 #define KEY_IMAGE_CROP_RESOLUTION "clip.vision.image_crop_resolution"
+#define KEY_FULLATTN_BLK_IDX      "clip.vision.fullatt_block_indexes"
+#define KEY_ATTN_WINDOW_SIZE      "clip.vision.window_size"
 
 
 //
@@ -62,6 +66,7 @@
 #define TN_FFN_DOWN        "%s.blk.%d.ffn_down.%s"
 #define TN_FFN_GATE        "%s.blk.%d.ffn_gate.%s"
 #define TN_FFN_UP          "%s.blk.%d.ffn_up.%s"
+#define TN_FFN_GATE        "%s.blk.%d.ffn_gate.%s"
 #define TN_LN_1            "%s.blk.%d.ln1.%s"
 #define TN_LN_2            "%s.blk.%d.ln2.%s"
 #define TN_LN_PRE          "%s.pre_ln.%s"

examples/llava/clip.cpp

@@ -169,6 +169,7 @@ struct clip_hparams {
     std::vector<int32_t> image_grid_pinpoints;
     int32_t image_crop_resolution;
     std::unordered_set<int32_t> vision_feature_layer;
+    std::vector<int32_t> full_attn_layers;
 };
 
 struct clip_layer {
@@ -200,6 +201,9 @@ struct clip_layer {
     struct ggml_tensor * ff_down_w = nullptr;
     struct ggml_tensor * ff_down_b = nullptr;
 
+    struct ggml_tensor * ff_g_w = NULL;
+    struct ggml_tensor * ff_g_b = NULL;
+
     // layernorm 2
     struct ggml_tensor * ln_2_w = nullptr;
     struct ggml_tensor * ln_2_b = nullptr;
@@ -324,6 +328,9 @@ struct clip_ctx {
     float image_std[3];
     bool use_gelu = false;
     bool use_silu = false;
+    bool use_glu_mlp = false;
+    bool use_rms_norm = false;
+    int32_t ftype = 1;
 
     gguf_context_ptr ctx_gguf;
     ggml_context_ptr ctx_data;
@@ -810,6 +817,7 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
     const int n_head               = hparams.n_head;
     const int d_head               = hidden_size / n_head;
     const float eps                = hparams.eps;
+    const bool use_window_attn     = hparams.full_attn_layers.size() > 0;
     int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
 
     const int batch_size = imgs.entries.size();
@@ -862,8 +870,10 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
         // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp));
         inp = ggml_add(ctx0, inp, model.patch_bias);
     }
-    struct ggml_tensor * embeddings = inp;
+    struct ggml_tensor * embeddings  = inp;
-    struct ggml_tensor * pos_embed = nullptr;
+    struct ggml_tensor * pos_embed   = nullptr;
+    struct ggml_tensor * window_mask = nullptr;
+    struct ggml_tensor * window_idx  = nullptr;
 
     if (ctx->has_llava_projector) {
         // concat class_embeddings and patch_embeddings
@@ -915,6 +925,28 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
     const auto & vision_feature_layer = hparams.vision_feature_layer;
 
     // loop over layers
+
+    if (use_window_attn) {
+        window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions / 4);
+        ggml_set_name(window_idx, "window_idx");
+        ggml_set_input(window_idx);
+
+        // mask for window attention
+        window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, num_positions, num_positions);
+        ggml_set_name(window_mask, "window_mask");
+        ggml_set_input(window_mask);
+
+        // embeddings shape: [hidden_size, patches_w * patches_h, batch_size]
+        GGML_ASSERT(batch_size == 1);
+        embeddings = ggml_reshape_2d(ctx0, embeddings, hidden_size * 4, patches_w * patches_h * batch_size / 4);
+        embeddings = ggml_get_rows(ctx0, embeddings, window_idx);
+        embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size, patches_w * patches_h, batch_size);
+
+        positions = ggml_reshape_2d(ctx0, positions, 16, num_position_ids / 4 / 4);
+        positions = ggml_get_rows(ctx0, positions, window_idx);
+        positions = ggml_reshape_1d(ctx0, positions, num_position_ids);
+    }
+
     for (int il = 0; il < ctx->max_feature_layer; il++) {
         struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states
 
@@ -927,9 +959,12 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
         //const size_t nb_q_w = model.layers[il].q_w->nb[0];
 
         // layernorm1
-        {
+        if (ctx->use_rms_norm) {
+            cur = ggml_rms_norm(ctx0, cur, eps);
+            cur = ggml_mul(ctx0, cur, model.layers[il].ln_1_w);
+        }
+        else {
             cur = ggml_norm(ctx0, cur, eps);
-
             cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_1_w),
                            model.layers[il].ln_1_b);
         }
@@ -969,7 +1004,14 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
             V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size);
 
             struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
-            KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f);
+            const bool inlist = std::find(hparams.full_attn_layers.begin(), hparams.full_attn_layers.end(), il) != hparams.full_attn_layers.end();
+            const bool full_attn = use_window_attn ? inlist : true;
+            if (full_attn) {
+                KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f);
+            } else {
+                KQ = ggml_soft_max_ext(ctx0, KQ, window_mask, 1.0f, 0.0f);
+            }
+
             struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ);
             KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size);
             KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
@@ -986,25 +1028,50 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
         embeddings = cur; // embeddings = residual, cur = hidden_states
 
         // layernorm2
-        {
+        if (ctx->use_rms_norm) {
+            cur = ggml_rms_norm(ctx0, cur, eps);
+            cur = ggml_mul(ctx0, cur, model.layers[il].ln_2_w);
+        } else {
             cur = ggml_norm(ctx0, cur, eps);
-
             cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_2_w), model.layers[il].ln_2_b);
         }
 
-        cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
+        // mlp
-        cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b);
+        if (ctx->use_glu_mlp) {
+            // ffn_up
+            auto cur_up = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
+            cur_up = ggml_add(ctx0, cur_up, model.layers[il].ff_o_b);
 
-        if (ctx->use_gelu) {
+            auto cur_gate = ggml_mul_mat(ctx0, model.layers[il].ff_g_w, cur);
-            cur = ggml_gelu_inplace(ctx0, cur);
+            cur_gate = ggml_add(ctx0, cur_gate, model.layers[il].ff_g_b);
-        } else if (ctx->use_silu) {
+            if (ctx->use_gelu) {
-            cur = ggml_silu_inplace(ctx0, cur);
+                cur_gate = ggml_gelu_inplace(ctx0, cur_gate);
-        } else {
+            } else if (ctx->use_silu) {
-            cur = ggml_gelu_quick_inplace(ctx0, cur);
+                cur_gate = ggml_silu_inplace(ctx0, cur_gate);
+            } else {
+                cur_gate = ggml_gelu_quick_inplace(ctx0, cur_gate);
+            }
+            cur = ggml_mul(ctx0, cur_gate, cur_up);
+
+            // ffn_down
+            cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
+            cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b);
         }
+        else {
+            cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
+            cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b);
 
-        cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
+            if (ctx->use_gelu) {
-        cur = ggml_add(ctx0, cur, model.layers[il].ff_o_b);
+                cur = ggml_gelu_inplace(ctx0, cur);
+            } else if (ctx->use_silu) {
+                cur = ggml_silu_inplace(ctx0, cur);
+            } else {
+                cur = ggml_gelu_quick_inplace(ctx0, cur);
+            }
+
+            cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
+            cur = ggml_add(ctx0, cur, model.layers[il].ff_o_b);
+        }
 
         // residual 2
         cur = ggml_add(ctx0, embeddings, cur);
@@ -1014,10 +1081,17 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
 
     // post-layernorm
     if (model.post_ln_w) {
-        embeddings = ggml_norm(ctx0, embeddings, eps);
+        if (ctx->use_rms_norm) {
-        ggml_set_name(embeddings, "post_ln");
+            embeddings = ggml_rms_norm(ctx0, embeddings, eps);
+            ggml_set_name(embeddings, "post_ln");
 
-        embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b);
+            embeddings = ggml_mul(ctx0, embeddings, model.post_ln_w);
+        } else {
+            embeddings = ggml_norm(ctx0, embeddings, eps);
+            ggml_set_name(embeddings, "post_ln");
+
+            embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b);
+        }
     }
 
     // final layer is a vision feature layer
@@ -1331,6 +1405,18 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
         embeddings = ggml_add(ctx0, embeddings, model.mm_1_b);
     }
 
+    if (use_window_attn) {
+        struct ggml_tensor * inv_window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions / 4);
+        ggml_set_name(inv_window_idx, "inv_window_idx");
+        ggml_set_input(inv_window_idx);
+
+        // embeddings shape: [hidden_size, patches_w * patches_h, batch_size]
+        GGML_ASSERT(batch_size == 1);
+        embeddings = ggml_reshape_2d(ctx0, embeddings, hparams.projection_dim, patches_w * patches_h / 4);
+        embeddings = ggml_get_rows(ctx0, embeddings, inv_window_idx);
+        embeddings = ggml_reshape_3d(ctx0, embeddings, hparams.projection_dim, patches_w * patches_h / 4, batch_size);
+    }
+
     // build the graph
     ggml_build_forward_expand(gf, embeddings);
 
@@ -1447,6 +1533,8 @@ struct clip_model_loader {
 
             get_bool(KEY_USE_GELU, ctx_clip.use_gelu, false);
             get_bool(KEY_USE_SILU, ctx_clip.use_silu, false);
+            get_bool(KEY_USE_GLU_MLP, ctx_clip.use_glu_mlp, false);
+            get_bool(KEY_USE_RMS_NORM, ctx_clip.use_rms_norm, false);
 
             get_u32(string_format(KEY_N_EMBD,         "vision"), hparams.hidden_size);
             get_u32(string_format(KEY_N_HEAD,         "vision"), hparams.n_head);
@@ -1458,6 +1546,7 @@ struct clip_model_loader {
             get_u32(KEY_PATCH_SIZE, hparams.patch_size);
             get_u32(KEY_IMAGE_CROP_RESOLUTION, hparams.image_crop_resolution, false);
             get_arr_int(KEY_IMAGE_GRID_PINPOINTS, hparams.image_grid_pinpoints, false);
+            get_arr_int(KEY_FULLATTN_BLK_IDX, hparams.full_attn_layers, false);
 
             {
                 std::string mm_patch_merge_type;
@@ -1603,8 +1692,10 @@ struct clip_model_loader {
             // legacy naming (the in and out is reversed! don't ask me why)
             layer.ff_i_w = layer.ff_down_w;
             layer.ff_o_w = layer.ff_up_w;
+            layer.ff_g_w = layer.ff_gate_b;
             layer.ff_i_b = layer.ff_down_b;
             layer.ff_o_b = layer.ff_up_b;
+            layer.ff_g_b = layer.ff_gate_b;
         }
 
         switch (ctx_clip.proj_type) {

examples/llava/qwen2_vl_surgery.py

@@ -5,10 +5,12 @@ import torch
 import numpy as np
 from gguf import *
 from transformers import (
-    Qwen2VLForConditionalGeneration,
-    Qwen2VLProcessor,
     AutoProcessor,
-    Qwen2VLConfig
+    Qwen2VLForConditionalGeneration,
+    Qwen2_5_VLForConditionalGeneration,
+    Qwen2VLProcessor,
+    Qwen2VLConfig,
+    Qwen2_5_VLConfig,
 )
 
 
@@ -18,62 +20,80 @@ VISION = "clip.vision"
 def k(raw_key: str, arch: str) -> str:
     return raw_key.format(arch=arch)
 
+class VL2:
 
-def to_gguf_name(name: str) -> str:
+    @staticmethod
-    og = name
+    def to_gguf_name(name: str) -> str:
-    name = name.replace("text_model", "t").replace("vision_model", "v")
+        og = name
-    name = name.replace("blocks", "blk").replace("embeddings.", "")
+        name = name.replace("text_model", "t").replace("vision_model", "v")
-    name = name.replace("attn.", "attn_")
+        name = name.replace("blocks", "blk").replace("embeddings.", "")
-    name = name.replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("proj.", "out.")
+        name = name.replace("attn.", "attn_")
-    # name = name.replace("layrnorm", "ln").replace("layer_norm", "ln").replace("layernorm", "ln")
+        name = name.replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("proj.", "out.")
-    name = name.replace("norm1", "ln1").replace("norm2", "ln2")
+        # name = name.replace("layrnorm", "ln").replace("layer_norm", "ln").replace("layernorm", "ln")
-    name = name.replace("merger.mlp", 'mm')
+        name = name.replace("norm1", "ln1").replace("norm2", "ln2")
-    print(f"[to_gguf_name] {og} --> {name}")
+        name = name.replace("merger.mlp", 'mm')
-    return name
+        print(f"[to_gguf_name] {og} --> {name}")
+        return name
 
-
+    @classmethod
-def find_vision_tensors(qwen2vl, dtype) -> Dict[str, np.ndarray]:
+    def find_vision_tensors(cls, qwen2vl, dtype) -> Dict[str, np.ndarray]:
-    vision_model = qwen2vl.visual
+        vision_model = qwen2vl.visual
-    tensor_map = {}
+        tensor_map = {}
-    for name, ten in vision_model.state_dict().items():
+        for name, ten in vision_model.state_dict().items():
-        ten = ten.numpy()
+            ten = ten.numpy()
-        if 'qkv' in name:
+            if 'qkv' in name:
-            if ten.ndim == 2: # weight
+                if ten.ndim == 2: # weight
-                c3, _ = ten.shape
+                    c3, _ = ten.shape
-            else:             # bias
+                else:             # bias
-                c3 = ten.shape[0]
+                    c3 = ten.shape[0]
-            assert c3 % 3 == 0
+                assert c3 % 3 == 0
-            c = c3 // 3
+                c = c3 // 3
-            wq = ten[:c]
+                wq = ten[:c]
-            wk = ten[c: c * 2]
+                wk = ten[c: c * 2]
-            wv = ten[c * 2:]
+                wv = ten[c * 2:]
-            tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "q")] = wq
+                tensor_map[cls.to_gguf_name(f"vision_model.{name}").replace("qkv", "q")] = wq
-            tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "k")] = wk
+                tensor_map[cls.to_gguf_name(f"vision_model.{name}").replace("qkv", "k")] = wk
-            tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "v")] = wv
+                tensor_map[cls.to_gguf_name(f"vision_model.{name}").replace("qkv", "v")] = wv
-        elif 'merger' in name:
+            elif 'merger' in name:
-            if name.endswith("ln_q.weight"):
+                if name.endswith("ln_q.weight"):
-                tensor_map['v.post_ln.weight'] = ten
+                    tensor_map['v.post_ln.weight'] = ten
-            elif name.endswith("ln_q.bias"):
+                elif name.endswith("ln_q.bias"):
-                tensor_map['v.post_ln.bias'] = ten
+                    tensor_map['v.post_ln.bias'] = ten
+                else:
+                    # "merger.mlp.%d.weight/bias" --> "mm.%d.weight/bias"
+                    tensor_map[cls.to_gguf_name(name)] = ten
+            elif 'patch_embed.proj.weight' in name:
+                # NOTE: split Conv3D into Conv2Ds
+                c1, c2, kt, kh, kw = ten.shape
+                assert kt == 2, "Current implmentation only support temporal_patch_size of 2"
+                tensor_map["v.patch_embd.weight"] = ten[:, :, 0, ...]
+                tensor_map["v.patch_embd.weight.1"] = ten[:, :, 1, ...]
             else:
-                # "merger.mlp.%d.weight/bias" --> "mm.%d.weight/bias"
+                tensor_map[cls.to_gguf_name(f"vision_model.{name}")] = ten
-                tensor_map[to_gguf_name(name)] = ten
-        elif 'patch_embed.proj.weight' in name:
-            # NOTE: split Conv3D into Conv2Ds
-            c1, c2, kt, kh, kw = ten.shape
-            assert kt == 2, "Current implmentation only support temporal_patch_size of 2"
-            tensor_map["v.patch_embd.weight"] = ten[:, :, 0, ...]
-            tensor_map["v.patch_embd.weight.1"] = ten[:, :, 1, ...]
-        else:
-            tensor_map[to_gguf_name(f"vision_model.{name}")] = ten
 
-    for new_name, ten in tensor_map.items():
+        for new_name, ten in tensor_map.items():
-        if ten.ndim <= 1 or new_name.endswith("_norm.weight"):
+            if ten.ndim <= 1 or new_name.endswith("_norm.weight"):
-            tensor_map[new_name] = ten.astype(np.float32)
+                tensor_map[new_name] = ten.astype(np.float32)
-        else:
+            else:
-            tensor_map[new_name] = ten.astype(dtype)
+                tensor_map[new_name] = ten.astype(dtype)
-    tensor_map["v.position_embd.weight"] = np.zeros([10, 10], dtype=np.float32)  # dummy tensor, just here as a placeholder
+        tensor_map["v.position_embd.weight"] = np.zeros([10, 10], dtype=np.float32)  # dummy tensor, just here as a placeholder
-    return tensor_map
+        return tensor_map
+
+
+class VL25(VL2):
+
+    @staticmethod
+    def to_gguf_name(name: str) -> str:
+        og = name
+        name = name.replace("text_model", "t").replace("vision_model", "v")
+        name = name.replace("blocks", "blk").replace("embeddings.", "")
+        name = name.replace("attn.", "attn_")
+        name = name.replace("mlp.down_proj", "ffn_down").replace("mlp.up_proj", "ffn_up")
+        name = name.replace("mlp.gate_proj", "ffn_gate").replace("proj.", "out.")
+        name = name.replace("norm1", "ln1").replace("norm2", "ln2")
+        name = name.replace("merger.mlp", 'mm')
+        print(f"[vl25][to_gguf_name] {og} --> {name}")
+        return name
 
 
 def main(args):
@@ -92,11 +112,18 @@ def main(args):
     model_path = ""
     model_name = args.model_name
     print("model_name: ", model_name)
-    qwen2vl = Qwen2VLForConditionalGeneration.from_pretrained(
+    if args.model_type == "qwen2vl":
-        model_name, torch_dtype=dtype, device_map="cpu"
+        qwen2vl = Qwen2VLForConditionalGeneration.from_pretrained(
-    )
+            model_name, torch_dtype=dtype, device_map="cpu"
-    cfg: Qwen2VLConfig = qwen2vl.config  # type: ignore[reportAssignmentType]
+        )
-    vcfg = cfg.vision_config
+        cfg: Qwen2VLConfig = qwen2vl.config  # type: ignore[reportAssignmentType]
+        vcfg = cfg.vision_config
+    else:
+        qwen2vl = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+            model_name, torch_dtype=dtype, device_map="cpu"
+        )
+        cfg: Qwen2_5_VLConfig = qwen2vl.config  # type: ignore[reportAssignmentType]
+        vcfg = cfg.vision_config
 
     if os.path.isdir(model_name):
         local_model = True
@@ -125,14 +152,26 @@ def main(args):
     else:
         raise ValueError()
 
-    tensor_map = find_vision_tensors(qwen2vl, np_dtype)
+    if args.model_type == "qwen2.5vl":
+        fout.add_bool("clip.use_glu_mlp", True)  # gate linear unit MLP layer in vision model
+        fout.add_bool("clip.use_rms_norm", True)
+        fout.add_array("clip.vision.fullatt_block_indexes", vcfg.fullatt_block_indexes)
+        fout.add_uint32("clip.vision.window_size", vcfg.window_size)
+        fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.hidden_size)
+        fout.add_uint32("clip.vision.projection_dim", vcfg.out_hidden_size)
+    else:
+        fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.embed_dim)
+        fout.add_uint32("clip.vision.projection_dim", vcfg.hidden_size)
+
+    if args.model_type == "qwen2.5vl":
+        tensor_map = VL25.find_vision_tensors(qwen2vl, np_dtype)
+    else:
+        tensor_map = VL2.find_vision_tensors(qwen2vl, np_dtype)
     for name, data in tensor_map.items():
         fout.add_tensor(name, data)
 
     fout.add_uint32("clip.vision.patch_size", vcfg.patch_size)
     fout.add_uint32("clip.vision.image_size", 14 * 40)  # some reasonable size that is divable by (14*2)
-    fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.embed_dim)
-    fout.add_uint32("clip.vision.projection_dim", vcfg.hidden_size)
     fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), vcfg.num_heads)
     fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
     fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), vcfg.depth)
@@ -160,6 +199,7 @@ def main(args):
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("model_name", nargs='?', default="Qwen/Qwen2-VL-2B-Instruct")
+    parser.add_argument("--model_type", nargs='?', choices=['qwen2vl', 'qwen2.5vl'], default="qwen2vl")
     parser.add_argument("--data_type", nargs='?', choices=['fp32', 'fp16'], default="fp32")
     args = parser.parse_args()
     main(args)