merge resolved

- fixed issues in convert
- tested several deepseek models

convert_hf_to_gguf.py

@@ -7519,9 +7519,6 @@ class DeepseekV2Model(TextModel):
             first_k_dense_replace = hparams["num_hidden_layers"] if not has_moe else 0
         self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace)
         kv_lora_rank = hparams["kv_lora_rank"] if hparams.get("kv_lora_rank") is not None else 512
-        routed_scaling_factor = hparams.get("routed_scaling_factor", 1.0)
-        norm_topk_prob = hparams.get("norm_topk_prob", False)
-        self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
         self.gguf_writer.add_vocab_size(hparams["vocab_size"])
         if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None:
             self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"])
@@ -7534,7 +7531,6 @@ class DeepseekV2Model(TextModel):
             self.gguf_writer.add_value_length(kv_lora_rank)
             self.gguf_writer.add_key_length_mla(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
             self.gguf_writer.add_value_length_mla(hparams["v_head_dim"])
-            self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
 
         # MoE parameters (required by C++ code for DEEPSEEK2 arch)
         # For non-MoE models like Youtu, use intermediate_size as expert_feed_forward_length
@@ -7554,11 +7550,6 @@ class DeepseekV2Model(TextModel):
 
         if (norm_topk_prob := hparams.get("norm_topk_prob")) is not None and norm_topk_prob:
             self.gguf_writer.add_expert_weights_norm(norm_topk_prob)
-        self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
-        self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
-        self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
-        self.gguf_writer.add_expert_weights_scale(routed_scaling_factor)
-        self.gguf_writer.add_expert_weights_norm(norm_topk_prob)
 
         self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
 

gguf-py/gguf/tensor_mapping.py

@@ -1312,7 +1312,7 @@ class TensorNameMap:
             "vision_model.positional_embedding_vlm", # llama 4
             "vision_tower.patch_embed.pos_emb", # kimi-vl
             "visual.pos_embed", # qwen3vl
-            "model.vision.patch_embedding.position_embedding",  # cogvlm
+            "model.vision.patch_embedding.position_embedding", # cogvlm
             "visual.embeddings.position_embedding", # glm4v
         ),
 

src/llama-model.cpp

@@ -4917,7 +4917,6 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
             case LLM_ARCH_DEEPSEEK2OCR:
                 {
                     // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B
-                    const bool is_lite = (hparams.n_layer == 27 || hparams.n_layer == 26);
                     const bool is_ocr = (arch == LLM_ARCH_DEEPSEEK2OCR);
 
                     const bool is_mla = hparams.is_mla();

src/models/deepseek2.cpp

@@ -3,8 +3,7 @@
 llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B
-    bool is_lite = (hparams.n_layer == 27 || hparams.n_layer == 26);
-    bool is_ocr = (model.arch == LLM_ARCH_DEEPSEEK2OCR);
+    bool is_ocr = model.arch == LLM_ARCH_DEEPSEEK2OCR;
 
     const bool is_mla = hparams.is_mla();
 
@@ -83,7 +82,7 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
             cb(Qcur, "q_pe", il);
             cb(Kcur, "k_pe", il);
 
-            cur = build_attn(inp_attn,
+            cur = build_attn(inp_attn_kv,
                         model.layers[il].wo, NULL,
                         Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
             cb(cur, "attn_out", il);

tools/mtmd/clip.cpp

@@ -4180,37 +4180,6 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
         return false;
     }
 
-    // print debug nodes
-    if (ctx->debug_graph) {
-        LOG_INF("\n\n---\n\n");
-        LOG_INF("\n\nDebug graph:\n\n");
-        for (ggml_tensor * t : ctx->debug_print_tensors) {
-            std::vector<uint8_t> data(ggml_nbytes(t));
-            ggml_backend_tensor_get(t, data.data(), 0, ggml_nbytes(t));
-            print_tensor_info(t);
-            print_tensor_shape(t);
-            print_tensor_sum(t, data.data(), 3);
-            std::string tname_s = std::string(t->name);
-
-            bool is_stored = false;
-            std::vector<std::string> patterns = {
-                /* Add tensor names here to dump (e.g. "sam_output") */
-            };
-
-            for (auto & p : patterns) {
-                if (tname_s == p) {
-                    save_tensor_to_file(t, data.data());
-                    is_stored = true;
-                    break;
-                }
-            }
-
-            if (!is_stored) {
-                print_tensor_data(t, data.data(), 3);
-            }
-        }
-    }
-
     // the last node is the embedding tensor
     ggml_tensor * embeddings = ggml_graph_node(gf, -1);
 

tools/mtmd/tests/test-deepseek-ocr.py

@@ -83,9 +83,9 @@ def read_expected_output(file_path: str) -> str:
 
 def main():
     ap = argparse.ArgumentParser(description="Compare llama.cpp and HuggingFace DeepSeek-OCR outputs")
-    ap.add_argument("--llama-model", default="gguf_models/deepseek-ai/deepseek-ocr-f16.gguf",
+    ap.add_argument("--llama-model", default="gguf_models/deepseek-ai/deepseek-ocr-q8_0_test.gguf",
                     help="Path to llama.cpp GGUF model")
-    ap.add_argument("--mmproj", default="gguf_models/deepseek-ai/mmproj-deepseek-ocr-f16.gguf",
+    ap.add_argument("--mmproj", default="gguf_models/deepseek-ai/mmproj-deepseek-ocr-f16_test.gguf",
                     help="Path to mmproj GGUF file")
     ap.add_argument("--image", default="test-1.jpeg",
                     help="Path to test image")