diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index a54cce887b..11dd9f610a 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -563,6 +563,10 @@ class ModelBase:
                             gguf.MODEL_TENSOR.A_ENC_EMBD_POS,
                             gguf.MODEL_TENSOR.ALTUP_CORRECT_COEF,
                             gguf.MODEL_TENSOR.ALTUP_PREDICT_COEF,
+                            # Kimi KDA conv weights should be F32
+                            gguf.MODEL_TENSOR.SSM_CONV1D_Q,
+                            gguf.MODEL_TENSOR.SSM_CONV1D_K,
+                            gguf.MODEL_TENSOR.SSM_CONV1D_V,
                         )
                     )
                     or new_name[-7:] not in (".weight", ".lora_a", ".lora_b")
@@ -4976,6 +4980,295 @@ class CodeShellModel(TextModel):
         self.gguf_writer.add_rope_scaling_factor(1.0)
 
 
+@ModelBase.register("KimiLinearModel", "KimiLinearForCausalLM")
+class KimiLinearModel(TextModel):
+    """Kimi-Linear model with hybrid MLA+KDA architecture"""
+    model_arch = gguf.MODEL_ARCH.KIMI_LINEAR
+    
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def set_gguf_parameters(self):
+        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
+        
+        # Use find_hparam for context length
+        # Kimi uses model_max_length
+        n_ctx = self.find_hparam(["max_position_embeddings", "model_max_length", "n_ctx", "n_positions"], optional=True)
+        if n_ctx is not None:
+            self.gguf_writer.add_context_length(n_ctx)
+        else:
+            # Default to 4096 if not found
+            logger.warning("No context length found in config, defaulting to 4096")
+            self.gguf_writer.add_context_length(4096)
+        
+        self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
+        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
+        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
+        self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+        # KDA & MLA params
+        # Get ssm_d_conv from linear_attn_config.short_conv_kernel_size or ssm_d_conv
+        linear_attn_config = self.hparams.get("linear_attn_config", {})
+        ssm_d_conv = self.hparams.get("ssm_d_conv") or linear_attn_config.get("short_conv_kernel_size")
+        if ssm_d_conv is not None:
+             self.gguf_writer.add_ssm_conv_kernel(ssm_d_conv)
+        
+        # MLA params - use add_* methods that handle arch substitution
+        # Support both HuggingFace naming (q_lora_rank, kv_lora_rank) and internal naming (n_lora_q, n_lora_kv)
+        q_lora_rank = self.hparams.get("q_lora_rank", self.hparams.get("n_lora_q"))
+        kv_lora_rank = self.hparams.get("kv_lora_rank", self.hparams.get("n_lora_kv"))
+        
+        if q_lora_rank is not None:
+             self.gguf_writer.add_q_lora_rank(q_lora_rank)
+        if kv_lora_rank is not None:
+             self.gguf_writer.add_kv_lora_rank(kv_lora_rank)
+        
+        # MLA head dimensions
+        # Support HuggingFace naming: qk_nope_head_dim, qk_rope_head_dim, v_head_dim
+        qk_nope_head_dim = self.hparams.get("qk_nope_head_dim")
+        qk_rope_head_dim = self.hparams.get("qk_rope_head_dim", self.hparams.get("n_rot"))
+        v_head_dim = self.hparams.get("v_head_dim")
+        
+        # Calculate n_embd_head_k_mla = qk_nope_head_dim + qk_rope_head_dim
+        if "n_embd_head_k_mla" in self.hparams:
+             self.gguf_writer.add_key_length_mla(self.hparams["n_embd_head_k_mla"])
+        elif qk_nope_head_dim is not None and qk_rope_head_dim is not None:
+             n_embd_head_k_mla = qk_nope_head_dim + qk_rope_head_dim
+             self.gguf_writer.add_key_length_mla(n_embd_head_k_mla)
+        
+        # n_embd_head_v_mla = v_head_dim
+        if "n_embd_head_v_mla" in self.hparams:
+             self.gguf_writer.add_value_length_mla(self.hparams["n_embd_head_v_mla"])
+        elif v_head_dim is not None:
+             self.gguf_writer.add_value_length_mla(v_head_dim)
+        
+        # Rotation - use qk_rope_head_dim for Kimi
+        rope_dim = self.hparams.get("qk_rope_head_dim") or self.hparams.get("n_rot")
+        if rope_dim is not None:
+             self.gguf_writer.add_rope_dimension_count(rope_dim)
+        else:
+             # Default to head_dim
+             head_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+             self.gguf_writer.add_rope_dimension_count(head_dim)
+
+        self.gguf_writer.add_rope_freq_base(self.hparams.get("rope_theta", 10000.0))
+
+        # MoE params
+        n_experts = self.hparams.get("num_local_experts", self.hparams.get("num_experts"))
+        if n_experts is not None:
+            self.gguf_writer.add_expert_count(n_experts)
+        # Support both num_experts_per_tok and num_experts_per_token
+        n_experts_used = self.hparams.get("num_experts_per_tok", self.hparams.get("num_experts_per_token"))
+        if n_experts_used is not None:
+            self.gguf_writer.add_expert_used_count(n_experts_used)
+        
+        # moe_intermediate_size (1024 for Kimi)
+        moe_intermediate_size = self.hparams.get("moe_intermediate_size")
+        if moe_intermediate_size is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+        
+        # num_shared_experts (1 for Kimi)
+        num_shared_experts = self.hparams.get("num_shared_experts")
+        if num_shared_experts is not None:
+            self.gguf_writer.add_expert_shared_count(num_shared_experts)
+        
+        # first_k_dense_replace (1 for Kimi - first layer uses dense MLP)
+        first_k_dense_replace = self.hparams.get("first_k_dense_replace")
+        if first_k_dense_replace is not None:
+            self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace)
+        
+        # Expert gating function (sigmoid for Kimi)
+        moe_router_activation_func = self.hparams.get("moe_router_activation_func", "sigmoid")
+        if moe_router_activation_func == "sigmoid":
+            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+        elif moe_router_activation_func == "softmax":
+            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
+        else:
+            logger.warning(f"Unknown moe_router_activation_func: {moe_router_activation_func}, defaulting to sigmoid")
+            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+        
+        # Routed scaling factor (expert_weights_scale = 2.446 for Kimi)
+        routed_scaling_factor = self.hparams.get("routed_scaling_factor")
+        if routed_scaling_factor is not None:
+            self.gguf_writer.add_expert_weights_scale(routed_scaling_factor)
+
+    def set_vocab(self):
+        # Kimi uses TikToken tokenizer - load via transformers
+        from transformers import AutoTokenizer
+        
+        dir_model = self.dir_model
+        vocab_size = self.hparams["vocab_size"]
+        
+        logger.info(f"Loading TikToken tokenizer from {dir_model}")
+        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
+        
+        tokens: list[str] = []
+        toktypes: list[int] = []
+        
+        # Get tokenizer pre string
+        tokpre = self.get_vocab_base_pre(tokenizer)
+        
+        # Build vocab from tokenizer
+        merges = []
+        vocab = {}
+        
+        # TikToken stores vocab in mergeable_ranks
+        if hasattr(tokenizer, 'mergeable_ranks'):
+            mergeable_ranks = tokenizer.mergeable_ranks
+            for token, rank in mergeable_ranks.items():
+                vocab[self._token_bytes_to_string(token)] = rank
+                if len(token) == 1:
+                    continue
+                # Build merges
+                merged = self._bpe(mergeable_ranks, token, max_rank=rank)
+                if len(merged) == 2:
+                    merges.append(' '.join(map(self._token_bytes_to_string, merged)))
+        else:
+            # Fallback: get vocab directly
+            vocab = {tok: idx for tok, idx in tokenizer.get_vocab().items()}
+        
+        # Get special tokens
+        added_vocab = {}
+        if hasattr(tokenizer, 'special_tokens'):
+            added_vocab = tokenizer.special_tokens
+        elif hasattr(tokenizer, 'added_tokens_encoder'):
+            added_vocab = tokenizer.added_tokens_encoder
+        
+        # Combine vocab
+        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()}
+        
+        for i in range(vocab_size):
+            if i not in reverse_vocab:
+                tokens.append(f"[PAD{i}]")
+                toktypes.append(gguf.TokenType.UNUSED)
+            elif i in added_vocab.values() if added_vocab else False:
+                tokens.append(reverse_vocab[i])
+                toktypes.append(gguf.TokenType.CONTROL)
+            else:
+                tokens.append(reverse_vocab[i])
+                toktypes.append(gguf.TokenType.NORMAL)
+        
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+        
+        special_vocab = gguf.SpecialVocab(dir_model, load_merges=False)
+        special_vocab.merges = merges
+        special_vocab.add_to_gguf(self.gguf_writer)
+        logger.info(f"Loaded {len(tokens)} tokens, {len(merges)} merges")
+    
+    @staticmethod
+    def _token_bytes_to_string(b: bytes) -> str:
+        """Convert bytes to string representation for tokenizer"""
+        return ''.join([chr(byte) if byte < 128 else f'<0x{byte:02X}>' for byte in b])
+    
+    @staticmethod  
+    def _bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]:
+        """BPE tokenization for merges extraction"""
+        parts = [bytes([b]) for b in token]
+        while True:
+            min_idx = None
+            min_rank = None
+            for i, pair in enumerate(zip(parts[:-1], parts[1:])):
+                rank = mergeable_ranks.get(pair[0] + pair[1])
+                if rank is not None and (min_rank is None or rank < min_rank):
+                    min_idx = i
+                    min_rank = rank
+            if min_rank is None or (max_rank is not None and min_rank >= max_rank):
+                break
+            parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:]
+        return parts
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+        if self._experts is not None:
+             experts = [k for d in self._experts for k in d.keys()]
+             if len(experts) > 0:
+                 raise ValueError(f"Unprocessed experts: {experts}")
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        logger.info(f"Processing {name}: shape before = {tuple(data_torch.shape)}")
+        
+        # Handle KDA conv1d weights
+        # HuggingFace/vLLM stores as [d_inner, d_conv] (2D), memory layout: conv_step changes fastest
+        # llama.cpp expects ggml ne = [d_conv, 1, d_inner, 1], memory layout: ne[0]=d_conv changes fastest
+        # GGUF reverses numpy shape when writing, so numpy (1, d_inner, 1, d_conv) -> ggml ne = [d_conv, 1, d_inner, 1]
+        # Memory layouts match: both have conv_step (d_conv) changing fastest
+        if name.endswith((".q_conv1d.weight", ".k_conv1d.weight", ".v_conv1d.weight")):
+            # HF shape: [d_inner, d_conv] e.g. [4096, 4]
+            # Target numpy shape: (1, d_inner, 1, d_conv) -> ggml ne = [d_conv, 1, d_inner, 1]
+            if data_torch.ndim == 2:
+                d_inner, d_conv = data_torch.shape
+                # Reshape to (1, d_inner, 1, d_conv) - memory layout preserved (d_conv fastest)
+                data_torch = data_torch.reshape(1, d_inner, 1, d_conv)
+                logger.info(f"Reshaped conv1d weight {name}: [d_inner={d_inner}, d_conv={d_conv}] -> numpy {tuple(data_torch.shape)} -> ggml ne=[{d_conv}, 1, {d_inner}, 1]")
+            elif data_torch.ndim == 3:
+                # Already 3D [d_inner, 1, d_conv] from unsqueeze
+                d_inner, _, d_conv = data_torch.shape
+                data_torch = data_torch.reshape(1, d_inner, 1, d_conv)
+                logger.info(f"Reshaped conv1d weight {name}: [d_inner={d_inner}, 1, d_conv={d_conv}] -> numpy {tuple(data_torch.shape)} -> ggml ne=[{d_conv}, 1, {d_inner}, 1]")
+        
+        # Handle A_log: HF stores as [1, 1, num_heads, 1]
+        # llama.cpp expects ggml ne = [1, num_heads, 1, 1]
+        # GGUF reverses numpy shape: numpy (1, 1, num_heads, 1) -> ggml ne = [1, num_heads, 1, 1]
+        # So no transformation needed! The shapes already match after GGUF reversal.
+        if name.endswith(".A_log"):
+            if data_torch.ndim == 4:
+                logger.info(f"A_log {name}: numpy {tuple(data_torch.shape)} -> ggml ne={list(reversed(data_torch.shape))}")
+        
+        # Kimi specific bias
+        if name.endswith("block_sparse_moe.gate.e_score_correction_bias"):
+             new_name = self.format_tensor_name(gguf.MODEL_TENSOR.FFN_EXP_PROBS_B, bid)
+             return [(new_name, data_torch)]
+
+        # process the experts separately
+        if name.find("block_sparse_moe.experts") != -1:
+            n_experts = self.hparams.get("num_local_experts", self.hparams.get("num_experts"))
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                tensors = []
+                # w1: gate, w2: down, w3: up
+                for wid, tname in [("w1", gguf.MODEL_TENSOR.FFN_GATE_EXP), 
+                                   ("w2", gguf.MODEL_TENSOR.FFN_DOWN_EXP), 
+                                   ("w3", gguf.MODEL_TENSOR.FFN_UP_EXP)]:
+                    datas: list[Tensor] = []
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+                    new_name = self.format_tensor_name(tname, bid)
+                    tensors.append((new_name, data_torch))
+                return tensors
+            return []
+        
+        mapped_name = self.map_tensor_name(name)
+        logger.info(f"Returning {mapped_name}: shape after = {tuple(data_torch.shape)}")
+        return [(mapped_name, data_torch)]
+
+    def get_vocab_base(self) -> tuple[list[str], list[int], str]:
+        # This method is not used when set_vocab is overridden
+        # But adding it for completeness in case it's called elsewhere
+        logger.warning("get_vocab_base called, but set_vocab is already overridden")
+        vocab_size = self.hparams.get("vocab_size", 100)
+        tokens = [f"<token_{i}>" for i in range(vocab_size)]
+        tokens[0] = "<unk>"
+        tokens[1] = "<s>"
+        tokens[2] = "</s>"
+        toktypes = [gguf.TokenType.NORMAL] * vocab_size
+        return tokens, toktypes, "gpt-2"
+
 @ModelBase.register("InternLM2ForCausalLM")
 class InternLM2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.INTERNLM2
diff --git a/src/models/models.h b/src/models/models.h
index d93601ad06..8b7af8d7bc 100644
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -283,6 +283,12 @@ struct llm_build_jamba : public llm_graph_context_mamba {
     llm_build_jamba(const llama_model & model, const llm_graph_params & params);
 };
 
+struct llm_build_kimi_linear : public llm_graph_context_mamba {
+    llm_build_kimi_linear(const llama_model & model, const llm_graph_params & params);
+private:
+    const llama_model & model;
+};
+
 struct llm_build_lfm2 : public llm_graph_context {
     const llama_model & model;