if chkhsh == "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c":
# ref: https://huggingface.co/Qwen/Qwen3-Embedding-0.6B
res = "qwen2"
+ if chkhsh == "1444df51289cfa8063b96f0e62b1125440111bc79a52003ea14b6eac7016fd5f":
+ # ref: https://huggingface.co/openbmb/MiniCPM-V-4_6
+ res = "qwen35"
if chkhsh == "66b8d4e19ab16c3bfd89bce5d785fb7e0155e8648708a1f42077cb9fe002c273":
# ref: https://huggingface.co/alvarobartt/grok-2-tokenizer
res = "grok-2"
yield from super().modify_tensors(data_torch, name, bid)
+class _Qwen35MRopeMixin:
+ # Qwen3.5 always applies interleaved MRoPE (see Qwen3_5RotaryEmbedding in transformers);
+ # the upstream default mrope_section is [11, 11, 10] and llama.cpp's QWEN35 / QWEN35MOE
+ # loaders treat qwen35.rope.dimension_sections as required, so make sure it is always
+ # written even when a particular checkpoint omits the field in `rope_parameters`.
+ _QWEN35_DEFAULT_MROPE_SECTION = [11, 11, 10, 0]
+
+ gguf_writer: gguf.GGUFWriter
+ rope_parameters: dict
+
+ def set_gguf_parameters(self):
+ super().set_gguf_parameters() # ty: ignore[unresolved-attribute]
+ if "mrope_section" not in self.rope_parameters:
+ self.gguf_writer.add_rope_dimension_sections(self._QWEN35_DEFAULT_MROPE_SECTION)
+
+
@ModelBase.register("Qwen3_5ForConditionalGeneration", "Qwen3_5ForCausalLM")
-class Qwen3_5TextModel(_LinearAttentionVReorderBase):
+class Qwen3_5TextModel(_Qwen35MRopeMixin, _LinearAttentionVReorderBase):
model_arch = gguf.MODEL_ARCH.QWEN35
@ModelBase.register("Qwen3_5MoeForConditionalGeneration", "Qwen3_5MoeForCausalLM")
-class Qwen3_5MoeTextModel(_LinearAttentionVReorderBase):
+class Qwen3_5MoeTextModel(_Qwen35MRopeMixin, _LinearAttentionVReorderBase):
model_arch = gguf.MODEL_ARCH.QWEN35MOE
+# MiniCPM-V 4.6: text tower is Qwen3.5 (linear+full hybrid attention) wrapped under
+# `model.language_model.*`; vision tower is SigLIP + a window-attention ViT merger
+# + a final DownsampleMLP merger. The same HF arch is registered twice below: once as
+# the LM (text mode) and once as the mmproj (vision mode), mirroring the Qwen3-VL setup.
+
+@ModelBase.register("MiniCPMV4_6ForConditionalGeneration")
+class MiniCPMV4_6TextModel(Qwen3_5TextModel):
+ model_arch = gguf.MODEL_ARCH.QWEN35
+
+ @classmethod
+ def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+ name, gen = item
+
+ if name.startswith("model.merger."):
+ return None
+ # MTP tensors are not used at inference yet; align with Qwen3Next behaviour
+ if name.startswith("mtp"):
+ return None
+
+ return super().filter_tensors(item)
+
+
+@ModelBase.register("MiniCPMV4_6ForConditionalGeneration")
+class MiniCPMV4_6VisionModel(MmprojModel):
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ if self.hparams_vision is not None:
+ # In MiniCPM-V 4.6 `vision_config.image_size` (980) describes the SigLIP
+ # positional embedding bucket grid (70 x 70), while the per-slice processing
+ # resolution is the preprocessor's `scale_resolution` (typically 448).
+ # The CLIP loader in tools/mtmd/clip.cpp consumes `clip.vision.image_size`
+ # as the slice size and warmup resolution, so report `scale_resolution` there
+ # to match the upstream MiniCPMV4_6ImageProcessorPil slicing rules.
+ scale_resolution = self.preprocessor_config.get("scale_resolution")
+ if scale_resolution is not None:
+ self.hparams_vision["image_size"] = int(scale_resolution)
+
+ def set_gguf_parameters(self):
+ super().set_gguf_parameters()
+ assert self.hparams_vision is not None
+
+ # projector type string is consumed by clip_projector_type_from_string() in clip.cpp
+ # (mapped to PROJECTOR_TYPE_MINICPMV4_6).
+ self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MINICPMV4_6)
+
+ # ViT merger 2x2 + final merger 2x2 = 4x spatial merge per dimension; used for slice alignment
+ self.gguf_writer.add_vision_projector_scale_factor(4)
+
+ # borrow wa_layer_indexes for vit_merger insertion point
+ insert_layer_id = int(self.global_config.get(
+ "insert_layer_id", self.hparams_vision.get("insert_layer_id", 6)))
+ self.gguf_writer.add_vision_wa_layer_indexes([insert_layer_id])
+
+ # SigLIP vision body uses gelu_pytorch_tanh, which matches ggml_gelu (tanh approx).
+ self.gguf_writer.add_vision_use_gelu(True)
+ self.gguf_writer.add_vision_attention_layernorm_eps(
+ self.hparams_vision.get("layer_norm_eps", 1e-6))
+
+ @classmethod
+ def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+ name, gen = item
+
+ # lm_head / MTP -> belong to the LM file
+ if name.startswith(("lm_head.", "mtp")):
+ return None
+
+ return super().filter_tensors(item)
+
+
@ModelBase.register("GPT2LMHeadModel")
class GPT2Model(TextModel):
model_arch = gguf.MODEL_ARCH.GPT2
{"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-34B-Base", "chkhsh": "48f8e02c0359c0bbdd82f26909171fac1c18a457bb47573ed1fe3bbb2c1cfd4b"},
{"name": "kimi-k2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/moonshotai/Kimi-K2-Base", "chkhsh": "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890"},
{"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "chkhsh": "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c"},
+ {"name": "qwen35", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openbmb/MiniCPM-V-4_6", "chkhsh": "1444df51289cfa8063b96f0e62b1125440111bc79a52003ea14b6eac7016fd5f"},
{"name": "grok-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/alvarobartt/grok-2-tokenizer", "chkhsh": "66b8d4e19ab16c3bfd89bce5d785fb7e0155e8648708a1f42077cb9fe002c273"},
# jina-v2-de variants
{"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/aari1995/German_Semantic_V3", "chkhsh": "b3d1dd861f1d4c5c0d2569ce36baf3f90fe8a102db3de50dd71ff860d91be3df"},
--- /dev/null
+## MiniCPM-V 4.6
+
+### Prepare models and code
+
+Download [MiniCPM-V-4_6](https://huggingface.co/openbmb/MiniCPM-V-4_6) PyTorch model from huggingface to "MiniCPM-V-4_6" folder.
+
+The model must be the standard `transformers` v5.7.0+ checkpoint (no `trust_remote_code`); the architecture in `config.json` is `MiniCPMV4_6ForConditionalGeneration` with a `qwen3_5_text` text model and a SigLIP-based vision tower plus a window-attention `vit_merger`.
+
+### Build llama.cpp
+
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md)
+
+Clone llama.cpp:
+```bash
+git clone https://github.com/ggml-org/llama.cpp
+cd llama.cpp
+```
+
+Build llama.cpp using `CMake`:
+```bash
+cmake -B build
+cmake --build build --config Release
+```
+
+
+### Usage of MiniCPM-V 4.6
+
+Unlike older MiniCPM-V variants, MiniCPM-V 4.6 is converted directly through `convert_hf_to_gguf.py`. The same script is invoked twice on the original Hugging Face directory: once to produce the language-model GGUF and once with `--mmproj` to produce the multimodal projector GGUF.
+
+```bash
+# language model
+python ./convert_hf_to_gguf.py ../MiniCPM-V-4_6 --outfile ../MiniCPM-V-4_6/ggml-model-f16.gguf
+
+# multimodal projector (vision tower + window-attention vit_merger + DownsampleMLP merger)
+python ./convert_hf_to_gguf.py ../MiniCPM-V-4_6 --mmproj --outfile ../MiniCPM-V-4_6/mmproj-model-f16.gguf
+
+# optional: quantize to Q4_K_M
+./build/bin/llama-quantize ../MiniCPM-V-4_6/ggml-model-f16.gguf ../MiniCPM-V-4_6/ggml-model-Q4_K_M.gguf Q4_K_M
+```
+
+
+Inference on Linux or Mac
+```bash
+# run in single-turn mode
+./build/bin/llama-mtmd-cli -m ../MiniCPM-V-4_6/ggml-model-f16.gguf --mmproj ../MiniCPM-V-4_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
+
+# run in conversation mode
+./build/bin/llama-mtmd-cli -m ../MiniCPM-V-4_6/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-V-4_6/mmproj-model-f16.gguf
+```
V_DS_NORM = auto() # qwen3vl
V_DS_FC1 = auto() # qwen3vl
V_DS_FC2 = auto() # qwen3vl
+ V_MERGER_LN1 = auto() # minicpmv4_6
+ V_MERGER_ATTN_Q = auto() # minicpmv4_6
+ V_MERGER_ATTN_K = auto() # minicpmv4_6
+ V_MERGER_ATTN_V = auto() # minicpmv4_6
+ V_MERGER_ATTN_O = auto() # minicpmv4_6
+ V_MERGER_DS_LN = auto() # minicpmv4_6
+ V_MERGER_DS_UP = auto() # minicpmv4_6
+ V_MERGER_DS_DOWN = auto() # minicpmv4_6
V_MM_POST_FC_NORM = auto() # cogvlm
V_MM_UP = auto() # cogvlm
V_MM_DOWN = auto() # cogvlm
MODEL_TENSOR.V_DS_NORM: "v.deepstack.{bid}.norm",
MODEL_TENSOR.V_DS_FC1: "v.deepstack.{bid}.fc1",
MODEL_TENSOR.V_DS_FC2: "v.deepstack.{bid}.fc2",
+ MODEL_TENSOR.V_MERGER_LN1: "v.vit_merger.ln1",
+ MODEL_TENSOR.V_MERGER_ATTN_Q: "v.vit_merger.attn_q",
+ MODEL_TENSOR.V_MERGER_ATTN_K: "v.vit_merger.attn_k",
+ MODEL_TENSOR.V_MERGER_ATTN_V: "v.vit_merger.attn_v",
+ MODEL_TENSOR.V_MERGER_ATTN_O: "v.vit_merger.attn_out",
+ MODEL_TENSOR.V_MERGER_DS_LN: "v.vit_merger.ds_ln",
+ MODEL_TENSOR.V_MERGER_DS_UP: "v.vit_merger.ds_ffn_up",
+ MODEL_TENSOR.V_MERGER_DS_DOWN: "v.vit_merger.ds_ffn_down",
MODEL_TENSOR.V_MM_POST_FC_NORM: "mm.post_fc_norm", # cogvlm
MODEL_TENSOR.V_MM_UP: "mm.up",
MODEL_TENSOR.V_MM_DOWN: "mm.down",
MODEL_TENSOR.V_DS_NORM,
MODEL_TENSOR.V_DS_FC1,
MODEL_TENSOR.V_DS_FC2,
+ MODEL_TENSOR.V_MERGER_LN1,
+ MODEL_TENSOR.V_MERGER_ATTN_Q,
+ MODEL_TENSOR.V_MERGER_ATTN_K,
+ MODEL_TENSOR.V_MERGER_ATTN_V,
+ MODEL_TENSOR.V_MERGER_ATTN_O,
+ MODEL_TENSOR.V_MERGER_DS_LN,
+ MODEL_TENSOR.V_MERGER_DS_UP,
+ MODEL_TENSOR.V_MERGER_DS_DOWN,
MODEL_TENSOR.V_MM_POST_FC_NORM,
MODEL_TENSOR.V_MM_UP,
MODEL_TENSOR.V_MM_DOWN,
NEMOTRON_V2_VL = "nemotron_v2_vl"
HUNYUANOCR = "hunyuanocr"
HUNYUANVL = "hunyuanvl"
+ MINICPMV4_6 = "minicpmv4_6"
GRANITE_SPEECH = "granite_speech" # audio
MODEL_TENSOR.V_ENC_EMBD_PATCH: (
"vision_tower.vision_model.embeddings.patch_embedding",
+ "model.vision_tower.embeddings.patch_embedding", # minicpmv4_6
"model.vision_tower.embeddings.patch_embeddings.projection", # Intern-S1
"vpm.embeddings.patch_embedding",
"model.vision_model.embeddings.patch_embedding", # SmolVLM
MODEL_TENSOR.V_ENC_EMBD_POS: (
"vision_tower.vision_model.embeddings.position_embedding",
+ "model.vision_tower.embeddings.position_embedding", # minicpmv4_6
"model.vision_tower.embeddings.position_embeddings", # Intern-S1
"vpm.embeddings.position_embedding",
"model.vision_model.embeddings.position_embedding", # SmolVLM
MODEL_TENSOR.V_ENC_ATTN_Q: (
"vision_tower.vision_model.encoder.layers.{bid}.self_attn.q_proj",
+ "model.vision_tower.encoder.layers.{bid}.self_attn.q_proj", # minicpmv4_6
"model.vision_tower.encoder.layer.{bid}.attention.q_proj", # Intern-S1
"vpm.encoder.layers.{bid}.self_attn.q_proj",
"model.vision_model.encoder.layers.{bid}.self_attn.q_proj", # SmolVLM
MODEL_TENSOR.V_ENC_ATTN_K: (
"vision_tower.vision_model.encoder.layers.{bid}.self_attn.k_proj",
+ "model.vision_tower.encoder.layers.{bid}.self_attn.k_proj", # minicpmv4_6
"model.vision_tower.encoder.layer.{bid}.attention.k_proj", # Intern-S1
"vpm.encoder.layers.{bid}.self_attn.k_proj",
"model.vision_model.encoder.layers.{bid}.self_attn.k_proj", # SmolVLM
MODEL_TENSOR.V_ENC_ATTN_V: (
"vision_tower.vision_model.encoder.layers.{bid}.self_attn.v_proj",
+ "model.vision_tower.encoder.layers.{bid}.self_attn.v_proj", # minicpmv4_6
"model.vision_tower.encoder.layer.{bid}.attention.v_proj", # Intern-S1
"vpm.encoder.layers.{bid}.self_attn.v_proj",
"model.vision_model.encoder.layers.{bid}.self_attn.v_proj", # SmolVLM
MODEL_TENSOR.V_ENC_INPUT_NORM: (
"vision_tower.vision_model.encoder.layers.{bid}.layer_norm1",
+ "model.vision_tower.encoder.layers.{bid}.layer_norm1", # minicpmv4_6
"vision_tower.vision_model.encoder.layers.{bid}.norm1", # InternVL
"model.vision_tower.encoder.layer.{bid}.layernorm_before", # Intern-S1
"vpm.encoder.layers.{bid}.layer_norm1",
MODEL_TENSOR.V_ENC_ATTN_O: (
"vision_tower.vision_model.encoder.layers.{bid}.self_attn.out_proj",
+ "model.vision_tower.encoder.layers.{bid}.self_attn.out_proj", # minicpmv4_6
"vision_tower.vision_model.encoder.layers.{bid}.attn.proj", # InternVL
"model.vision_tower.encoder.layer.{bid}.attention.projection_layer", # Intern-S1
"vpm.encoder.layers.{bid}.self_attn.out_proj",
MODEL_TENSOR.V_ENC_POST_ATTN_NORM: (
"vision_tower.vision_model.encoder.layers.{bid}.layer_norm2",
+ "model.vision_tower.encoder.layers.{bid}.layer_norm2", # minicpmv4_6
"vision_tower.vision_model.encoder.layers.{bid}.norm2", # InternVL
"model.vision_tower.encoder.layer.{bid}.layernorm_after", # Intern-S1
"vpm.encoder.layers.{bid}.layer_norm2",
MODEL_TENSOR.V_ENC_FFN_UP: (
"vision_tower.vision_model.encoder.layers.{bid}.mlp.fc1",
+ "model.vision_tower.encoder.layers.{bid}.mlp.fc1", # minicpmv4_6
"model.vision_tower.encoder.layer.{bid}.mlp.fc1", # Intern-S1
"vpm.encoder.layers.{bid}.mlp.fc1",
"model.vision_model.encoder.layers.{bid}.mlp.fc1", # SmolVLM, gemma3
MODEL_TENSOR.V_ENC_FFN_DOWN: (
"vision_tower.vision_model.encoder.layers.{bid}.mlp.fc2",
+ "model.vision_tower.encoder.layers.{bid}.mlp.fc2", # minicpmv4_6
"model.vision_tower.encoder.layer.{bid}.mlp.fc2", # Intern-S1
"vpm.encoder.layers.{bid}.mlp.fc2",
"model.vision_model.encoder.layers.{bid}.mlp.fc2", # SmolVLM, gemma3
MODEL_TENSOR.V_POST_NORM: (
"vision_tower.vision_model.post_layernorm",
+ "model.vision_tower.post_layernorm", # minicpmv4_6
"model.vision_model.post_layernorm", # SmolVLM
"vision_model.layernorm_post", # llama4
"visual.merger.ln_q", # qwen2vl
"mlp_AR.pre_norm", # PaddleOCR-VL
"merger.ln_q",
"vision_tower.merger.ln_q", # dots.ocr
+ "model.merger.mlp.0.pre_norm", # minicpmv4_6
),
MODEL_TENSOR.V_MM_SOFT_EMB_NORM: (
"model.visual.deepstack_merger_list.{bid}.linear_fc2", # deepstack in qwen3vl
),
+ MODEL_TENSOR.V_MERGER_LN1: (
+ "model.vision_tower.vit_merger.layer_norm1", # minicpmv4_6
+ ),
+
+ MODEL_TENSOR.V_MERGER_ATTN_Q: (
+ "model.vision_tower.vit_merger.self_attn.q_proj", # minicpmv4_6
+ ),
+
+ MODEL_TENSOR.V_MERGER_ATTN_K: (
+ "model.vision_tower.vit_merger.self_attn.k_proj", # minicpmv4_6
+ ),
+
+ MODEL_TENSOR.V_MERGER_ATTN_V: (
+ "model.vision_tower.vit_merger.self_attn.v_proj", # minicpmv4_6
+ ),
+
+ MODEL_TENSOR.V_MERGER_ATTN_O: (
+ "model.vision_tower.vit_merger.self_attn.out_proj", # minicpmv4_6
+ ),
+
+ MODEL_TENSOR.V_MERGER_DS_LN: (
+ "model.vision_tower.vit_merger.pre_norm", # minicpmv4_6
+ ),
+
+ MODEL_TENSOR.V_MERGER_DS_UP: (
+ "model.vision_tower.vit_merger.linear_1", # minicpmv4_6
+ ),
+
+ MODEL_TENSOR.V_MERGER_DS_DOWN: (
+ "model.vision_tower.vit_merger.linear_2", # minicpmv4_6
+ ),
+
MODEL_TENSOR.V_SAM_POS_EMBD: (
"model.sam_model.pos_embed",
),
MODEL_TENSOR.V_MM_UP: (
"model.vision.linear_proj.dense_h_to_4h", # cogvlm
"visual.merger.up_proj", # glm4v
+ "model.merger.mlp.0.linear_1", # minicpmv4_6
),
MODEL_TENSOR.V_MM_DOWN: (
"model.vision.linear_proj.dense_4h_to_h", # cogvlm
"visual.merger.down_proj", # glm4v
+ "model.merger.mlp.0.linear_2", # minicpmv4_6
),
MODEL_TENSOR.V_MM_GATE: (
- Qwen 2 VL and Qwen 2.5 VL (from [Qwen](https://huggingface.co/Qwen))
- [Mistral Small 3.1 24B](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503)
- InternVL 2.5 and InternVL 3 from [OpenGVLab](https://huggingface.co/OpenGVLab) (note: we don't support conversion of `InternVL3-*-hf` model, only non-HF version is supported ; `InternLM2Model` **text** model is not supported)
+- [MiniCPM-V 4.6](https://huggingface.co/openbmb/MiniCPM-V-4_6) ; See the guide [here](../../docs/multimodal/minicpmv4.6.md) - requires the standard `transformers` v5.7.0+ checkpoint
For older models, please refer to the relevant guide for instructions on how to obtain or create them:
- [MiniCPM-V 2.5](../../docs/multimodal/minicpmv2.5.md)
- [MiniCPM-V 2.6](../../docs/multimodal/minicpmv2.6.md)
- [MiniCPM-o 2.6](../../docs/multimodal/minicpmo2.6.md)
+- [MiniCPM-V 4.0](../../docs/multimodal/minicpmv4.0.md)
+- [MiniCPM-o 4.0](../../docs/multimodal/minicpmo4.0.md)
+- [MiniCPM-V 4.5](../../docs/multimodal/minicpmv4.5.md)
- [IBM Granite Vision](../../docs/multimodal/granitevision.md)
#define TN_MINICPMV_ATTN "resampler.attn.%s.%s"
#define TN_MINICPMV_LN "resampler.ln_%s.%s"
+// MiniCPM-V 4.6 ViT merger (window attention + MLP downsample),
+// matching the upstream `vit_merger` module name in transformers.
+#define TN_VIT_MERGER_LN1 "v.vit_merger.ln1.%s"
+#define TN_VIT_MERGER_ATTN_Q "v.vit_merger.attn_q.%s"
+#define TN_VIT_MERGER_ATTN_K "v.vit_merger.attn_k.%s"
+#define TN_VIT_MERGER_ATTN_V "v.vit_merger.attn_v.%s"
+#define TN_VIT_MERGER_ATTN_O "v.vit_merger.attn_out.%s"
+#define TN_VIT_MERGER_DS_LN "v.vit_merger.ds_ln.%s"
+#define TN_VIT_MERGER_DS_UP "v.vit_merger.ds_ffn_up.%s"
+#define TN_VIT_MERGER_DS_DOWN "v.vit_merger.ds_ffn_down.%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"
PROJECTOR_TYPE_NEMOTRON_V2_VL,
PROJECTOR_TYPE_HUNYUANOCR,
PROJECTOR_TYPE_HUNYUANVL,
+ PROJECTOR_TYPE_MINICPMV4_6,
PROJECTOR_TYPE_GRANITE_SPEECH,
PROJECTOR_TYPE_UNKNOWN,
};
{ PROJECTOR_TYPE_NEMOTRON_V2_VL, "nemotron_v2_vl"},
{ PROJECTOR_TYPE_HUNYUANOCR, "hunyuanocr"},
{ PROJECTOR_TYPE_HUNYUANVL, "hunyuanvl"},
+ { PROJECTOR_TYPE_MINICPMV4_6, "minicpmv4_6"},
{ PROJECTOR_TYPE_GRANITE_SPEECH, "granite_speech"},
};
bool has_llava_projector = false;
int minicpmv_version = 0;
int32_t minicpmv_query_num = 0; // MiniCPM-V query number
+ int32_t insert_layer_id = 0; // MiniCPM-V 4.6 ViT merger insertion layer
// custom value provided by user, can be undefined if not set
int32_t custom_image_min_tokens = -1;
ggml_tensor * mm_model_ln_post_w = nullptr;
ggml_tensor * mm_model_ln_post_b = nullptr;
+ // MiniCPM-V 4.6 ViT merger (window self-attention + ViT MLP downsample)
+ ggml_tensor * vit_merger_ln1_w = nullptr;
+ ggml_tensor * vit_merger_ln1_b = nullptr;
+ ggml_tensor * vit_merger_attn_q_w = nullptr;
+ ggml_tensor * vit_merger_attn_q_b = nullptr;
+ ggml_tensor * vit_merger_attn_k_w = nullptr;
+ ggml_tensor * vit_merger_attn_k_b = nullptr;
+ ggml_tensor * vit_merger_attn_v_w = nullptr;
+ ggml_tensor * vit_merger_attn_v_b = nullptr;
+ ggml_tensor * vit_merger_attn_o_w = nullptr;
+ ggml_tensor * vit_merger_attn_o_b = nullptr;
+ ggml_tensor * vit_merger_ds_ln_w = nullptr;
+ ggml_tensor * vit_merger_ds_ln_b = nullptr;
+ ggml_tensor * vit_merger_ds_up_w = nullptr;
+ ggml_tensor * vit_merger_ds_up_b = nullptr;
+ ggml_tensor * vit_merger_ds_down_w = nullptr;
+ ggml_tensor * vit_merger_ds_down_b = nullptr;
+
// gemma3
ggml_tensor * mm_input_proj_w = nullptr;
ggml_tensor * mm_soft_emb_norm_w = nullptr;
{
builder = std::make_unique<clip_graph_minicpmv>(ctx, img);
} break;
+ case PROJECTOR_TYPE_MINICPMV4_6:
+ {
+ builder = std::make_unique<clip_graph_minicpmv4_6>(ctx, img);
+ } break;
case PROJECTOR_TYPE_INTERNVL:
{
builder = std::make_unique<clip_graph_internvl>(ctx, img);
hparams.minicpmv_version = 2; // default to 2 if not set
}
} break;
+ case PROJECTOR_TYPE_MINICPMV4_6:
+ {
+ // MiniCPM-V 4.6 unified merger projector
+ // ViT merger 2x2 + final merger 2x2 = 4x spatial merge per dimension
+ hparams.n_merge = 4;
+ get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
+
+ // borrow wa_layer_indexes for vit_merger insertion point
+ std::vector<int> wa_layer_indexes_vec;
+ get_arr_int(KEY_WIN_ATTN_LAYER_INDEXES, wa_layer_indexes_vec, false);
+ if (!wa_layer_indexes_vec.empty()) {
+ hparams.insert_layer_id = wa_layer_indexes_vec[0];
+ }
+ } break;
case PROJECTOR_TYPE_INTERNVL:
{
// use default llava-uhd preprocessing params
|| model.proj_type == PROJECTOR_TYPE_GEMMA3
|| model.proj_type == PROJECTOR_TYPE_IDEFICS3
|| model.proj_type == PROJECTOR_TYPE_MINICPMV
+ || model.proj_type == PROJECTOR_TYPE_MINICPMV4_6
) && layer.ff_up_w && layer.ff_down_w && layer.ff_down_w->ne[0] == hparams.n_embd;
if (is_ffn_swapped) {
// swap up and down weights
model.mm_model_ln_post_w = get_tensor(string_format(TN_MINICPMV_LN, "post", "weight"));
model.mm_model_ln_post_b = get_tensor(string_format(TN_MINICPMV_LN, "post", "bias"));
} break;
+ case PROJECTOR_TYPE_MINICPMV4_6:
+ {
+ // ViT merger: window self-attention
+ model.vit_merger_ln1_w = get_tensor(string_format(TN_VIT_MERGER_LN1, "weight"));
+ model.vit_merger_ln1_b = get_tensor(string_format(TN_VIT_MERGER_LN1, "bias"));
+ model.vit_merger_attn_q_w = get_tensor(string_format(TN_VIT_MERGER_ATTN_Q, "weight"));
+ model.vit_merger_attn_q_b = get_tensor(string_format(TN_VIT_MERGER_ATTN_Q, "bias"), false);
+ model.vit_merger_attn_k_w = get_tensor(string_format(TN_VIT_MERGER_ATTN_K, "weight"));
+ model.vit_merger_attn_k_b = get_tensor(string_format(TN_VIT_MERGER_ATTN_K, "bias"), false);
+ model.vit_merger_attn_v_w = get_tensor(string_format(TN_VIT_MERGER_ATTN_V, "weight"));
+ model.vit_merger_attn_v_b = get_tensor(string_format(TN_VIT_MERGER_ATTN_V, "bias"), false);
+ model.vit_merger_attn_o_w = get_tensor(string_format(TN_VIT_MERGER_ATTN_O, "weight"));
+ model.vit_merger_attn_o_b = get_tensor(string_format(TN_VIT_MERGER_ATTN_O, "bias"), false);
+ // ViT merger: MLP downsample
+ model.vit_merger_ds_ln_w = get_tensor(string_format(TN_VIT_MERGER_DS_LN, "weight"));
+ model.vit_merger_ds_ln_b = get_tensor(string_format(TN_VIT_MERGER_DS_LN, "bias"));
+ model.vit_merger_ds_up_w = get_tensor(string_format(TN_VIT_MERGER_DS_UP, "weight"));
+ model.vit_merger_ds_up_b = get_tensor(string_format(TN_VIT_MERGER_DS_UP, "bias"), false);
+ model.vit_merger_ds_down_w = get_tensor(string_format(TN_VIT_MERGER_DS_DOWN, "weight"));
+ model.vit_merger_ds_down_b = get_tensor(string_format(TN_VIT_MERGER_DS_DOWN, "bias"), false);
+ // Final Merger (DownsampleMLP)
+ model.mm_input_norm_w = get_tensor(TN_MM_INP_NORM);
+ model.mm_input_norm_b = get_tensor(TN_MM_INP_NORM_B, false);
+ model.mm_ffn_up_w = get_tensor(string_format(TN_MM_UP, "weight"));
+ model.mm_ffn_up_b = get_tensor(string_format(TN_MM_UP, "bias"), false);
+ model.mm_ffn_down_w = get_tensor(string_format(TN_MM_DOWN, "weight"));
+ model.mm_ffn_down_b = get_tensor(string_format(TN_MM_DOWN, "bias"), false);
+ } break;
case PROJECTOR_TYPE_GLM_EDGE:
{
model.mm_model_adapter_conv_w = get_tensor(string_format(TN_GLM_ADAPER_CONV, "weight"));
}
}
} break;
+ case PROJECTOR_TYPE_MINICPMV4_6:
+ {
+ // ViT merger 4x + final merger 4x = 16x total spatial downsample
+ n_patches = n_patches / 16;
+ } break;
case PROJECTOR_TYPE_QWEN2VL:
case PROJECTOR_TYPE_QWEN25VL:
case PROJECTOR_TYPE_QWEN3VL:
}
set_input_f32("omega", omega);
} break;
+ case PROJECTOR_TYPE_MINICPMV4_6:
+ {
+ // SigLIP position buckets (same as resampler path)
+ std::vector<int32_t> positions(pos_h * pos_w);
+ int bucket_coords_h[1024];
+ int bucket_coords_w[1024];
+ for (int i = 0; i < pos_h; i++){
+ bucket_coords_h[i] = std::floor(70.0*i/pos_h);
+ }
+ for (int i = 0; i < pos_w; i++){
+ bucket_coords_w[i] = std::floor(70.0*i/pos_w);
+ }
+ for (int i = 0, id = 0; i < pos_h; i++){
+ for (int j = 0; j < pos_w; j++){
+ positions[id++] = bucket_coords_h[i]*70 + bucket_coords_w[j];
+ }
+ }
+ set_input_i32("positions", positions);
+
+ const int half_h = pos_h / 2;
+ const int half_w = pos_w / 2;
+
+ // window reorder indices for 2x2 windows
+ std::vector<int32_t> window_idx(n_pos);
+ std::vector<int32_t> inv_window_idx(n_pos);
+ {
+ int k = 0;
+ for (int wi = 0; wi < half_h; wi++) {
+ for (int wj = 0; wj < half_w; wj++) {
+ window_idx[k++] = (2*wi ) * pos_w + (2*wj );
+ window_idx[k++] = (2*wi ) * pos_w + (2*wj + 1);
+ window_idx[k++] = (2*wi + 1) * pos_w + (2*wj );
+ window_idx[k++] = (2*wi + 1) * pos_w + (2*wj + 1);
+ }
+ }
+ for (int i = 0; i < n_pos; i++) {
+ inv_window_idx[window_idx[i]] = i;
+ }
+ }
+ set_input_i32("vit_merger_window_idx", window_idx);
+ set_input_i32("vit_merger_inv_window_idx", inv_window_idx);
+
+ // block-diagonal attention mask: tokens in the same 4-token
+ // window attend to each other (mask = 0), all other positions
+ // are masked out (-inf). matches the window-major reorder above.
+ std::vector<float> window_mask_data(n_pos * n_pos, std::numeric_limits<float>::lowest());
+ for (int wi = 0; wi < n_pos / 4; wi++) {
+ for (int i = 0; i < 4; i++) {
+ for (int j = 0; j < 4; j++) {
+ window_mask_data[(wi*4 + i) * n_pos + (wi*4 + j)] = 0.0f;
+ }
+ }
+ }
+ set_input_f32("vit_merger_window_mask", window_mask_data);
+
+ // ViT merger 2x2 downsample indices
+ auto make_ds_idx = [](int off_r, int off_c, int ds_h, int ds_w, int stride_w) {
+ std::vector<int32_t> idx(ds_h * ds_w);
+ for (int i = 0; i < ds_h; i++) {
+ for (int j = 0; j < ds_w; j++) {
+ idx[i * ds_w + j] = (2*i + off_r) * stride_w + (2*j + off_c);
+ }
+ }
+ return idx;
+ };
+ auto vit_merger_ds_0 = make_ds_idx(0, 0, half_h, half_w, pos_w);
+ auto vit_merger_ds_1 = make_ds_idx(0, 1, half_h, half_w, pos_w);
+ auto vit_merger_ds_2 = make_ds_idx(1, 0, half_h, half_w, pos_w);
+ auto vit_merger_ds_3 = make_ds_idx(1, 1, half_h, half_w, pos_w);
+ set_input_i32("vit_merger_ds_idx_0", vit_merger_ds_0);
+ set_input_i32("vit_merger_ds_idx_1", vit_merger_ds_1);
+ set_input_i32("vit_merger_ds_idx_2", vit_merger_ds_2);
+ set_input_i32("vit_merger_ds_idx_3", vit_merger_ds_3);
+
+ // final merger 2x2 downsample indices (operates on half_h x half_w grid)
+ const int qh = half_h / 2;
+ const int qw = half_w / 2;
+ auto m_ds_0 = make_ds_idx(0, 0, qh, qw, half_w);
+ auto m_ds_1 = make_ds_idx(0, 1, qh, qw, half_w);
+ auto m_ds_2 = make_ds_idx(1, 0, qh, qw, half_w);
+ auto m_ds_3 = make_ds_idx(1, 1, qh, qw, half_w);
+ set_input_i32("merger_ds_idx_0", m_ds_0);
+ set_input_i32("merger_ds_idx_1", m_ds_1);
+ set_input_i32("merger_ds_idx_2", m_ds_2);
+ set_input_i32("merger_ds_idx_3", m_ds_3);
+ } break;
case PROJECTOR_TYPE_QWEN2VL:
case PROJECTOR_TYPE_QWEN3VL:
case PROJECTOR_TYPE_GLM4V:
return ctx->model.mm_3_b->ne[0];
case PROJECTOR_TYPE_MINICPMV:
return ctx->model.mm_model_proj->ne[0];
+ case PROJECTOR_TYPE_MINICPMV4_6:
+ return ctx->model.mm_ffn_down_w->ne[1];
case PROJECTOR_TYPE_GLM_EDGE:
return ctx->model.mm_model_mlp_3_w->ne[1];
case PROJECTOR_TYPE_QWEN2VL:
if (ctx->proj_type() == PROJECTOR_TYPE_MINICPMV) {
return ctx->model.hparams.minicpmv_version;
}
+ if (ctx->proj_type() == PROJECTOR_TYPE_MINICPMV4_6) {
+ return 46;
+ }
return 0;
}
return gf;
}
+
+ggml_cgraph * clip_graph_minicpmv4_6::build() {
+ const int insert_lid = hparams.insert_layer_id;
+ const int n_pos = n_patches;
+ const int half_h = n_patches_y / 2;
+ const int half_w = n_patches_x / 2;
+ const int n_ds = half_h * half_w; // after ViT merger 2x2 downsample
+ const int qh = half_h / 2;
+ const int qw = half_w / 2;
+ const int n_ds2 = qh * qw; // after final merger 2x2 downsample
+
+ auto add_i32_input = [&](const char * name, int n) {
+ ggml_tensor * t = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n);
+ ggml_set_name(t, name);
+ ggml_set_input(t);
+ return t;
+ };
+
+ // position indices for ViT learned positional embeddings
+ ggml_tensor * positions = add_i32_input("positions", n_pos);
+ ggml_tensor * learned_pos_embd = ggml_get_rows(ctx0, model.position_embeddings, positions);
+
+ // ViT merger window reorder indices + block-diagonal mask
+ // (mask layout follows qwen2vl: -inf except for 4x4 blocks on the diagonal,
+ // so each window-major group of 4 tokens only attends to itself)
+ ggml_tensor * vit_merger_window_idx = add_i32_input("vit_merger_window_idx", n_pos);
+ ggml_tensor * vit_merger_inv_window_idx = add_i32_input("vit_merger_inv_window_idx", n_pos);
+ ggml_tensor * vit_merger_window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos);
+ ggml_set_name(vit_merger_window_mask, "vit_merger_window_mask");
+ ggml_set_input(vit_merger_window_mask);
+ if (flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) {
+ vit_merger_window_mask = ggml_cast(ctx0, vit_merger_window_mask, GGML_TYPE_F16);
+ }
+
+ // ViT merger 2x2 downsample gather indices
+ ggml_tensor * vit_merger_ds_idx_0 = add_i32_input("vit_merger_ds_idx_0", n_ds);
+ ggml_tensor * vit_merger_ds_idx_1 = add_i32_input("vit_merger_ds_idx_1", n_ds);
+ ggml_tensor * vit_merger_ds_idx_2 = add_i32_input("vit_merger_ds_idx_2", n_ds);
+ ggml_tensor * vit_merger_ds_idx_3 = add_i32_input("vit_merger_ds_idx_3", n_ds);
+
+ // final merger 2x2 downsample gather indices
+ ggml_tensor * merger_ds_idx_0 = add_i32_input("merger_ds_idx_0", n_ds2);
+ ggml_tensor * merger_ds_idx_1 = add_i32_input("merger_ds_idx_1", n_ds2);
+ ggml_tensor * merger_ds_idx_2 = add_i32_input("merger_ds_idx_2", n_ds2);
+ ggml_tensor * merger_ds_idx_3 = add_i32_input("merger_ds_idx_3", n_ds2);
+
+ // patch embedding + positional embedding
+ ggml_tensor * inp = build_inp();
+ inp = ggml_add(ctx0, inp, learned_pos_embd);
+ cb(inp, "pos_embed", -1);
+
+ ggml_tensor * inpL = inp;
+ if (model.pre_ln_w) {
+ inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, NORM_TYPE_NORMAL, eps, -1);
+ cb(inpL, "pre_ln", -1);
+ }
+
+ // ViT layers 0..insert_layer_id (inclusive)
+ // Mirrors the separate-qkv path of clip_graph::build_vit so the two manually
+ // unrolled segments around the ViT merger read like build_vit() expansions.
+ for (int il = 0; il <= insert_lid; il++) {
+ auto & layer = model.layers[il];
+ ggml_tensor * cur = inpL;
+
+ cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il);
+ cb(cur, "layer_inp_normed", il);
+
+ {
+ ggml_tensor * Qcur = build_mm(layer.q_w, cur);
+ if (layer.q_b) {
+ Qcur = ggml_add(ctx0, Qcur, layer.q_b);
+ }
+ ggml_tensor * Kcur = build_mm(layer.k_w, cur);
+ if (layer.k_b) {
+ Kcur = ggml_add(ctx0, Kcur, layer.k_b);
+ }
+ ggml_tensor * Vcur = build_mm(layer.v_w, cur);
+ if (layer.v_b) {
+ Vcur = ggml_add(ctx0, Vcur, layer.v_b);
+ }
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos);
+ Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos);
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ cur = build_attn(layer.o_w, layer.o_b, Qcur, Kcur, Vcur, nullptr, kq_scale, il);
+ cb(cur, "attn_out", il);
+ }
+
+ if (layer.ls_1_w) {
+ cur = ggml_mul(ctx0, cur, layer.ls_1_w);
+ cb(cur, "attn_out_scaled", il);
+ }
+ cur = ggml_add(ctx0, cur, inpL);
+ inpL = cur;
+ cb(cur, "ffn_inp", il);
+
+ cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il);
+ cb(cur, "ffn_inp_normed", il);
+
+ cur = build_ffn(cur, layer.ff_up_w, layer.ff_up_b, layer.ff_gate_w, layer.ff_gate_b,
+ layer.ff_down_w, layer.ff_down_b, hparams.ffn_op, il);
+ cb(cur, "ffn_out", il);
+
+ if (layer.ls_2_w) {
+ cur = ggml_mul(ctx0, cur, layer.ls_2_w);
+ cb(cur, "ffn_out_scaled", il);
+ }
+ cur = ggml_add(ctx0, inpL, cur);
+ cb(cur, "layer_out", il);
+
+ inpL = cur;
+ }
+
+ // ViT merger: window self-attention
+ // Tokens are reordered to window-major (4 tokens per window are contiguous),
+ // and a block-diagonal mask restricts attention to within each window. This
+ // mirrors the qwen2vl windowed-attention pattern so build_attn() can pick the
+ // flash-attention path when available.
+ {
+ ggml_tensor * residual = inpL;
+ ggml_tensor * cur = build_norm(inpL,
+ model.vit_merger_ln1_w, model.vit_merger_ln1_b,
+ NORM_TYPE_NORMAL, eps, -1);
+ cb(cur, "vit_merger_attn_inp_normed", -1);
+
+ cur = ggml_get_rows(ctx0, cur, vit_merger_window_idx);
+ cb(cur, "vit_merger_window_reorder", -1);
+
+ ggml_tensor * Qcur = build_mm(model.vit_merger_attn_q_w, cur);
+ if (model.vit_merger_attn_q_b) {
+ Qcur = ggml_add(ctx0, Qcur, model.vit_merger_attn_q_b);
+ }
+ ggml_tensor * Kcur = build_mm(model.vit_merger_attn_k_w, cur);
+ if (model.vit_merger_attn_k_b) {
+ Kcur = ggml_add(ctx0, Kcur, model.vit_merger_attn_k_b);
+ }
+ ggml_tensor * Vcur = build_mm(model.vit_merger_attn_v_w, cur);
+ if (model.vit_merger_attn_v_b) {
+ Vcur = ggml_add(ctx0, Vcur, model.vit_merger_attn_v_b);
+ }
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos);
+ Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos);
+ cb(Qcur, "vit_merger_Qcur", -1);
+ cb(Kcur, "vit_merger_Kcur", -1);
+ cb(Vcur, "vit_merger_Vcur", -1);
+
+ cur = build_attn(model.vit_merger_attn_o_w, model.vit_merger_attn_o_b,
+ Qcur, Kcur, Vcur, vit_merger_window_mask, kq_scale, -1);
+ cb(cur, "vit_merger_attn_out", -1);
+
+ cur = ggml_get_rows(ctx0, cur, vit_merger_inv_window_idx);
+ inpL = ggml_add(ctx0, cur, residual);
+ cb(inpL, "vit_merger_attn_residual", -1);
+ }
+
+ // ViT merger: 2x2 spatial downsample + MLP (4 tokens -> 1)
+ {
+ ggml_tensor * p0 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_0);
+ ggml_tensor * p1 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_1);
+ ggml_tensor * p2 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_2);
+ ggml_tensor * p3 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_3);
+
+ ggml_tensor * mean_res = ggml_add(ctx0, p0, p1);
+ mean_res = ggml_add(ctx0, mean_res, p2);
+ mean_res = ggml_add(ctx0, mean_res, p3);
+ mean_res = ggml_scale(ctx0, mean_res, 0.25f);
+ cb(mean_res, "vit_merger_ds_mean_res", -1);
+
+ ggml_tensor * cat = ggml_concat(ctx0, p0, p1, 0);
+ cat = ggml_concat(ctx0, cat, p2, 0);
+ cat = ggml_concat(ctx0, cat, p3, 0);
+
+ ggml_tensor * cur = build_norm(cat,
+ model.vit_merger_ds_ln_w, model.vit_merger_ds_ln_b,
+ NORM_TYPE_NORMAL, eps, -1);
+ cb(cur, "vit_merger_ds_normed", -1);
+
+ // ViTWindowAttentionMerger downsample MLP uses gelu_pytorch_tanh (FFN_GELU)
+ cur = build_ffn(cur,
+ model.vit_merger_ds_up_w, model.vit_merger_ds_up_b,
+ nullptr, nullptr,
+ model.vit_merger_ds_down_w, model.vit_merger_ds_down_b,
+ FFN_GELU, -1);
+ cb(cur, "vit_merger_ds_mlp_out", -1);
+
+ inpL = ggml_add(ctx0, cur, mean_res);
+ cb(inpL, "vit_merger_ds_out", -1);
+ }
+
+ // ViT layers (insert_layer_id+1)..n_layer-1, operating on the downsampled tokens
+ {
+ const int64_t n_pos_ds = n_ds;
+ for (int il = insert_lid + 1; il < n_layer; il++) {
+ auto & layer = model.layers[il];
+ ggml_tensor * cur = inpL;
+
+ cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il);
+ cb(cur, "layer_inp_normed", il);
+
+ {
+ ggml_tensor * Qcur = build_mm(layer.q_w, cur);
+ if (layer.q_b) {
+ Qcur = ggml_add(ctx0, Qcur, layer.q_b);
+ }
+ ggml_tensor * Kcur = build_mm(layer.k_w, cur);
+ if (layer.k_b) {
+ Kcur = ggml_add(ctx0, Kcur, layer.k_b);
+ }
+ ggml_tensor * Vcur = build_mm(layer.v_w, cur);
+ if (layer.v_b) {
+ Vcur = ggml_add(ctx0, Vcur, layer.v_b);
+ }
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos_ds);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos_ds);
+ Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos_ds);
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ cur = build_attn(layer.o_w, layer.o_b, Qcur, Kcur, Vcur, nullptr, kq_scale, il);
+ cb(cur, "attn_out", il);
+ }
+
+ if (layer.ls_1_w) {
+ cur = ggml_mul(ctx0, cur, layer.ls_1_w);
+ cb(cur, "attn_out_scaled", il);
+ }
+ cur = ggml_add(ctx0, cur, inpL);
+ inpL = cur;
+ cb(cur, "ffn_inp", il);
+
+ cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il);
+ cb(cur, "ffn_inp_normed", il);
+
+ cur = build_ffn(cur, layer.ff_up_w, layer.ff_up_b, layer.ff_gate_w, layer.ff_gate_b,
+ layer.ff_down_w, layer.ff_down_b, hparams.ffn_op, il);
+ cb(cur, "ffn_out", il);
+
+ if (layer.ls_2_w) {
+ cur = ggml_mul(ctx0, cur, layer.ls_2_w);
+ cb(cur, "ffn_out_scaled", il);
+ }
+ cur = ggml_add(ctx0, inpL, cur);
+ cb(cur, "layer_out", il);
+
+ inpL = cur;
+ }
+ }
+
+ if (model.post_ln_w) {
+ inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, NORM_TYPE_NORMAL, eps, -1);
+ cb(inpL, "post_ln", -1);
+ }
+
+ // Final Merger (DownsampleMLP): another 2x2 spatial merge -> projector embedding
+ {
+ ggml_tensor * p0 = ggml_get_rows(ctx0, inpL, merger_ds_idx_0);
+ ggml_tensor * p1 = ggml_get_rows(ctx0, inpL, merger_ds_idx_1);
+ ggml_tensor * p2 = ggml_get_rows(ctx0, inpL, merger_ds_idx_2);
+ ggml_tensor * p3 = ggml_get_rows(ctx0, inpL, merger_ds_idx_3);
+
+ ggml_tensor * cat = ggml_concat(ctx0, p0, p1, 0);
+ cat = ggml_concat(ctx0, cat, p2, 0);
+ cat = ggml_concat(ctx0, cat, p3, 0);
+
+ ggml_tensor * cur = build_norm(cat,
+ model.mm_input_norm_w, model.mm_input_norm_b,
+ NORM_TYPE_NORMAL, eps, -1);
+ cb(cur, "merger_normed", -1);
+
+ // MiniCPMV4_6DownsampleMLP uses nn.GELU() (erf-based, FFN_GELU_ERF)
+ cur = build_ffn(cur,
+ model.mm_ffn_up_w, model.mm_ffn_up_b,
+ nullptr, nullptr,
+ model.mm_ffn_down_w, model.mm_ffn_down_b,
+ FFN_GELU_ERF, -1);
+ cb(cur, "merger_out", -1);
+
+ inpL = cur;
+ }
+
+ ggml_build_forward_expand(gf, inpL);
+ return gf;
+}
ggml_cgraph * build() override;
};
+struct clip_graph_minicpmv4_6 : clip_graph {
+ clip_graph_minicpmv4_6(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+ ggml_cgraph * build() override;
+};
+
struct clip_graph_internvl : clip_graph {
clip_graph_internvl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
mtmd_image_preprocessor_llava_uhd::slice_instructions mtmd_image_preprocessor_llava_uhd::get_slice_instructions(const clip_image_size & original_size) {
mtmd_image_preprocessor_llava_uhd::slice_instructions res;
- const int patch_size = hparams.patch_size;
+ // align slices by patch_size * n_merge so an integer number of merger output tokens fits per slice
+ const int n_merge = hparams.n_merge > 0 ? hparams.n_merge : 1;
+ const int patch_size = hparams.patch_size * n_merge;
const int slice_size = hparams.image_size;
const int original_width = original_size.width;
const int original_height = original_size.height;
}
image_preproc = std::make_unique<mtmd_image_preprocessor_llava_uhd>(ctx_v);
} break;
+ case PROJECTOR_TYPE_MINICPMV4_6:
+ {
+ slice_tmpl = MTMD_SLICE_TMPL_MINICPMV_2_6;
+ tok_ov_img_start = {lookup_token("<image>")};
+ tok_ov_img_end = {lookup_token("</image>")};
+ tok_sli_img_start = {lookup_token("<slice>")};
+ tok_sli_img_end = {lookup_token("</slice>")};
+ tok_row_end = {lookup_token("\n")};
+ tok_row_end_trail = false; // no trailing end-of-row token
+ ov_img_first = true;
+ image_preproc = std::make_unique<mtmd_image_preprocessor_llava_uhd>(ctx_v);
+ } break;
case PROJECTOR_TYPE_QWEN2VL:
case PROJECTOR_TYPE_QWEN25VL:
case PROJECTOR_TYPE_QWEN3VL:
overview / pkg / ggml / sources / llama.cpp / commitdiff