支持llama.cpp 部署么？

[WangFengtu1996]

• 看到有fork了这个llama.cpp 的仓库

[iceflame89]

We are working on it，please stay tuned.

[CyberTimon]

I'm interested in this feature too! Thanks

[iceflame89]

coming soon!

[CyberTimon]

Nice! Looking forward to it :)

[leeaction]

any update? I'm Really looking forward to it

[zhengxingmao]

+1

[Achazwl]

Created a PR: ggml-org/llama.cpp#6919.
I created a folder called "minicpmv" in the examples folder of llama.cpp.
More detail can be seen in llama.cpp/examples/minicpmv/README.md

[leeaction]

Created a PR: ggerganov/llama.cpp#6919. I created a folder called "minicpmv" in the examples folder of llama.cpp. More detail can be seen in llama.cpp/examples/minicpmv/README.md

Hi Achazwl, Can you provide the minicpmv gguf Q4_K_M file? That could be easily to deploy on ollama locally.

[Achazwl]

Created a PR: ggerganov/llama.cpp#6919. I created a folder called "minicpmv" in the examples folder of llama.cpp. More detail can be seen in llama.cpp/examples/minicpmv/README.md

Hi Achazwl, Can you provide the minicpmv gguf Q4_K_M file? That could be easily to deploy on ollama locally.

Thanks mzwing for uploading: https://huggingface.co/mzwing/MiniCPM-V-2-GGUF/tree/main

[leeaction]

Created a PR: ggerganov/llama.cpp#6919. I created a folder called "minicpmv" in the examples folder of llama.cpp. More detail can be seen in llama.cpp/examples/minicpmv/README.md

Hi Achazwl, Can you provide the minicpmv gguf Q4_K_M file? That could be easily to deploy on ollama locally.

Thanks mzwing for uploading: https://huggingface.co/mzwing/MiniCPM-V-2-GGUF/tree/main

Thanks to provid the MiniCPM-V-2-GGUF model , but when I import it with Modelfile to ollama, the log shows that load model failed ,the error message is below

main error is done_getting_tensors: wrong number of tensors; expected 363, got 362

Please give some help to resolve this issue.

Ollama version ：0.1.37

logs：

May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: loaded meta data with 23 key-value pairs and 363 tensors from /usr/share/ollama/.ollama/models/blobs/sha256-e336d2d263931fce7f8006ea7e283911854458c4faf6efe79f8ebd8730be16e3 (version GGUF V3 (latest))
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 0: general.architecture str = minicpm
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 1: general.name str = MiniCPM
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 2: minicpm.context_length u32 = 4096
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 3: minicpm.embedding_length u32 = 2304
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 4: minicpm.block_count u32 = 40
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 5: minicpm.feed_forward_length u32 = 5760
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 6: minicpm.rope.dimension_count u32 = 64
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 7: minicpm.attention.head_count u32 = 36
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 8: minicpm.attention.head_count_kv u32 = 36
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 9: minicpm.attention.layer_norm_rms_epsilon f32 = 0.000010
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 10: general.file_type u32 = 15
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 11: minicpm.tie_lm_head bool = false
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 12: tokenizer.ggml.model str = llama
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,122753] = ["", "", "", "", "<C...
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,122753] = [-1000.000000, -1000.000000, -1000.00...
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,122753] = [3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 2
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 0
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 19: tokenizer.ggml.padding_token_id u32 = 0
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 20: tokenizer.ggml.add_bos_token bool = true
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 21: tokenizer.ggml.add_eos_token bool = false
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 22: general.quantization_version u32 = 2
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - type f32: 81 tensors
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - type q5_0: 20 tensors
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - type q8_0: 20 tensors
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - type q4_K: 221 tensors
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - type q6_K: 21 tensors
May 14 12:08:40 wbs-desktop ollama[226291]: time=2024-05-14T12:08:40.645+08:00 level=INFO source=server.go:524 msg="waiting for server to become available" status="llm server loading model"
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_vocab: mismatch in special tokens definition ( 3528/122753 vs 271/122753 ).
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: format = GGUF V3 (latest)
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: arch = minicpm
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: vocab type = SPM
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_vocab = 122753
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_merges = 0
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_ctx_train = 4096
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_embd = 2304
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_head = 36
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_head_kv = 36
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_layer = 40
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_rot = 64
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_embd_head_k = 64
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_embd_head_v = 64
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_gqa = 1
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_embd_k_gqa = 2304
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_embd_v_gqa = 2304
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: f_norm_eps = 0.0e+00
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: f_norm_rms_eps = 1.0e-05
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: f_clamp_kqv = 0.0e+00
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: f_max_alibi_bias = 0.0e+00
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: f_logit_scale = 0.0e+00
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_ff = 5760
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_expert = 0
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_expert_used = 0
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: causal attn = 1
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: pooling type = 0
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: rope type = 0
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: rope scaling = linear
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: freq_base_train = 10000.0
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: freq_scale_train = 1
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_yarn_orig_ctx = 4096
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: rope_finetuned = unknown
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: ssm_d_conv = 0
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: ssm_d_inner = 0
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: ssm_d_state = 0
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: ssm_dt_rank = 0
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: model type = 2B
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: model ftype = Q4_K - Medium
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: model params = 3.01 B
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: model size = 1.82 GiB (5.21 BPW)
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: general.name = MiniCPM
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: BOS token = 1 ''
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: EOS token = 2 ''
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: UNK token = 0 ''
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: PAD token = 0 ''
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: LF token = 1099 '<0x0A>'
May 14 12:08:40 wbs-desktop ollama[226291]: ggml_cuda_init: GGML_CUDA_FORCE_MMQ: yes
May 14 12:08:40 wbs-desktop ollama[226291]: ggml_cuda_init: CUDA_USE_TENSOR_CORES: no
May 14 12:08:40 wbs-desktop ollama[226291]: ggml_cuda_init: found 1 CUDA devices:
May 14 12:08:40 wbs-desktop ollama[226291]: Device 0: NVIDIA GeForce GTX 1050 Ti, compute capability 6.1, VMM: yes
May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_tensors: ggml ctx size = 0.37 MiB
May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_load: error loading model: done_getting_tensors: wrong number of tensors; expected 363, got 362
May 14 12:08:40 wbs-desktop ollama[226291]: llama_load_model_from_file: exception loading model
May 14 12:08:40 wbs-desktop ollama[226291]: terminate called after throwing an instance of 'std::runtime_error'
May 14 12:08:40 wbs-desktop ollama[226291]: what(): done_getting_tensors: wrong number of tensors; expected 363, got 362

[Achazwl]

Created a PR: ggerganov/llama.cpp#6919. I created a folder called "minicpmv" in the examples folder of llama.cpp. More detail can be seen in llama.cpp/examples/minicpmv/README.md

Hi Achazwl, Can you provide the minicpmv gguf Q4_K_M file? That could be easily to deploy on ollama locally.

Thanks mzwing for uploading: https://huggingface.co/mzwing/MiniCPM-V-2-GGUF/tree/main

Thank to provid the MiniCPM-V-2-GGUF model , but when I import it with Modelfile to ollama, the log shows that load model failed ,the error message is below

main error is done_getting_tensors: wrong number of tensors; expected 363, got 362

Please give some help to resolve this issue.

Ollama version ：0.1.37

logs：

May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: loaded meta data with 23 key-value pairs and 363 tensors from /usr/share/ollama/.ollama/models/blobs/sha256-e336d2d263931fce7f8006ea7e283911854458c4faf6efe79f8ebd8730be16e3 (version GGUF V3 (latest)) May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 0: general.architecture str = minicpm May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 1: general.name str = MiniCPM May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 2: minicpm.context_length u32 = 4096 May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 3: minicpm.embedding_length u32 = 2304 May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 4: minicpm.block_count u32 = 40 May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 5: minicpm.feed_forward_length u32 = 5760 May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 6: minicpm.rope.dimension_count u32 = 64 May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 7: minicpm.attention.head_count u32 = 36 May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 8: minicpm.attention.head_count_kv u32 = 36 May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 9: minicpm.attention.layer_norm_rms_epsilon f32 = 0.000010 May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 10: general.file_type u32 = 15 May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 11: minicpm.tie_lm_head bool = false May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 12: tokenizer.ggml.model str = llama May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,122753] = ["", "", "", "", "<C... May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,122753] = [-1000.000000, -1000.000000, -1000.00... May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,122753] = [3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1 May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 2 May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 0 May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 19: tokenizer.ggml.padding_token_id u32 = 0 May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 20: tokenizer.ggml.add_bos_token bool = true May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 21: tokenizer.ggml.add_eos_token bool = false May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - kv 22: general.quantization_version u32 = 2 May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - type f32: 81 tensors May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - type q5_0: 20 tensors May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - type q8_0: 20 tensors May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - type q4_K: 221 tensors May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_loader: - type q6_K: 21 tensors May 14 12:08:40 wbs-desktop ollama[226291]: time=2024-05-14T12:08:40.645+08:00 level=INFO source=server.go:524 msg="waiting for server to become available" status="llm server loading model" May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_vocab: mismatch in special tokens definition ( 3528/122753 vs 271/122753 ). May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: format = GGUF V3 (latest) May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: arch = minicpm May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: vocab type = SPM May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_vocab = 122753 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_merges = 0 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_ctx_train = 4096 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_embd = 2304 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_head = 36 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_head_kv = 36 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_layer = 40 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_rot = 64 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_embd_head_k = 64 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_embd_head_v = 64 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_gqa = 1 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_embd_k_gqa = 2304 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_embd_v_gqa = 2304 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: f_norm_eps = 0.0e+00 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: f_norm_rms_eps = 1.0e-05 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: f_clamp_kqv = 0.0e+00 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: f_max_alibi_bias = 0.0e+00 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: f_logit_scale = 0.0e+00 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_ff = 5760 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_expert = 0 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_expert_used = 0 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: causal attn = 1 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: pooling type = 0 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: rope type = 0 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: rope scaling = linear May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: freq_base_train = 10000.0 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: freq_scale_train = 1 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: n_yarn_orig_ctx = 4096 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: rope_finetuned = unknown May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: ssm_d_conv = 0 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: ssm_d_inner = 0 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: ssm_d_state = 0 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: ssm_dt_rank = 0 May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: model type = 2B May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: model ftype = Q4_K - Medium May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: model params = 3.01 B May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: model size = 1.82 GiB (5.21 BPW) May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: general.name = MiniCPM May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: BOS token = 1 '' May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: EOS token = 2 '' May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: UNK token = 0 '' May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: PAD token = 0 '' May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_print_meta: LF token = 1099 '<0x0A>' May 14 12:08:40 wbs-desktop ollama[226291]: ggml_cuda_init: GGML_CUDA_FORCE_MMQ: yes May 14 12:08:40 wbs-desktop ollama[226291]: ggml_cuda_init: CUDA_USE_TENSOR_CORES: no May 14 12:08:40 wbs-desktop ollama[226291]: ggml_cuda_init: found 1 CUDA devices: May 14 12:08:40 wbs-desktop ollama[226291]: Device 0: NVIDIA GeForce GTX 1050 Ti, compute capability 6.1, VMM: yes May 14 12:08:40 wbs-desktop ollama[226291]: llm_load_tensors: ggml ctx size = 0.37 MiB May 14 12:08:40 wbs-desktop ollama[226291]: llama_model_load: error loading model: done_getting_tensors: wrong number of tensors; expected 363, got 362 May 14 12:08:40 wbs-desktop ollama[226291]: llama_load_model_from_file: exception loading model May 14 12:08:40 wbs-desktop ollama[226291]: terminate called after throwing an instance of 'std::runtime_error' May 14 12:08:40 wbs-desktop ollama[226291]: what(): done_getting_tensors: wrong number of tensors; expected 363, got 362

My PR also updated a few lines of minicpm's code, which is in the llama.cpp file, are those modification synced?

https://github.com/ggerganov/llama.cpp/pull/6919/files#diff-150dc86746a90bad4fc2c3334aeb9b5887b3adad3cc1459446717638605348ef

[Achazwl]

@mzwing Would my latest commit ggml-org/llama.cpp@a76fbcd that tidy the modification outside the minicpmv folder change the gguf file?