[BUG] <title>无法推理视频

[liweimin12]

是否已有关于该错误的issue或讨论？ | Is there an existing issue / discussion for this?

• 我已经搜索过已有的issues和讨论 | I have searched the existing issues / discussions

该问题是否在FAQ中有解答？ | Is there an existing answer for this in FAQ?

• 我已经搜索过FAQ | I have searched FAQ

当前行为 | Current Behavior

我直接下载了抱脸上的 gguf 文件，然后用你们fork 的 llama.cpp 运行，在图片推理上一切正常，但是无法使用视频推理，情况如下：
Log start
llama_model_loader: loaded meta data with 22 key-value pairs and 339 tensors from /Users/sergio/Minicpmv2_6gguf/ggml-model-Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = qwen2
llama_model_loader: - kv 1: general.name str = model
llama_model_loader: - kv 2: qwen2.block_count u32 = 28
llama_model_loader: - kv 3: qwen2.context_length u32 = 32768
llama_model_loader: - kv 4: qwen2.embedding_length u32 = 3584
llama_model_loader: - kv 5: qwen2.feed_forward_length u32 = 18944
llama_model_loader: - kv 6: qwen2.attention.head_count u32 = 28
llama_model_loader: - kv 7: qwen2.attention.head_count_kv u32 = 4
llama_model_loader: - kv 8: qwen2.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 9: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 10: general.file_type u32 = 7
llama_model_loader: - kv 11: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 12: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,151666] = ["!", """, "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,151666] = [3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 15: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 151644
llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 128244
llama_model_loader: - kv 19: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 20: tokenizer.chat_template str = {% for message in messages %}{% if lo...
llama_model_loader: - kv 21: general.quantization_version u32 = 2
llama_model_loader: - type f32: 141 tensors
llama_model_loader: - type q8_0: 198 tensors
llm_load_vocab: special tokens cache size = 25
llm_load_vocab: token to piece cache size = 0.9309 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = qwen2
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 151666
llm_load_print_meta: n_merges = 151387
llm_load_print_meta: n_ctx_train = 32768
llm_load_print_meta: n_embd = 3584
llm_load_print_meta: n_head = 28
llm_load_print_meta: n_head_kv = 4
llm_load_print_meta: n_layer = 28
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 7
llm_load_print_meta: n_embd_k_gqa = 512
llm_load_print_meta: n_embd_v_gqa = 512
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-06
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 18944
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 2
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 32768
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: model type = ?B
llm_load_print_meta: model ftype = Q8_0
llm_load_print_meta: model params = 7.61 B
llm_load_print_meta: model size = 7.53 GiB (8.50 BPW)
llm_load_print_meta: general.name = model
llm_load_print_meta: BOS token = 151644 '<|im_start|>'
llm_load_print_meta: EOS token = 151645 '<|im_end|>'
llm_load_print_meta: UNK token = 128244 ''
llm_load_print_meta: PAD token = 0 '!'
llm_load_print_meta: LF token = 148848 'ÄĬ'
llm_load_print_meta: EOT token = 151645 '<|im_end|>'
llm_load_print_meta: max token length = 256
llm_load_tensors: ggml ctx size = 0.30 MiB
ggml_backend_metal_log_allocated_size: allocated buffer, size = 7164.02 MiB, ( 7164.08 / 10922.67)
llm_load_tensors: offloading 28 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 29/29 layers to GPU
llm_load_tensors: Metal buffer size = 7164.00 MiB
llm_load_tensors: CPU buffer size = 550.79 MiB
.......................................................................................
clip_model_load: description: image encoder for MiniCPM-V
clip_model_load: GGUF version: 3
clip_model_load: alignment: 32
clip_model_load: n_tensors: 455
clip_model_load: n_kv: 19
clip_model_load: ftype: f16

clip_model_load: loaded meta data with 19 key-value pairs and 455 tensors from /Users/sergio/Minicpmv2_6gguf/mmproj-model-f16.gguf
clip_model_load: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
clip_model_load: - kv 0: general.architecture str = clip
clip_model_load: - kv 1: clip.has_text_encoder bool = false
clip_model_load: - kv 2: clip.has_vision_encoder bool = true
clip_model_load: - kv 3: clip.has_minicpmv_projector bool = true
clip_model_load: - kv 4: general.file_type u32 = 1
clip_model_load: - kv 5: general.description str = image encoder for MiniCPM-V
clip_model_load: - kv 6: clip.projector_type str = resampler
clip_model_load: - kv 7: clip.minicpmv_version i32 = 3
clip_model_load: - kv 8: clip.vision.image_size u32 = 448
clip_model_load: - kv 9: clip.vision.patch_size u32 = 14
clip_model_load: - kv 10: clip.vision.embedding_length u32 = 1152
clip_model_load: - kv 11: clip.vision.feed_forward_length u32 = 4304
clip_model_load: - kv 12: clip.vision.projection_dim u32 = 0
clip_model_load: - kv 13: clip.vision.attention.head_count u32 = 16
clip_model_load: - kv 14: clip.vision.attention.layer_norm_epsilon f32 = 0.000001
clip_model_load: - kv 15: clip.vision.block_count u32 = 27
clip_model_load: - kv 16: clip.vision.image_mean arr[f32,3] = [0.500000, 0.500000, 0.500000]
clip_model_load: - kv 17: clip.vision.image_std arr[f32,3] = [0.500000, 0.500000, 0.500000]
clip_model_load: - kv 18: clip.use_gelu bool = true
clip_model_load: - type f32: 285 tensors
clip_model_load: - type f16: 170 tensors
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M1
ggml_metal_init: picking default device: Apple M1
ggml_metal_init: default.metallib not found, loading from source
ggml_metal_init: GGML_METAL_PATH_RESOURCES = nil
ggml_metal_init: loading '/Users/sergio/llama.cpp/ggml-metal.metal'
ggml_metal_init: GPU name: Apple M1
ggml_metal_init: GPU family: MTLGPUFamilyApple7 (1007)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3 (5001)
ggml_metal_init: simdgroup reduction support = true
ggml_metal_init: simdgroup matrix mul. support = true
ggml_metal_init: hasUnifiedMemory = true
ggml_metal_init: recommendedMaxWorkingSetSize = 11453.25 MB
clip_model_load: CLIP using Metal backend
clip_model_load: text_encoder: 0
clip_model_load: vision_encoder: 1
clip_model_load: llava_projector: 0
clip_model_load: minicpmv_projector: 1
clip_model_load: model size: 996.02 MB
clip_model_load: metadata size: 0.16 MB
clip_model_load: params backend buffer size = 996.02 MB (455 tensors)
key clip.vision.image_grid_pinpoints not found in file
key clip.vision.mm_patch_merge_type not found in file
key clip.vision.image_crop_resolution not found in file
clip_image_build_graph: 448 448
clip_model_load: compute allocated memory: 102.80 MB
llama_new_context_with_model: n_ctx = 8192
llama_new_context_with_model: n_batch = 2048
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M1
ggml_metal_init: picking default device: Apple M1
ggml_metal_init: default.metallib not found, loading from source
ggml_metal_init: GGML_METAL_PATH_RESOURCES = nil
ggml_metal_init: loading '/Users/sergio/llama.cpp/ggml-metal.metal'
ggml_metal_init: GPU name: Apple M1
ggml_metal_init: GPU family: MTLGPUFamilyApple7 (1007)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3 (5001)
ggml_metal_init: simdgroup reduction support = true
ggml_metal_init: simdgroup matrix mul. support = true
ggml_metal_init: hasUnifiedMemory = true
ggml_metal_init: recommendedMaxWorkingSetSize = 11453.25 MB
llama_kv_cache_init: Metal KV buffer size = 448.00 MiB
llama_new_context_with_model: KV self size = 448.00 MiB, K (f16): 224.00 MiB, V (f16): 224.00 MiB
llama_new_context_with_model: CPU output buffer size = 0.58 MiB
llama_new_context_with_model: Metal compute buffer size = 492.00 MiB
llama_new_context_with_model: CPU compute buffer size = 23.01 MiB
llama_new_context_with_model: graph nodes = 986
llama_new_context_with_model: graph splits = 2
Could not open video file.很抱歉，作为一个AI语言模型，我没有查看视频的能力。但是，我可以帮助您分析视频中可能出现的内容的文本描述或字幕。请提供更多的上下文信息，例如视频的主题、目的或预期的受众，以便我更好地协助您。%

期望行为 | Expected Behavior

能够描述视频内容

复现方法 | Steps To Reproduce

sergio@shenmeshiguojifenxishia ~ % cd llama.cpp
sergio@shenmeshiguojifenxishia llama.cpp % ./llama-minicpmv-cli -m /Users/sergio/Minicpmv2_6gguf/ggml-model-Q8_0.gguf --mmproj /Users/sergio/Minicpmv2_6gguf/mmproj-model-f16.gguf -c 8192 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --video ./IMG_0288.mp4 -p "我接下来会给你一个视频，请告诉我视频中出现了什么"
Log start
llama_model_loader: loaded meta data with 22 key-value pairs and 339 tensors from /Users/sergio/Minicpmv2_6gguf/ggml-model-Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = qwen2
llama_model_loader: - kv 1: general.name str = model
llama_model_loader: - kv 2: qwen2.block_count u32 = 28
llama_model_loader: - kv 3: qwen2.context_length u32 = 32768
llama_model_loader: - kv 4: qwen2.embedding_length u32 = 3584
llama_model_loader: - kv 5: qwen2.feed_forward_length u32 = 18944
llama_model_loader: - kv 6: qwen2.attention.head_count u32 = 28
llama_model_loader: - kv 7: qwen2.attention.head_count_kv u32 = 4
llama_model_loader: - kv 8: qwen2.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 9: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 10: general.file_type u32 = 7
llama_model_loader: - kv 11: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 12: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,151666] = ["!", """, "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,151666] = [3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 15: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 151644
llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 128244
llama_model_loader: - kv 19: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 20: tokenizer.chat_template str = {% for message in messages %}{% if lo...
llama_model_loader: - kv 21: general.quantization_version u32 = 2
llama_model_loader: - type f32: 141 tensors
llama_model_loader: - type q8_0: 198 tensors
llm_load_vocab: special tokens cache size = 25
llm_load_vocab: token to piece cache size = 0.9309 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = qwen2
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 151666
llm_load_print_meta: n_merges = 151387
llm_load_print_meta: n_ctx_train = 32768
llm_load_print_meta: n_embd = 3584
llm_load_print_meta: n_head = 28
llm_load_print_meta: n_head_kv = 4
llm_load_print_meta: n_layer = 28
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 7
llm_load_print_meta: n_embd_k_gqa = 512
llm_load_print_meta: n_embd_v_gqa = 512
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-06
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 18944
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 2
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 32768
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: model type = ?B
llm_load_print_meta: model ftype = Q8_0
llm_load_print_meta: model params = 7.61 B
llm_load_print_meta: model size = 7.53 GiB (8.50 BPW)
llm_load_print_meta: general.name = model
llm_load_print_meta: BOS token = 151644 '<|im_start|>'
llm_load_print_meta: EOS token = 151645 '<|im_end|>'
llm_load_print_meta: UNK token = 128244 ''
llm_load_print_meta: PAD token = 0 '!'
llm_load_print_meta: LF token = 148848 'ÄĬ'
llm_load_print_meta: EOT token = 151645 '<|im_end|>'
llm_load_print_meta: max token length = 256
llm_load_tensors: ggml ctx size = 0.30 MiB
ggml_backend_metal_log_allocated_size: allocated buffer, size = 7164.02 MiB, ( 7164.08 / 10922.67)
llm_load_tensors: offloading 28 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 29/29 layers to GPU
llm_load_tensors: Metal buffer size = 7164.00 MiB
llm_load_tensors: CPU buffer size = 550.79 MiB
.......................................................................................
clip_model_load: description: image encoder for MiniCPM-V
clip_model_load: GGUF version: 3
clip_model_load: alignment: 32
clip_model_load: n_tensors: 455
clip_model_load: n_kv: 19
clip_model_load: ftype: f16

clip_model_load: loaded meta data with 19 key-value pairs and 455 tensors from /Users/sergio/Minicpmv2_6gguf/mmproj-model-f16.gguf
clip_model_load: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
clip_model_load: - kv 0: general.architecture str = clip
clip_model_load: - kv 1: clip.has_text_encoder bool = false
clip_model_load: - kv 2: clip.has_vision_encoder bool = true
clip_model_load: - kv 3: clip.has_minicpmv_projector bool = true
clip_model_load: - kv 4: general.file_type u32 = 1
clip_model_load: - kv 5: general.description str = image encoder for MiniCPM-V
clip_model_load: - kv 6: clip.projector_type str = resampler
clip_model_load: - kv 7: clip.minicpmv_version i32 = 3
clip_model_load: - kv 8: clip.vision.image_size u32 = 448
clip_model_load: - kv 9: clip.vision.patch_size u32 = 14
clip_model_load: - kv 10: clip.vision.embedding_length u32 = 1152
clip_model_load: - kv 11: clip.vision.feed_forward_length u32 = 4304
clip_model_load: - kv 12: clip.vision.projection_dim u32 = 0
clip_model_load: - kv 13: clip.vision.attention.head_count u32 = 16
clip_model_load: - kv 14: clip.vision.attention.layer_norm_epsilon f32 = 0.000001
clip_model_load: - kv 15: clip.vision.block_count u32 = 27
clip_model_load: - kv 16: clip.vision.image_mean arr[f32,3] = [0.500000, 0.500000, 0.500000]
clip_model_load: - kv 17: clip.vision.image_std arr[f32,3] = [0.500000, 0.500000, 0.500000]
clip_model_load: - kv 18: clip.use_gelu bool = true
clip_model_load: - type f32: 285 tensors
clip_model_load: - type f16: 170 tensors
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M1
ggml_metal_init: picking default device: Apple M1
ggml_metal_init: default.metallib not found, loading from source
ggml_metal_init: GGML_METAL_PATH_RESOURCES = nil
ggml_metal_init: loading '/Users/sergio/llama.cpp/ggml-metal.metal'
ggml_metal_init: GPU name: Apple M1
ggml_metal_init: GPU family: MTLGPUFamilyApple7 (1007)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3 (5001)
ggml_metal_init: simdgroup reduction support = true
ggml_metal_init: simdgroup matrix mul. support = true
ggml_metal_init: hasUnifiedMemory = true
ggml_metal_init: recommendedMaxWorkingSetSize = 11453.25 MB
clip_model_load: CLIP using Metal backend
clip_model_load: text_encoder: 0
clip_model_load: vision_encoder: 1
clip_model_load: llava_projector: 0
clip_model_load: minicpmv_projector: 1
clip_model_load: model size: 996.02 MB
clip_model_load: metadata size: 0.16 MB
clip_model_load: params backend buffer size = 996.02 MB (455 tensors)
key clip.vision.image_grid_pinpoints not found in file
key clip.vision.mm_patch_merge_type not found in file
key clip.vision.image_crop_resolution not found in file
clip_image_build_graph: 448 448
clip_model_load: compute allocated memory: 102.80 MB
llama_new_context_with_model: n_ctx = 8192
llama_new_context_with_model: n_batch = 2048
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M1
ggml_metal_init: picking default device: Apple M1
ggml_metal_init: default.metallib not found, loading from source
ggml_metal_init: GGML_METAL_PATH_RESOURCES = nil
ggml_metal_init: loading '/Users/sergio/llama.cpp/ggml-metal.metal'
ggml_metal_init: GPU name: Apple M1
ggml_metal_init: GPU family: MTLGPUFamilyApple7 (1007)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3 (5001)
ggml_metal_init: simdgroup reduction support = true
ggml_metal_init: simdgroup matrix mul. support = true
ggml_metal_init: hasUnifiedMemory = true
ggml_metal_init: recommendedMaxWorkingSetSize = 11453.25 MB
llama_kv_cache_init: Metal KV buffer size = 448.00 MiB
llama_new_context_with_model: KV self size = 448.00 MiB, K (f16): 224.00 MiB, V (f16): 224.00 MiB
llama_new_context_with_model: CPU output buffer size = 0.58 MiB
llama_new_context_with_model: Metal compute buffer size = 492.00 MiB
llama_new_context_with_model: CPU compute buffer size = 23.01 MiB
llama_new_context_with_model: graph nodes = 986
llama_new_context_with_model: graph splits = 2
Could not open video file.很抱歉，我无法观看视频。我是由OpenAI训练的大型语言模型，没有浏览互联网或访问视觉媒体的能力。我只能根据我所接受的信息和知识回答问题。如果您有关于特定主题的问题，我会尽力提供准确和有用的答案。%

运行环境 | Environment

- OS: Macos Sonoma 14.5
- Python:3.13.2
- Transformers:4.48.3
- PyTorch:2.6
- CUDA (`python -c 'import torch; print(torch.version.cuda)'`):

备注 | Anything else?

No response

[guoQiNing]

Could not open video file.看起来视频文件没有正确被打开。是不是您的文件路径有问题？可以尝试使用绝对路径。我使用minicpmv-main分支的最新代码以及huggingface上提供的模型能够构建运行并描述视频内容。

cmake -B build -DLLAMA_CUDA=ON
cmake --build build --config Release -t llama-minicpmv-cli

./build/bin/Release/llama-minicpmv-cli -m xxx/ggml-model-Q4_K_M.gguf --mmproj xxx/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --video xxx\video_0.mp4  -p "What is in the video?"

可以正常得到结果：

Log start
llama_model_loader: loaded meta data with 24 key-value pairs and 291 tensors from D:/000_cpm_V_0208/llama.cpp/models/1/ggml-model-Q4_K_M.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = llama
llama_model_loader: - kv   1:                               general.name str              = model
......
encode_image_with_clip: image encoded in   482.23 ms by CLIP (    1.67 ms per image patch)
process_image: image token past: 1205
process_image: image token past: 1305
A group of people are sitting in a lounge area.

不过，我使用的是Windows系统而不是Macos，可能存在一些差异。