Qwen VL - how can I not output logs other than the model's response?

[prt0117]

Hello Dear Community!

I am trying to use the Qwen VL, but I cannot suppress the output of logs before and after the model's response. For example, the following command

`./build/bin/llama-qwen2vl-cli -m ./models/Qwen2-VL-7B-Instruct-Q4_0.gguf --mmproj ./models/mmproj-Qwen2-VL-7B-Instruct-f32.gguf -p 'Describe this image.' --image './models/test_image.jpg'`

outputs:

build: 4383 (485dc012) with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
llama_model_loader: loaded meta data with 37 key-value pairs and 339 tensors from ./models/Qwen2-VL-7B-Instruct-Q4_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              = qwen2vl
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Qwen2 VL 7B Instruct
llama_model_loader: - kv   3:                           general.finetune str              = Instruct
llama_model_loader: - kv   4:                           general.basename str              = Qwen2-VL
llama_model_loader: - kv   5:                         general.size_label str              = 7B
llama_model_loader: - kv   6:                            general.license str              = apache-2.0
llama_model_loader: - kv   7:                   general.base_model.count u32              = 1
llama_model_loader: - kv   8:                  general.base_model.0.name str              = Qwen2 VL 7B
llama_model_loader: - kv   9:          general.base_model.0.organization str              = Qwen
llama_model_loader: - kv  10:              general.base_model.0.repo_url str              = https://huggingface.co/Qwen/Qwen2-VL-7B
llama_model_loader: - kv  11:                               general.tags arr[str,2]       = ["multimodal", "image-text-to-text"]
llama_model_loader: - kv  12:                          general.languages arr[str,1]       = ["en"]
llama_model_loader: - kv  13:                        qwen2vl.block_count u32              = 28
llama_model_loader: - kv  14:                     qwen2vl.context_length u32              = 32768
llama_model_loader: - kv  15:                   qwen2vl.embedding_length u32              = 3584
llama_model_loader: - kv  16:                qwen2vl.feed_forward_length u32              = 18944
llama_model_loader: - kv  17:               qwen2vl.attention.head_count u32              = 28
llama_model_loader: - kv  18:            qwen2vl.attention.head_count_kv u32              = 4
llama_model_loader: - kv  19:                     qwen2vl.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv  20:   qwen2vl.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  21:                          general.file_type u32              = 2
llama_model_loader: - kv  22:            qwen2vl.rope.dimension_sections arr[i32,4]       = [16, 24, 24, 0]
llama_model_loader: - kv  23:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  24:                         tokenizer.ggml.pre str              = qwen2
llama_model_loader: - kv  25:                      tokenizer.ggml.tokens arr[str,152064]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  26:                  tokenizer.ggml.token_type arr[i32,152064]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  27:                      tokenizer.ggml.merges arr[str,151387]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv  28:                tokenizer.ggml.eos_token_id u32              = 151645
llama_model_loader: - kv  29:            tokenizer.ggml.padding_token_id u32              = 151643
llama_model_loader: - kv  30:                tokenizer.ggml.bos_token_id u32              = 151643
llama_model_loader: - kv  31:                    tokenizer.chat_template str              = {% set image_count = namespace(value=...
llama_model_loader: - kv  32:               general.quantization_version u32              = 2
llama_model_loader: - kv  33:                      quantize.imatrix.file str              = /models_out/Qwen2-VL-7B-Instruct-GGUF...
llama_model_loader: - kv  34:                   quantize.imatrix.dataset str              = /training_dir/calibration_datav3.txt
llama_model_loader: - kv  35:             quantize.imatrix.entries_count i32              = 196
llama_model_loader: - kv  36:              quantize.imatrix.chunks_count i32              = 128
llama_model_loader: - type  f32:  141 tensors
llama_model_loader: - type q4_0:  194 tensors
llama_model_loader: - type q4_1:    3 tensors
llama_model_loader: - type q6_K:    1 tensors
llm_load_vocab: special tokens cache size = 14
llm_load_vocab: token to piece cache size = 0.9309 MB
llm_load_print_meta: format           = GGUF V3 (latest)
llm_load_print_meta: arch             = qwen2vl
llm_load_print_meta: vocab type       = BPE
llm_load_print_meta: n_vocab          = 152064
llm_load_print_meta: n_merges         = 151387
llm_load_print_meta: vocab_only       = 0
llm_load_print_meta: n_ctx_train      = 32768
llm_load_print_meta: n_embd           = 3584
llm_load_print_meta: n_layer          = 28
llm_load_print_meta: n_head           = 28
llm_load_print_meta: n_head_kv        = 4
llm_load_print_meta: n_rot            = 128
llm_load_print_meta: n_swa            = 0
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        = 8
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: ssm_dt_b_c_rms   = 0
llm_load_print_meta: model type       = 7B
llm_load_print_meta: model ftype      = Q4_0
llm_load_print_meta: model params     = 7.62 B
llm_load_print_meta: model size       = 4.13 GiB (4.66 BPW) 
llm_load_print_meta: general.name     = Qwen2 VL 7B Instruct
llm_load_print_meta: BOS token        = 151643 '<|endoftext|>'
llm_load_print_meta: EOS token        = 151645 '<|im_end|>'
llm_load_print_meta: EOT token        = 151645 '<|im_end|>'
llm_load_print_meta: PAD token        = 151643 '<|endoftext|>'
llm_load_print_meta: LF token         = 148848 'ÄĬ'
llm_load_print_meta: EOG token        = 151643 '<|endoftext|>'
llm_load_print_meta: EOG token        = 151645 '<|im_end|>'
llm_load_print_meta: max token length = 256
llm_load_tensors:   CPU_Mapped model buffer size =  4196.14 MiB
llm_load_tensors:  CPU_AARCH64 model buffer size =  3391.17 MiB
......................................................................................
clip_model_load: model name:   Qwen2-VL-7B-Instruct
clip_model_load: description:  image encoder for Qwen2VL
clip_model_load: GGUF version: 3
clip_model_load: alignment:    32
clip_model_load: n_tensors:    521
clip_model_load: n_kv:         20
clip_model_load: ftype:        f32

clip_model_load: loaded meta data with 20 key-value pairs and 521 tensors from ./models/mmproj-Qwen2-VL-7B-Instruct-f32.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:                        general.description str              = image encoder for Qwen2VL
clip_model_load: - kv   2:                          general.file_type u32              = 0
clip_model_load: - kv   3:                      clip.has_text_encoder bool             = false
clip_model_load: - kv   4:                    clip.has_vision_encoder bool             = true
clip_model_load: - kv   5:                    clip.has_qwen2vl_merger bool             = true
clip_model_load: - kv   6:                        clip.projector_type str              = qwen2vl_merger
clip_model_load: - kv   7:                              clip.use_silu bool             = false
clip_model_load: - kv   8:                              clip.use_gelu bool             = false
clip_model_load: - kv   9:                     clip.vision.patch_size u32              = 14
clip_model_load: - kv  10:                     clip.vision.image_size u32              = 560
clip_model_load: - kv  11:               clip.vision.embedding_length u32              = 1280
clip_model_load: - kv  12:                 clip.vision.projection_dim u32              = 3584
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              = 32
clip_model_load: - kv  16:            clip.vision.feed_forward_length u32              = 0
clip_model_load: - kv  17:                               general.name str              = Qwen2-VL-7B-Instruct
clip_model_load: - kv  18:                     clip.vision.image_mean arr[f32,3]       = [0.481455, 0.457828, 0.408211]
clip_model_load: - kv  19:                      clip.vision.image_std arr[f32,3]       = [0.268630, 0.261303, 0.275777]
clip_model_load: - type  f32:  521 tensors
clip_model_load: CLIP using CPU backend
clip_model_load: text_encoder:   0
clip_model_load: vision_encoder: 1
clip_model_load: llava_projector:  0
clip_model_load: minicpmv_projector:  0
clip_model_load: model size:     2577.82 MB
clip_model_load: metadata size:  0.18 MB
clip_model_load: params backend buffer size =  2577.82 MB (521 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_model_load: compute allocated memory: 198.93 MB
llama_new_context_with_model: n_seq_max     = 1
llama_new_context_with_model: n_ctx         = 4096
llama_new_context_with_model: n_ctx_per_seq = 4096
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
llama_new_context_with_model: n_ctx_per_seq (4096) < n_ctx_train (32768) -- the full capacity of the model will not be utilized
llama_kv_cache_init: kv_size = 4096, offload = 1, type_k = 'f16', type_v = 'f16', n_layer = 28
llama_kv_cache_init:        CPU KV buffer size =   224.00 MiB
llama_new_context_with_model: KV self size  =  224.00 MiB, K (f16):  112.00 MiB, V (f16):  112.00 MiB
llama_new_context_with_model:        CPU  output buffer size =     0.58 MiB
llama_new_context_with_model:        CPU compute buffer size =   304.00 MiB
llama_new_context_with_model: graph nodes  = 986
llama_new_context_with_model: graph splits = 1
encode_image_with_clip: step 1 of 1 encoded in 71298.16 ms
encode_image_with_clip: all 1 segments encoded in 71298.19 ms
encode_image_with_clip: load_image_size 958 539
encode_image_with_clip: image embedding created: 700 tokens

Then it outputs the response:

The image depicts a street scene where two individuals appear to be...

And then it outputs a little more logs:

llama_perf_context_print:        load time =  135007.65 ms
llama_perf_context_print: prompt eval time =   58405.58 ms /   725 tokens (   80.56 ms per token,    12.41 tokens per second)
llama_perf_context_print:        eval time =   47276.63 ms /   255 runs   (  185.40 ms per token,     5.39 tokens per second)
llama_perf_context_print:       total time =  182521.68 ms /   980 tokens

Could you please help me to disable everything around the response so that it only writes to the terminal:

The image depicts a street scene where two individuals appear to be...

I was trying --log-disable and 2>/dev/null but it didn't help.

Thank you in advance!