Open
Description
Name and Version
./build/bin/llama-cli --version
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: yes
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes
version: 0 (unknown)
built with cc (GCC) 13.3.1 20240611 (Red Hat 13.3.1-2) for x86_64-redhat-linux
(newest b4876 version)
Operating systems
Linux
GGML backends
CUDA
Hardware
Ryzen 3900x + rtx 3060 12gb
Models
Gemma-3-12b_Q5_K_M
Problem description & steps to reproduce
Prompt eval time is way slower when using quantized kv cache than standard kv cache. Also I see that the cpu is used when the quantized kv cache is turned on. So I believe that the kv cache is not properly processed by the gpu if the quantized kv cache is provided
First Bad Commit
No response
Relevant log output
# Unquantized kv cache:
./build/bin/llama-server -m '/home/luis/Downloads/llama.cpp-b4876/models/gemma-3-12b-it-Q5_K_M.gguf' --n-gpu-layers -1 --batch_size 1024 --flash-attn -c 4000 --port 7777 -t 8 -ngl 99
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: yes
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes
build: 0 (unknown) with cc (GCC) 13.3.1 20240611 (Red Hat 13.3.1-2) for x86_64-redhat-linux
system info: n_threads = 8, n_threads_batch = 8, total_threads = 24
system_info: n_threads = 8 (n_threads_batch = 8) / 24 | CUDA : ARCHS = 860 | FORCE_CUBLAS = 1 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | OPENMP = 1 | AARCH64_REPACK = 1 |
main: HTTP server is listening, hostname: 127.0.0.1, port: 7777, http threads: 23
main: loading model
srv load_model: loading model '/home/luis/Downloads/llama.cpp-b4876/models/gemma-3-12b-it-Q5_K_M.gguf'
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 3060) - 10456 MiB free
llama_model_loader: loaded meta data with 34 key-value pairs and 626 tensors from /home/luis/Downloads/llama.cpp-b4876/models/gemma-3-12b-it-Q5_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 = gemma3
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Gemma 3
llama_model_loader: - kv 3: general.quantized_by str = Unsloth
llama_model_loader: - kv 4: general.size_label str = 12B
llama_model_loader: - kv 5: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 6: gemma3.context_length u32 = 131072
llama_model_loader: - kv 7: gemma3.embedding_length u32 = 3840
llama_model_loader: - kv 8: gemma3.block_count u32 = 48
llama_model_loader: - kv 9: gemma3.feed_forward_length u32 = 15360
llama_model_loader: - kv 10: gemma3.attention.head_count u32 = 16
llama_model_loader: - kv 11: gemma3.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 12: gemma3.attention.key_length u32 = 256
llama_model_loader: - kv 13: gemma3.attention.value_length u32 = 256
llama_model_loader: - kv 14: gemma3.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 15: gemma3.attention.sliding_window u32 = 1024
llama_model_loader: - kv 16: gemma3.attention.head_count_kv u32 = 8
llama_model_loader: - kv 17: gemma3.rope.scaling.type str = linear
llama_model_loader: - kv 18: gemma3.rope.scaling.factor f32 = 8.000000
llama_model_loader: - kv 19: tokenizer.ggml.model str = llama
llama_model_loader: - kv 20: tokenizer.ggml.pre str = default
llama_model_loader: - kv 21: tokenizer.ggml.tokens arr[str,262208] = ["<pad>", "<eos>", "<bos>", "<unk>", ...
llama_model_loader: - kv 22: tokenizer.ggml.scores arr[f32,262208] = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv 23: tokenizer.ggml.token_type arr[i32,262208] = [3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, ...
llama_model_loader: - kv 24: tokenizer.ggml.bos_token_id u32 = 2
llama_model_loader: - kv 25: tokenizer.ggml.eos_token_id u32 = 106
llama_model_loader: - kv 26: tokenizer.ggml.unknown_token_id u32 = 3
llama_model_loader: - kv 27: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 28: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 29: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 30: tokenizer.chat_template str = {{ bos_token }}\n{%- if messages[0]['r...
llama_model_loader: - kv 31: tokenizer.ggml.add_space_prefix bool = false
llama_model_loader: - kv 32: general.quantization_version u32 = 2
llama_model_loader: - kv 33: general.file_type u32 = 17
llama_model_loader: - type f32: 289 tensors
llama_model_loader: - type q5_K: 288 tensors
llama_model_loader: - type q6_K: 49 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q5_K - Medium
print_info: file size = 7.86 GiB (5.74 BPW)
load: special tokens cache size = 6415
load: token to piece cache size = 1.9446 MB
print_info: arch = gemma3
print_info: vocab_only = 0
print_info: n_ctx_train = 131072
print_info: n_embd = 3840
print_info: n_layer = 48
print_info: n_head = 16
print_info: n_head_kv = 8
print_info: n_rot = 256
print_info: n_swa = 1024
print_info: n_embd_head_k = 256
print_info: n_embd_head_v = 256
print_info: n_gqa = 2
print_info: n_embd_k_gqa = 2048
print_info: n_embd_v_gqa = 2048
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 6.2e-02
print_info: n_ff = 15360
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: causal attn = 1
print_info: pooling type = 0
print_info: rope type = 2
print_info: rope scaling = linear
print_info: freq_base_train = 1000000.0
print_info: freq_scale_train = 0.125
print_info: n_ctx_orig_yarn = 131072
print_info: rope_finetuned = unknown
print_info: ssm_d_conv = 0
print_info: ssm_d_inner = 0
print_info: ssm_d_state = 0
print_info: ssm_dt_rank = 0
print_info: ssm_dt_b_c_rms = 0
print_info: model type = 12B
print_info: model params = 11.77 B
print_info: general.name = Gemma 3
print_info: vocab type = SPM
print_info: n_vocab = 262208
print_info: n_merges = 0
print_info: BOS token = 2 '<bos>'
print_info: EOS token = 106 '<end_of_turn>'
print_info: EOT token = 106 '<end_of_turn>'
print_info: UNK token = 3 '<unk>'
print_info: PAD token = 0 '<pad>'
print_info: LF token = 248 '<0x0A>'
print_info: EOG token = 106 '<end_of_turn>'
print_info: max token length = 48
load_tensors: loading model tensors, this can take a while... (mmap = true)
load_tensors: offloading 48 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 49/49 layers to GPU
load_tensors: CPU_Mapped model buffer size = 787.69 MiB
load_tensors: CUDA0 model buffer size = 8047.63 MiB
.....................................................................................
llama_init_from_model: n_seq_max = 1
llama_init_from_model: n_ctx = 4096
llama_init_from_model: n_ctx_per_seq = 4096
llama_init_from_model: n_batch = 1024
llama_init_from_model: n_ubatch = 512
llama_init_from_model: flash_attn = 1
llama_init_from_model: freq_base = 1000000.0
llama_init_from_model: freq_scale = 0.125
llama_init_from_model: n_ctx_per_seq (4096) < n_ctx_train (131072) -- 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 = 48, can_shift = 1
llama_kv_cache_init: CUDA0 KV buffer size = 1536.00 MiB
llama_init_from_model: KV self size = 1536.00 MiB, K (f16): 768.00 MiB, V (f16): 768.00 MiB
llama_init_from_model: CUDA_Host output buffer size = 1.00 MiB
llama_init_from_model: CUDA0 compute buffer size = 519.62 MiB
llama_init_from_model: CUDA_Host compute buffer size = 23.51 MiB
llama_init_from_model: graph nodes = 1737
llama_init_from_model: graph splits = 2
common_init_from_params: setting dry_penalty_last_n to ctx_size = 4096
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
srv init: initializing slots, n_slots = 1
slot init: id 0 | task -1 | new slot n_ctx_slot = 4096
main: model loaded
main: chat template, chat_template: {{ bos_token }}
{%- if messages[0]['role'] == 'system' -%}
{%- if messages[0]['content'] is string -%}
{%- set first_user_prefix = messages[0]['content'] + '
' -%}
{%- else -%}
{%- set first_user_prefix = messages[0]['content'][0]['text'] + '
' -%}
{%- endif -%}
{%- set loop_messages = messages[1:] -%}
{%- else -%}
{%- set first_user_prefix = "" -%}
{%- set loop_messages = messages -%}
{%- endif -%}
{%- for message in loop_messages -%}
{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
{%- endif -%}
{%- if (message['role'] == 'assistant') -%}
{%- set role = "model" -%}
{%- else -%}
{%- set role = message['role'] -%}
{%- endif -%}
{{ '<start_of_turn>' + role + '
' + (first_user_prefix if loop.first else "") }}
{%- if message['content'] is string -%}
{{ message['content'] | trim }}
{%- elif message['content'] is iterable -%}
{%- for item in message['content'] -%}
{%- if item['type'] == 'image' -%}
{{ '<start_of_image>' }}
{%- elif item['type'] == 'text' -%}
{{ item['text'] | trim }}
{%- endif -%}
{%- endfor -%}
{%- else -%}
{{ raise_exception("Invalid content type") }}
{%- endif -%}
{{ '<end_of_turn>
' }}
{%- endfor -%}
{%- if add_generation_prompt -%}
{{'<start_of_turn>model
'}}
{%- endif -%}
, example_format: '<start_of_turn>user
You are a helpful assistant
Hello<end_of_turn>
<start_of_turn>model
Hi there<end_of_turn>
<start_of_turn>user
How are you?<end_of_turn>
<start_of_turn>model
'
main: server is listening on http://127.0.0.1:7777 - starting the main loop
srv update_slots: all slots are idle
srv params_from_: Chat format: Content-only
slot launch_slot_: id 0 | task 0 | processing task
slot update_slots: id 0 | task 0 | new prompt, n_ctx_slot = 4096, n_keep = 0, n_prompt_tokens = 2505
slot update_slots: id 0 | task 0 | kv cache rm [0, end)
slot update_slots: id 0 | task 0 | prompt processing progress, n_past = 1024, n_tokens = 1024, progress = 0.408782
slot update_slots: id 0 | task 0 | kv cache rm [1024, end)
slot update_slots: id 0 | task 0 | prompt processing progress, n_past = 2048, n_tokens = 1024, progress = 0.817565
slot update_slots: id 0 | task 0 | kv cache rm [2048, end)
slot update_slots: id 0 | task 0 | prompt processing progress, n_past = 2505, n_tokens = 457, progress = 1.000000
slot update_slots: id 0 | task 0 | prompt done, n_past = 2505, n_tokens = 457
slot release: id 0 | task 0 | stop processing: n_past = 3199, truncated = 0
slot print_timing: id 0 | task 0 |
prompt eval time = 2697.39 ms / 2505 tokens ( 1.08 ms per token, 928.67 tokens per second)
eval time = 24911.73 ms / 695 tokens ( 35.84 ms per token, 27.90 tokens per second)
total time = 27609.12 ms / 3200 tokens
srv update_slots: all slots are idle
srv log_server_r: request: POST /v1/chat/completions 127.0.0.1 200
# Quantized kv cache:
./build/bin/llama-server -m '/home/luis/Downloads/llama.cpp-b4876/models/gemma-3-12b-it-Q5_K_M.gguf' --n-gpu-layers -1 --cache-type-k q8_0 --cache-type-v q8_0 --batch_size 1024 --flash-attn -c 4000 --port 7777 -t 8 -ngl 99
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: yes
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes
build: 0 (unknown) with cc (GCC) 13.3.1 20240611 (Red Hat 13.3.1-2) for x86_64-redhat-linux
system info: n_threads = 8, n_threads_batch = 8, total_threads = 24
system_info: n_threads = 8 (n_threads_batch = 8) / 24 | CUDA : ARCHS = 860 | FORCE_CUBLAS = 1 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | OPENMP = 1 | AARCH64_REPACK = 1 |
main: HTTP server is listening, hostname: 127.0.0.1, port: 7777, http threads: 23
main: loading model
srv load_model: loading model '/home/luis/Downloads/llama.cpp-b4876/models/gemma-3-12b-it-Q5_K_M.gguf'
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 3060) - 10500 MiB free
llama_model_loader: loaded meta data with 34 key-value pairs and 626 tensors from /home/luis/Downloads/llama.cpp-b4876/models/gemma-3-12b-it-Q5_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 = gemma3
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Gemma 3
llama_model_loader: - kv 3: general.quantized_by str = Unsloth
llama_model_loader: - kv 4: general.size_label str = 12B
llama_model_loader: - kv 5: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 6: gemma3.context_length u32 = 131072
llama_model_loader: - kv 7: gemma3.embedding_length u32 = 3840
llama_model_loader: - kv 8: gemma3.block_count u32 = 48
llama_model_loader: - kv 9: gemma3.feed_forward_length u32 = 15360
llama_model_loader: - kv 10: gemma3.attention.head_count u32 = 16
llama_model_loader: - kv 11: gemma3.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 12: gemma3.attention.key_length u32 = 256
llama_model_loader: - kv 13: gemma3.attention.value_length u32 = 256
llama_model_loader: - kv 14: gemma3.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 15: gemma3.attention.sliding_window u32 = 1024
llama_model_loader: - kv 16: gemma3.attention.head_count_kv u32 = 8
llama_model_loader: - kv 17: gemma3.rope.scaling.type str = linear
llama_model_loader: - kv 18: gemma3.rope.scaling.factor f32 = 8.000000
llama_model_loader: - kv 19: tokenizer.ggml.model str = llama
llama_model_loader: - kv 20: tokenizer.ggml.pre str = default
llama_model_loader: - kv 21: tokenizer.ggml.tokens arr[str,262208] = ["<pad>", "<eos>", "<bos>", "<unk>", ...
llama_model_loader: - kv 22: tokenizer.ggml.scores arr[f32,262208] = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv 23: tokenizer.ggml.token_type arr[i32,262208] = [3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, ...
llama_model_loader: - kv 24: tokenizer.ggml.bos_token_id u32 = 2
llama_model_loader: - kv 25: tokenizer.ggml.eos_token_id u32 = 106
llama_model_loader: - kv 26: tokenizer.ggml.unknown_token_id u32 = 3
llama_model_loader: - kv 27: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 28: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 29: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 30: tokenizer.chat_template str = {{ bos_token }}\n{%- if messages[0]['r...
llama_model_loader: - kv 31: tokenizer.ggml.add_space_prefix bool = false
llama_model_loader: - kv 32: general.quantization_version u32 = 2
llama_model_loader: - kv 33: general.file_type u32 = 17
llama_model_loader: - type f32: 289 tensors
llama_model_loader: - type q5_K: 288 tensors
llama_model_loader: - type q6_K: 49 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q5_K - Medium
print_info: file size = 7.86 GiB (5.74 BPW)
load: special tokens cache size = 6415
load: token to piece cache size = 1.9446 MB
print_info: arch = gemma3
print_info: vocab_only = 0
print_info: n_ctx_train = 131072
print_info: n_embd = 3840
print_info: n_layer = 48
print_info: n_head = 16
print_info: n_head_kv = 8
print_info: n_rot = 256
print_info: n_swa = 1024
print_info: n_embd_head_k = 256
print_info: n_embd_head_v = 256
print_info: n_gqa = 2
print_info: n_embd_k_gqa = 2048
print_info: n_embd_v_gqa = 2048
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 6.2e-02
print_info: n_ff = 15360
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: causal attn = 1
print_info: pooling type = 0
print_info: rope type = 2
print_info: rope scaling = linear
print_info: freq_base_train = 1000000.0
print_info: freq_scale_train = 0.125
print_info: n_ctx_orig_yarn = 131072
print_info: rope_finetuned = unknown
print_info: ssm_d_conv = 0
print_info: ssm_d_inner = 0
print_info: ssm_d_state = 0
print_info: ssm_dt_rank = 0
print_info: ssm_dt_b_c_rms = 0
print_info: model type = 12B
print_info: model params = 11.77 B
print_info: general.name = Gemma 3
print_info: vocab type = SPM
print_info: n_vocab = 262208
print_info: n_merges = 0
print_info: BOS token = 2 '<bos>'
print_info: EOS token = 106 '<end_of_turn>'
print_info: EOT token = 106 '<end_of_turn>'
print_info: UNK token = 3 '<unk>'
print_info: PAD token = 0 '<pad>'
print_info: LF token = 248 '<0x0A>'
print_info: EOG token = 106 '<end_of_turn>'
print_info: max token length = 48
load_tensors: loading model tensors, this can take a while... (mmap = true)
load_tensors: offloading 48 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 49/49 layers to GPU
load_tensors: CPU_Mapped model buffer size = 787.69 MiB
load_tensors: CUDA0 model buffer size = 8047.63 MiB
.....................................................................................
llama_init_from_model: n_seq_max = 1
llama_init_from_model: n_ctx = 4096
llama_init_from_model: n_ctx_per_seq = 4096
llama_init_from_model: n_batch = 1024
llama_init_from_model: n_ubatch = 512
llama_init_from_model: flash_attn = 1
llama_init_from_model: freq_base = 1000000.0
llama_init_from_model: freq_scale = 0.125
llama_init_from_model: n_ctx_per_seq (4096) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
llama_kv_cache_init: kv_size = 4096, offload = 1, type_k = 'q8_0', type_v = 'q8_0', n_layer = 48, can_shift = 1
llama_kv_cache_init: CUDA0 KV buffer size = 816.00 MiB
llama_init_from_model: KV self size = 816.00 MiB, K (q8_0): 408.00 MiB, V (q8_0): 408.00 MiB
llama_init_from_model: CUDA_Host output buffer size = 1.00 MiB
llama_init_from_model: CUDA0 compute buffer size = 519.62 MiB
llama_init_from_model: CUDA_Host compute buffer size = 45.01 MiB
llama_init_from_model: graph nodes = 1737
llama_init_from_model: graph splits = 98
common_init_from_params: setting dry_penalty_last_n to ctx_size = 4096
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
srv init: initializing slots, n_slots = 1
slot init: id 0 | task -1 | new slot n_ctx_slot = 4096
main: model loaded
main: chat template, chat_template: {{ bos_token }}
{%- if messages[0]['role'] == 'system' -%}
{%- if messages[0]['content'] is string -%}
{%- set first_user_prefix = messages[0]['content'] + '
' -%}
{%- else -%}
{%- set first_user_prefix = messages[0]['content'][0]['text'] + '
' -%}
{%- endif -%}
{%- set loop_messages = messages[1:] -%}
{%- else -%}
{%- set first_user_prefix = "" -%}
{%- set loop_messages = messages -%}
{%- endif -%}
{%- for message in loop_messages -%}
{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
{%- endif -%}
{%- if (message['role'] == 'assistant') -%}
{%- set role = "model" -%}
{%- else -%}
{%- set role = message['role'] -%}
{%- endif -%}
{{ '<start_of_turn>' + role + '
' + (first_user_prefix if loop.first else "") }}
{%- if message['content'] is string -%}
{{ message['content'] | trim }}
{%- elif message['content'] is iterable -%}
{%- for item in message['content'] -%}
{%- if item['type'] == 'image' -%}
{{ '<start_of_image>' }}
{%- elif item['type'] == 'text' -%}
{{ item['text'] | trim }}
{%- endif -%}
{%- endfor -%}
{%- else -%}
{{ raise_exception("Invalid content type") }}
{%- endif -%}
{{ '<end_of_turn>
' }}
{%- endfor -%}
{%- if add_generation_prompt -%}
{{'<start_of_turn>model
'}}
{%- endif -%}
, example_format: '<start_of_turn>user
You are a helpful assistant
Hello<end_of_turn>
<start_of_turn>model
Hi there<end_of_turn>
<start_of_turn>user
How are you?<end_of_turn>
<start_of_turn>model
'
main: server is listening on http://127.0.0.1:7777 - starting the main loop
srv update_slots: all slots are idle
srv params_from_: Chat format: Content-only
slot launch_slot_: id 0 | task 0 | processing task
slot update_slots: id 0 | task 0 | new prompt, n_ctx_slot = 4096, n_keep = 0, n_prompt_tokens = 2505
slot update_slots: id 0 | task 0 | kv cache rm [0, end)
slot update_slots: id 0 | task 0 | prompt processing progress, n_past = 1024, n_tokens = 1024, progress = 0.408782
slot update_slots: id 0 | task 0 | kv cache rm [1024, end)
slot update_slots: id 0 | task 0 | prompt processing progress, n_past = 2048, n_tokens = 1024, progress = 0.817565
slot update_slots: id 0 | task 0 | kv cache rm [2048, end)
slot update_slots: id 0 | task 0 | prompt processing progress, n_past = 2505, n_tokens = 457, progress = 1.000000
slot update_slots: id 0 | task 0 | prompt done, n_past = 2505, n_tokens = 457
slot release: id 0 | task 0 | stop processing: n_past = 3209, truncated = 0
slot print_timing: id 0 | task 0 |
prompt eval time = 23150.35 ms / 2505 tokens ( 9.24 ms per token, 108.21 tokens per second)
eval time = 78076.05 ms / 705 tokens ( 110.75 ms per token, 9.03 tokens per second)
total time = 101226.41 ms / 3210 tokens
srv update_slots: all slots are idle
srv log_server_r: request: POST /v1/chat/completions 127.0.0.1 200