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ggml_cuda_init: found 1 CUDA devices (Total VRAM: 6143 MiB):
Device 0: NVIDIA GeForce RTX 3060 Laptop GPU, compute capability 8.6, VMM: yes, VRAM: 6143 MiB
main: n_parallel is set to auto, using n_parallel = 4 and kv_unified = true
build: 8522 (9c600bcd4) with GNU 13.3.0 for Linux x86_64
system info: n_threads = 8, n_threads_batch = 8, total_threads = 16
system_info: n_threads = 8 (n_threads_batch = 8) / 16 | CUDA : ARCHS = 860 | 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 | REPACK = 1 |
init: using 15 threads for HTTP server
start: binding port with default address family
main: loading model
srv load_model: loading model '/home/quansloth/quansloth-app/models/Llama-3.2-1B-Instruct-Q8_0.gguf'
common_init_result: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on
llama_params_fit_impl: projected to use 1614 MiB of device memory vs. 5122 MiB of free device memory
llama_params_fit_impl: will leave 3507 >= 1024 MiB of free device memory, no changes needed
llama_params_fit: successfully fit params to free device memory
llama_params_fit: fitting params to free memory took 0.47 seconds
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 3060 Laptop GPU) (0000:01:00.0) - 5122 MiB free
llama_model_loader: loaded meta data with 30 key-value pairs and 147 tensors from /home/quansloth/quansloth-app/models/Llama-3.2-1B-Instruct-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 = llama
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Llama 3.2 1B Instruct
llama_model_loader: - kv 3: general.organization str = Meta Llama
llama_model_loader: - kv 4: general.finetune str = Instruct
llama_model_loader: - kv 5: general.basename str = Llama-3.2
llama_model_loader: - kv 6: general.size_label str = 1B
llama_model_loader: - kv 7: llama.block_count u32 = 16
llama_model_loader: - kv 8: llama.context_length u32 = 131072
llama_model_loader: - kv 9: llama.embedding_length u32 = 2048
llama_model_loader: - kv 10: llama.feed_forward_length u32 = 8192
llama_model_loader: - kv 11: llama.attention.head_count u32 = 32
llama_model_loader: - kv 12: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 13: llama.rope.freq_base f32 = 500000.000000
llama_model_loader: - kv 14: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 15: llama.attention.key_length u32 = 64
llama_model_loader: - kv 16: llama.attention.value_length u32 = 64
llama_model_loader: - kv 17: general.file_type u32 = 7
llama_model_loader: - kv 18: llama.vocab_size u32 = 128256
llama_model_loader: - kv 19: llama.rope.dimension_count u32 = 64
llama_model_loader: - kv 20: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 21: tokenizer.ggml.pre str = llama-bpe
llama_model_loader: - kv 22: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 23: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 24: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv 25: tokenizer.ggml.bos_token_id u32 = 128000
llama_model_loader: - kv 26: tokenizer.ggml.eos_token_id u32 = 128009
llama_model_loader: - kv 27: tokenizer.ggml.padding_token_id u32 = 128004
llama_model_loader: - kv 28: tokenizer.chat_template str = {{- bos_token }}\n{%- if custom_tools ...
llama_model_loader: - kv 29: general.quantization_version u32 = 2
llama_model_loader: - type f32: 34 tensors
llama_model_loader: - type q8_0: 113 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q8_0
print_info: file size = 1.22 GiB (8.50 BPW)
load: 0 unused tokens
load: printing all EOG tokens:
load: - 128001 ('<|end_of_text|>')
load: - 128008 ('<|eom_id|>')
load: - 128009 ('<|eot_id|>')
load: special tokens cache size = 256
load: token to piece cache size = 0.7999 MB
print_info: arch = llama
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 131072
print_info: n_embd = 2048
print_info: n_embd_inp = 2048
print_info: n_layer = 16
print_info: n_head = 32
print_info: n_head_kv = 8
print_info: n_rot = 64
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 64
print_info: n_embd_head_v = 64
print_info: n_gqa = 4
print_info: n_embd_k_gqa = 512
print_info: n_embd_v_gqa = 512
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-05
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 = 0.0e+00
print_info: n_ff = 8192
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: n_expert_groups = 0
print_info: n_group_used = 0
print_info: causal attn = 1
print_info: pooling type = 0
print_info: rope type = 0
print_info: rope scaling = linear
print_info: freq_base_train = 500000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 131072
print_info: rope_yarn_log_mul = 0.0000
print_info: rope_finetuned = unknown
print_info: model type = 1B
print_info: model params = 1.24 B
print_info: general.name = Llama 3.2 1B Instruct
print_info: vocab type = BPE
print_info: n_vocab = 128256
print_info: n_merges = 280147
print_info: BOS token = 128000 '<|begin_of_text|>'
print_info: EOS token = 128009 '<|eot_id|>'
print_info: EOT token = 128009 '<|eot_id|>'
print_info: EOM token = 128008 '<|eom_id|>'
print_info: PAD token = 128004 '<|finetune_right_pad_id|>'
print_info: LF token = 198 'Ċ'
print_info: EOG token = 128001 '<|end_of_text|>'
print_info: EOG token = 128008 '<|eom_id|>'
print_info: EOG token = 128009 '<|eot_id|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = true, direct_io = false)
load_tensors: offloading output layer to GPU
load_tensors: offloading 15 repeating layers to GPU
load_tensors: offloaded 17/17 layers to GPU
load_tensors: CPU_Mapped model buffer size = 266.16 MiB
load_tensors: CUDA0 model buffer size = 1252.41 MiB
.............................................................
common_init_result: added <|end_of_text|> logit bias = -inf
common_init_result: added <|eom_id|> logit bias = -inf
common_init_result: added <|eot_id|> logit bias = -inf
llama_context: constructing llama_context
llama_context: n_seq_max = 4
llama_context: n_ctx = 8192
llama_context: n_ctx_seq = 8192
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = enabled
llama_context: kv_unified = true
llama_context: freq_base = 500000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (8192) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 1.96 MiB
llama_kv_cache: CUDA0 KV buffer size = 104.00 MiB
llama_kv_cache: size = 104.00 MiB ( 8192 cells, 16 layers, 4/1 seqs), K (q8_0): 68.00 MiB, V (q4_0): 36.00 MiB
sched_reserve: reserving ...
sched_reserve: resolving fused Gated Delta Net support:
sched_reserve: fused Gated Delta Net (autoregressive) enabled
sched_reserve: fused Gated Delta Net (chunked) enabled
sched_reserve: CUDA0 compute buffer size = 258.50 MiB
sched_reserve: CUDA_Host compute buffer size = 28.01 MiB
sched_reserve: graph nodes = 503
sched_reserve: graph splits = 34
sched_reserve: reserve took 24.72 ms, sched copies = 1
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
srv load_model: initializing slots, n_slots = 4
no implementations specified for speculative decoding
slot load_model: id 0 | task -1 | speculative decoding context not initialized
slot load_model: id 0 | task -1 | new slot, n_ctx = 8192
no implementations specified for speculative decoding
slot load_model: id 1 | task -1 | speculative decoding context not initialized
slot load_model: id 1 | task -1 | new slot, n_ctx = 8192
no implementations specified for speculative decoding
slot load_model: id 2 | task -1 | speculative decoding context not initialized
slot load_model: id 2 | task -1 | new slot, n_ctx = 8192
no implementations specified for speculative decoding
slot load_model: id 3 | task -1 | speculative decoding context not initialized
slot load_model: id 3 | task -1 | new slot, n_ctx = 8192
srv load_model: prompt cache is enabled, size limit: 8192 MiB
srv load_model: use `--cache-ram 0` to disable the prompt cache
srv load_model: for more info see https://github.com/ggml-org/llama.cpp/pull/16391
init: chat template, example_format: '<|start_header_id|>system<|end_header_id|>
Cutting Knowledge Date: December 2023
Today Date: 31 Mar 2026
You are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>
Hello<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Hi there<|eot_id|><|start_header_id|>user<|end_header_id|>
How are you?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
'
srv init: init: chat template, thinking = 0
main: model loaded
main: server is listening on http://127.0.0.1:8080
main: starting the main loop...
srv update_slots: all slots are idle
srv params_from_: Chat format: peg-native
slot get_availabl: id 3 | task -1 | selected slot by LRU, t_last = -1
slot launch_slot_: id 3 | task -1 | sampler chain: logits -> ?penalties -> ?dry -> ?top-n-sigma -> top-k -> ?typical -> top-p -> min-p -> ?xtc -> temp-ext -> dist
slot launch_slot_: id 3 | task 0 | processing task, is_child = 0
slot update_slots: id 3 | task 0 | new prompt, n_ctx_slot = 8192, n_keep = 0, task.n_tokens = 5057
slot update_slots: id 3 | task 0 | n_tokens = 0, memory_seq_rm [0, end)
slot update_slots: id 3 | task 0 | prompt processing progress, n_tokens = 2048, batch.n_tokens = 2048, progress = 0.404983
slot update_slots: id 3 | task 0 | n_tokens = 2048, memory_seq_rm [2048, end)
slot update_slots: id 3 | task 0 | prompt processing progress, n_tokens = 4096, batch.n_tokens = 2048, progress = 0.809966
slot update_slots: id 3 | task 0 | n_tokens = 4096, memory_seq_rm [4096, end)
slot init_sampler: id 3 | task 0 | init sampler, took 0.68 ms, tokens: text = 5057, total = 5057
slot update_slots: id 3 | task 0 | prompt processing done, n_tokens = 5057, batch.n_tokens = 961
srv log_server_r: done request: POST /v1/chat/completions 127.0.0.1 200
slot print_timing: id 3 | task 0 |
prompt eval time = 65233.58 ms / 5057 tokens ( 12.90 ms per token, 77.52 tokens per second)
eval time = 42026.84 ms / 715 tokens ( 58.78 ms per token, 17.01 tokens per second)
total time = 107260.42 ms / 5772 tokens
slot release: id 3 | task 0 | stop processing: n_tokens = 5771, truncated = 0
srv update_slots: all slots are idle
srv operator(): operator(): cleaning up before exit...
llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
llama_memory_breakdown_print: | - CUDA0 (RTX 3060 Laptop GPU) | 6143 = 3482 + (1614 = 1252 + 104 + 258) + 1046 |
llama_memory_breakdown_print: | - Host | 294 = 266 + 0 + 28 |
Received second interrupt, terminating immediately.