***
Welcome to KoboldCpp - Version 1.124.2
Loading Chat Completions Adapter: C:\Users\<user>\AppData\Local\Temp\_MEI33682\kcpp_adapters\AutoGuess.json
Chat Completions Adapter Loaded
No GPU or CPU backend was selected. Trying to assign one for you automatically...
Unable to detect VRAM.
Auto Selected Default Backend (flag=0)

Unable to detect VRAM.
No GPU backend found, or could not automatically determine GPU layers. You may prefer to set layers manually.
System: Windows 10.0.20348 AMD64 AMD64 Family 25 Model 17 Stepping 1, AuthenticAMD
Unable to determine GPU Memory
Detected Available RAM: 13751 MB
Initializing dynamic library: koboldcpp_default.dll
Loaded shared common library: koboldcpp_common.dll
==========
Namespace(admin=False, admindir='', adminpassword=<redacted> adminunloadtimeout=0, agent=False, agent_api_key=<redacted> agent_base_url=None, agentload='', allow_config_onready=False, analyze='', autofit=False, autofitpadding=1024, autoswapmode=False, autoswapthreshold=256, baseconfig='', batchsize=512, benchmark=None, blasthreads=0, chatcompletionsadapter='AutoGuess', cli=False, config=None, contextsize=4096, debugmode=0, defaultgenamt=2048, device='', downloaddir='', draftamount=4, draftgpulayers=999, draftgpusplit=None, draftmodel='', embeddingsgpu=False, embeddingsmaxctx=4096, embeddingsmodel='models\\all-MiniLM-L6-v2-Q8_0.gguf', enableguidance=False, exportconfig='', exporttemplate='', failsafe=False, ffncpu=0, flashattention=False, forceversion=False, foreground=False, gendefaults='', gendefaultsoverwrite=False, genlimit=0, gpulayers=0, highpriority=False, hordeconfig=None, hordegenlen=0, hordekey='', hordemaxctx=0, hordemodelname='', hordeworkername='', host='', ignoremissing=False, jinja=False, jinja_kwargs='', jinjatemplate='', jinjathink='default', launch=False, lazy_mode='auto', lora=None, loramult=1.0, lowvram=False, maingpu=-1, maxrequestsize=32, mcpfile='', mmproj='models\\mmproj-SmolVLM-256M-Instruct-Q8_0.gguf', mmprojcpu=False, model=['models\\SmolVLM-256M-Instruct-Q8_0.gguf'], model_param='models\\SmolVLM-256M-Instruct-Q8_0.gguf', moe_cache_mib=0, moecpu=0, moeexperts=-1, multiplayer=False, multiuser=None, musicdiffusion='', musicembeddings='', musicllm='', musiclowvram=False, musicvae='', noavx2=False, noblas=False, nobostoken=<redacted> nocertify=False, nofastforward=False, noflashattention=False, nommap=False, nommq=False, nomodel=False, nopipelineparallel=False, noshift=False, noswa=False, onready='', overridekv='', overridenativecontext=0, overridetensors='', parallelrequests=1, paralleltotalcontext=0, password=<redacted> pipelineparallel=False, port=63122, port_param=63122, preloadstory='', prompt='', proxy_port=None, quantkv='f16', queuelimit=10, quiet=False, ratelimit=0, reasoningeffort='default', remotetunnel=False, reqtimeout=600, ropeconfig=[0.0, 10000.0], routermode=False, rpccache='', rpcdevice='', rpchost='0.0.0.0', rpcmode='disabled', rpcport=5551, rpctargets='', run_bundled_agent=False, savedatafile='', sdaudiovae='', sdclamped=0, sdclampedsoft=0, sdclip1='', sdclip2='', sdclipdevice=-2, sdclipgpu=False, sdconfig=None, sdconvdirect='off', sdflashattention=False, sdgendefaults=False, sdllm='', sdlora=[], sdloramult=[1.0], sdmaingpu=None, sdmodel='', sdnotile=False, sdoffloadcpu=False, sdphotomaker='', sdquant=0, sdt5xxl=None, sdthreads=0, sdtiledvae=512, sdupscaler='', sdvae='', sdvaeauto=False, sdvaecpu=False, sdvaedevice=-1, sdvramlimit=0, showgui=False, singleinstance=False, skiplauncher=True, smartcache=0, smartcontext=False, splitmode='layer', ssl=None, swapadding=0, tensor_split=None, testmemory=False, threads=2, ttsdir='', ttsgpu=False, ttsmaxlen=4096, ttsmodel='', ttsthreads=0, ttswavtokenizer='', ubatchsize=-1, unpack='', usecpu=False, usecuda=None, usedirectio=False, usemlock=False, usemmap=False, usemtp=False, useswa=False, usevulkan=None, version=False, visionmaxres=1024, visionmaxtokens=-1, visionmintokens=-1, websearch=False, whispermodel='models\\ggml-tiny.en.bin')
==========
Loading Text Model: D:\a\test-KoboldCpp\test-KoboldCpp\models\SmolVLM-256M-Instruct-Q8_0.gguf
llama_model_loader: loaded meta data with 49 key-value pairs and 273 tensors from D:\a\test-KoboldCpp\test-KoboldCpp\models\SmolVLM-256M-Instruct-Q8_0.gguf (version GGUF V3 (latest))
print_info: file format = GGUF V3 (latest)
print_info: file type   = all F32
print_info: file size   = 165.24 MiB (8.51 BPW) 
init_tokenizer: initializing tokenizer for type 2
load: 0 unused tokens
load: printing all EOG tokens:
load:   - 0 ('<|endoftext|>')
load:   - 2 ('<|im_end|>')
load:   - 4 ('<reponame>')
load:   - 49279 ('<end_of_utterance>')
load: special tokens cache size = 145
load: token to piece cache size = 0.3199 MB
print_info: arch                  = llama
print_info: vocab_only            = 0
print_info: no_alloc              = 0
print_info: n_ctx_train           = 8192
print_info: n_embd_inp            = 576
print_info: n_embd                = 576
print_info: n_embd_out            = 576
print_info: n_layer               = 30
print_info: n_layer_all           = 30
print_info: n_head                = 9
print_info: n_head_kv             = 3
print_info: n_rot                 = 64
print_info: n_swa                 = 0
print_info: is_swa_any            = 0
print_info: non_causal_type       = 0
print_info: n_embd_head_k         = 64
print_info: n_embd_head_v         = 64
print_info: n_gqa                 = 3
print_info: n_embd_k_gqa          = 192
print_info: n_embd_v_gqa          = 192
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: f_attn_value_scale    = 0.0000
print_info: n_ff                  = 1536
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          = -1
print_info: rope type             = 0
print_info: rope scaling          = linear
print_info: freq_base_train       = 100000.0
print_info: freq_scale_train      = 1
print_info: n_ctx_orig_yarn       = 8192
print_info: rope_yarn_log_mul     = 0.0000
print_info: rope_finetuned        = unknown
print_info: model type            = 256M
print_info: model params          = 162.97 M
print_info: general.name          = SmolVLM 256M Instruct
print_info: vocab type            = BPE
print_info: n_vocab               = 49280
print_info: n_merges              = 48900
print_info: BOS token             = <redacted> '<|im_start|>'
print_info: EOS token             = <redacted> '<end_of_utterance>'
print_info: EOT token             = <redacted> '<end_of_utterance>'
print_info: UNK token             = <redacted> '<|endoftext|>'
print_info: PAD token             = <redacted> '<|im_end|>'
print_info: LF token              = <redacted> 'Ċ'
print_info: FIM REP token         = <redacted> '<reponame>'
print_info: EOG token             = <redacted> '<|endoftext|>'
print_info: EOG token             = <redacted> '<|im_end|>'
print_info: EOG token             = <redacted> '<reponame>'
print_info: EOG token             = <redacted> '<end_of_utterance>'
print_info: max token length      = 162
load_tensors: loading model tensors, this can take a while... (load_mode = none)
done_getting_tensors: tensor 'token_embd.weight' (q8_0) (and 272 others) moved from CPU_REPACK, using CPU instead
load_tensors: offloading 0 repeating layers to GPU
load_tensors: offloaded 0/31 layers to GPU
load_tensors:          CPU model buffer size =   165.24 MiB
....................................................................
llama_context: constructing llama_context
llama_context: n_seq_max             = 1
llama_context: n_ctx                 = 4352
llama_context: n_ctx_seq             = 4352
llama_context: n_batch               = 512
llama_context: n_ubatch              = 512
llama_context: causal_attn           = 1
llama_context: flash_attn            = enabled
llama_context: kv_unified            = true
llama_context: freq_base             = 100000.0
llama_context: freq_scale            = 1
llama_context: n_rs_seq              = 0
llama_context: n_outputs_max         = 512
llama_context: n_outputs_max_per_seq = 1
llama_context: n_ctx_seq (4352) < n_ctx_train (8192) -- the full capacity of the model will not be utilized
set_abort_callback: call
llama_context:        CPU  output buffer size =     0.19 MiB
llama_kv_cache:        CPU KV buffer size =    95.62 MiB
llama_kv_cache: size =   95.62 MiB (  4352 cells,  30 layers,  1/1 seqs), K (f16):   47.81 MiB, V (f16):   47.81 MiB
llama_kv_cache: n_rot_k = 0, n_embd_head_k_all = 64
llama_kv_cache: n_rot_v = 0, n_embd_head_k_all = 64
llama_context: enumerating backends
llama_context: backend_ptrs.size() = 1
sched_reserve: reserving ...
sched_reserve: max_nodes = 2184
resolve_fused_ops: resolving fused DeepSeek V4 HC support:
resolve_fused_ops: fused DeepSeek V4 HC pre enabled
resolve_fused_ops: fused DeepSeek V4 HC comb enabled
resolve_fused_ops: fused DeepSeek V4 HC post enabled
sched_reserve:        CPU compute buffer size =   107.27 MiB
sched_reserve: graph: nodes = 939, splits = 1, input objects = 4, input tensors = 9
sched_reserve: reserve took 2.99 ms, sched copies = 1
attach_threadpool: call
clip_model_loader: model name:   SmolVLM 256M Instruct
clip_model_loader: description:  
clip_model_loader: GGUF version: 3
clip_model_loader: alignment:    32
clip_model_loader: n_tensors:    198
clip_model_loader: n_kv:         40

clip_model_loader: has vision encoder
clip_ctx: CLIP using CPU backend
load_hparams: projector:          idefics3
load_hparams: n_embd:             768
load_hparams: n_head:             12
load_hparams: n_ff:               3072
load_hparams: n_layer:            12
load_hparams: ffn_op:             gelu
load_hparams: projection_dim:     576

--- vision hparams ---
load_hparams: image_size:         512
load_hparams: patch_size:         16
load_hparams: has_llava_proj:     0
load_hparams: minicpmv_version:   0
load_hparams: n_merge:            4
load_hparams: n_wa_pattern: 0

load_hparams: model size:         98.95 MiB
load_hparams: metadata size:      0.09 MiB
load_tensors: ffn up/down are swapped
load_tensors: loaded 198 tensors from D:\a\test-KoboldCpp\test-KoboldCpp\models\mmproj-SmolVLM-256M-Instruct-Q8_0.gguf
get_dummy_batch: warmup with image size = 512 x 512
llama_model_loader: loaded meta data with 24 key-value pairs and 101 tensors from D:\a\test-KoboldCpp\test-KoboldCpp\models\all-MiniLM-L6-v2-Q8_0.gguf (version GGUF V3 (latest))
print_info: file format = GGUF V3 (latest)
print_info: file type   = unknown, may not work
print_info: file size   = 23.13 MiB (8.60 BPW) 
init_tokenizer: initializing tokenizer for type 3
load: 0 unused tokens
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: printing all EOG tokens:
load:   - 0 ('[PAD]')
load:   - 102 ('[SEP]')
load: special tokens cache size = 5
load: token to piece cache size = 0.2032 MB
print_info: arch                  = bert
print_info: vocab_only            = 0
print_info: no_alloc              = 0
print_info: n_ctx_train           = 512
print_info: n_embd_inp            = 384
print_info: n_embd                = 384
print_info: n_embd_out            = 384
print_info: n_layer               = 6
print_info: n_layer_all           = 6
print_info: n_head                = 12
print_info: n_head_kv             = 12
print_info: n_rot                 = 32
print_info: n_swa                 = 0
print_info: is_swa_any            = 0
print_info: non_causal_type       = 0
print_info: n_embd_head_k         = 32
print_info: n_embd_head_v         = 32
print_info: n_gqa                 = 1
print_info: n_embd_k_gqa          = 384
print_info: n_embd_v_gqa          = 384
print_info: f_norm_eps            = 1.0e-12
print_info: f_norm_rms_eps        = 0.0e+00
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: f_attn_value_scale    = 0.0000
print_info: n_ff                  = 1536
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           = 0
print_info: pooling type          = 1
print_info: rope type             = 2
print_info: rope scaling          = linear
print_info: freq_base_train       = 10000.0
print_info: freq_scale_train      = 1
print_info: n_ctx_orig_yarn       = 512
print_info: rope_yarn_log_mul     = 0.0000
print_info: rope_finetuned        = unknown
print_info: model type            = 22M
print_info: model params          = 22.57 M
print_info: general.name          = all-MiniLM-L6-v2
print_info: vocab type            = WPM
print_info: n_vocab               = 30522
print_info: n_merges              = 0
print_info: BOS token             = <redacted> '[CLS]'
print_info: EOS token             = <redacted> '[SEP]'
print_info: UNK token             = <redacted> '[UNK]'
print_info: SEP token             = <redacted> '[SEP]'
print_info: PAD token             = <redacted> '[PAD]'
print_info: MASK token            = <redacted> '[MASK]'
print_info: LF token              = <redacted> '[PAD]'
print_info: FIM PAD token         = <redacted> '[PAD]'
print_info: EOG token             = <redacted> '[PAD]'
print_info: EOG token             = <redacted> '[SEP]'
print_info: max token length      = 21
load_tensors: loading model tensors, this can take a while... (load_mode = none)
done_getting_tensors: tensor 'token_embd.weight' (q8_0) (and 100 others) moved from CPU_REPACK, using CPU instead
load_tensors: offloading 0 repeating layers to GPU
load_tensors: offloaded 0/7 layers to GPU
load_tensors:          CPU model buffer size =    23.13 MiB
...............................
llama_context: constructing llama_context
llama_context: n_seq_max             = 1
llama_context: n_ctx                 = 512
llama_context: n_ctx_seq             = 512
llama_context: n_batch               = 512
llama_context: n_ubatch              = 512
llama_context: causal_attn           = 0
llama_context: flash_attn            = enabled
llama_context: kv_unified            = true
llama_context: freq_base             = 10000.0
llama_context: freq_scale            = 1
llama_context: n_rs_seq              = 0
llama_context: n_outputs_max         = 512
llama_context: n_outputs_max_per_seq = 1
set_abort_callback: call
llama_context:        CPU  output buffer size =     0.12 MiB
llama_context: enumerating backends
llama_context: backend_ptrs.size() = 1
sched_reserve: reserving ...
sched_reserve: max_nodes = 1024
init: embeddings required but some input tokens were not marked as outputs -> overriding
Load Text Model OK: True
Chat template heuristics failed to identify chat completions format. Jinja will be used.
Load Audio Transcription Model OK: True
Load Embeddings Model OK: True
Embedded KoboldAI Lite loaded.
Embedded API docs loaded.
Llama.cpp UI loaded.
Embedded MusicUI loaded.
======
Active Modules: TextGeneration VoiceRecognition MultimodalVision VectorEmbeddings
Inactive Modules: ImageGeneration MultimodalAudio NetworkMultiplayer ApiKeyPassword WebSearchProxy TextToSpeech AdminControl MCPBridge MusicGen RouterMode
Enabled APIs: KoboldCppApi OpenAiApi OllamaApi AnthropicApi WhisperTranscribeApi
Note: For third party Ollama API Emulation, you should set the port to 11434.
Starting Kobold API on port 63122 at http://localhost:63122/api/
Starting OpenAI Compatible API on port 63122 at http://localhost:63122/v1/
Starting llama.cpp secondary WebUI at http://localhost:63122/lcpp/
MusicUI is available at http://localhost:63122/musicui/
======
Please connect to custom endpoint at http://localhost:63122

Input: {"messages": [{"role": "user", "content": [{"type": "text", "text": "Describe this image: list the shapes, their colours and any text."}, {"type": "image_url", "image_url": {"url": "data:image/png;base64,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...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"}}]}], "max_tokens": 80, "temperature": 0}mtmd_group_mergeable_bitmaps: no merging for part index 0
add_media: preproc_out has 1 entries, grid_x = 1, grid_y = 1, has_overview = 1
add_media: adding 1 slices (1 rows x 1 cols)
add_text: <fake_token_around_image><row_1_col_1>
add_media: adding slice image at row 0 col 0

The reported GGUF Arch is: llama
Arch Category: 1

---
Identified as GGUF model.
Attempting to Load...
---
Using automatic RoPE scaling for GGUF. If the model has custom RoPE settings, they'll be used directly instead!
System Info: AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | AMX_INT8 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | RISCV_VECT = 0 | WASM_SIMD = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | 
Number of Backends: 2
Backend 0: RPC
Backend 1: CPU

This model does not use SWA
Automatic RoPE Scaling: Using model internal value.
Threadpool set to 2 threads and 2 blasthreads...

Attempting to apply Multimodal Projector: D:\a\test-KoboldCpp\test-KoboldCpp\models\mmproj-SmolVLM-256M-Instruct-Q8_0.gguf
Starting model warm up, please wait a moment...

Loading Whisper Model: D:\a\test-KoboldCpp\test-KoboldCpp\models\ggml-tiny.en.bin
Whisper Load Complete.

Loading Embeddings Model: D:\a\test-KoboldCpp\test-KoboldCpp\models\all-MiniLM-L6-v2-Q8_0.gguf 

Embeddings Model Load Complete.


Processing Prompt (0 / 159 tokens)
Processing Prompt (18 / 159 tokens)encoding image slice...
reserve_compute_meta:        CPU compute buffer size =    21.00 MiB
reserve_compute_meta: graph splits = 1, nodes = 396
warmup: flash attention is enabled
clip_encode: copying image 1/1 to input buffer (nx=512, ny=512)
clip_encode: output embedding shape [576, 64, 1]
image slice encoded in 1691 ms
decoding image batch 1/1, n_tokens_batch = 64
image decoded (batch 1/1) in 103 ms
encoding image slice...
clip_encode: copying image 1/1 to input buffer (nx=512, ny=512)
clip_encode: output embedding shape [576, 64, 1]
image slice encoded in 1678 ms
decoding image batch 1/1, n_tokens_batch = 64
image decoded (batch 1/1) in 135 ms

Processing Media Embedding 1 (2 tokens)
Processing Media Embedding 1 (64 tokens)
Processing Media Embedding 1 (3 tokens)
Processing Media Embedding 1 (64 tokens)
Processing Media Embedding 1 (1 tokens)
Processing Media End Separator (1 tokens)
Processing Prompt (159 / 159 tokens)

Generating (1 / 80 tokens)
Generating (2 / 80 tokens)
Generating (3 / 80 tokens)
Generating (4 / 80 tokens)
Generating (5 / 80 tokens)
Generating (6 / 80 tokens)
Generating (7 / 80 tokens)
Generating (8 / 80 tokens)
Generating (9 / 80 tokens)
(EOS token triggered! ID:49279)
[13:13:58] CtxLimit:168/4096, Init:0.01s, Processed:159 in 3.70s (42.94T/s), Generated:9/80 in 0.08s (118.42T/s), Total:3.79s

Output:  \red\quad\blue\quad
Detected uploaded file: ('jfk.wav', '')

Input: {"audio_data": 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