Not sure if debug log is relevant here, but I added it either way. This is the debug for creating the custom model (as a child of Dolphin), and attempting to use it.
INFO [ollama_instances.py | start] Ollama version is 0.30.10
time=2026-06-23T12:41:59.273+01:00 level=INFO source=model_recommendations.go:177 msg="model recommendations cache sleep scheduled" wait=4h14m32.477735563s consecutive_failures=0
time=2026-06-23T12:42:00.198+01:00 level=INFO source=types.go:32 msg="inference compute" id=0 filter_id=0 library=Vulkan compute=0.0 name=Vulkan0 description="Quadro M4000" libdirs=ollama,vulkan driver=0.0 pci_id=0000:01:00.0 type=discrete total="8.2 GiB" available="6.8 GiB"
time=2026-06-23T12:42:00.198+01:00 level=INFO source=routes.go:2031 msg="vram-based default context" total_vram="8.2 GiB" default_num_ctx=4096
[GIN] 2026/06/23 - 12:42:00 | 200 | 1.594766ms | 127.0.0.1 | GET "/api/tags"
INFO [_client.py | _send_single_request] HTTP Request: GET http://127.0.0.1:11435/api/tags "HTTP/1.1 200 OK"
INFO [_client.py | _send_single_request] HTTP Request: POST http://127.0.0.1:11435/api/show "HTTP/1.1 200 OK"
[GIN] 2026/06/23 - 12:42:00 | 200 | 479.586208ms | 127.0.0.1 | POST "/api/show"
INFO [_client.py | _send_single_request] HTTP Request: POST http://127.0.0.1:11435/api/show "HTTP/1.1 200 OK"
[GIN] 2026/06/23 - 12:42:06 | 200 | 382.859559ms | 127.0.0.1 | POST "/api/show"
/app/share/Alpaca/alpaca/widgets/models/creator.py:86: Warning: g_value_get_int: assertion 'G_VALUE_HOLDS_INT (value)' failed
self.name_el.set_text(prettify_model_name(model.get_name(), True)[0])
/usr/lib/python3.13/site-packages/gi/overrides/Gio.py:138: Warning: g_value_get_int: assertion 'G_VALUE_HOLDS_INT (value)' failed
return Gio.Application.run(self, *args, **kwargs)
[GIN] 2026/06/23 - 12:42:32 | 404 | 1.724522ms | 127.0.0.1 | POST "/api/show"
INFO [_client.py | _send_single_request] HTTP Request: POST http://127.0.0.1:11435/api/show "HTTP/1.1 404 Not Found"
ERROR [ollama_instances.py | get_model_info] model 'pancake-bot:custom' not found (status code: 404)
INFO [_client.py | _send_single_request] HTTP Request: POST http://127.0.0.1:11435/api/create "HTTP/1.1 200 OK"
[GIN] 2026/06/23 - 12:42:32 | 200 | 84.460643ms | 127.0.0.1 | POST "/api/create"
INFO [_client.py | _send_single_request] HTTP Request: POST http://127.0.0.1:11435/api/show "HTTP/1.1 200 OK"
[GIN] 2026/06/23 - 12:42:32 | 200 | 328.2247ms | 127.0.0.1 | POST "/api/show"
[GIN] 2026/06/23 - 12:43:00 | 200 | 1.048644ms | 127.0.0.1 | GET "/api/tags"
INFO [_client.py | _send_single_request] HTTP Request: GET http://127.0.0.1:11435/api/tags "HTTP/1.1 200 OK"
[GIN] 2026/06/23 - 12:43:00 | 200 | 5.491637ms | 127.0.0.1 | POST "/api/show"
INFO [_client.py | _send_single_request] HTTP Request: POST http://127.0.0.1:11435/api/show "HTTP/1.1 200 OK"
time=2026-06-23T12:43:01.938+01:00 level=INFO source=server.go:109 msg="using llama-server for model" model=/home/theuser/.var/app/com.jeffser.Alpaca/data/.ollama/models/blobs/sha256-1eee6953530837b2b17d61a4e6f71a5aa31c9714cfcf3cb141aa5c1972b5116b
time=2026-06-23T12:43:01.938+01:00 level=INFO source=llama_server.go:406 msg="starting llama-server" cmd="/home/theuser/.var/app/com.jeffser.Alpaca/data/ollama_installation/lib/ollama/llama-server --model /home/theuser/.var/app/com.jeffser.Alpaca/data/.ollama/models/blobs/sha256-1eee6953530837b2b17d61a4e6f71a5aa31c9714cfcf3cb141aa5c1972b5116b --port 38615 --host 127.0.0.1 --no-webui --offline -c 16384 -np 1 --log-verbosity 4 --no-log-prefix --no-log-timestamps --no-jinja --chat-template chatml --flash-attn auto -b 512 -ub 512 --context-shift --keep 4"
time=2026-06-23T12:43:01.939+01:00 level=INFO source=sched.go:623 msg="system memory" total="31.3 GiB" free="21.6 GiB" free_swap="1.0 GiB"
time=2026-06-23T12:43:01.939+01:00 level=INFO source=sched.go:630 msg="gpu memory" id=0 library=Vulkan available="6.2 GiB" free="6.7 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-06-23T12:43:01.939+01:00 level=INFO source=llama_server.go:889 msg="loading model via llama-server" model=/home/theuser/.var/app/com.jeffser.Alpaca/data/.ollama/models/blobs/sha256-1eee6953530837b2b17d61a4e6f71a5aa31c9714cfcf3cb141aa5c1972b5116b
time=2026-06-23T12:43:01.939+01:00 level=INFO source=llama_server.go:1134 msg="waiting for llama-server to start responding"
time=2026-06-23T12:43:01.939+01:00 level=INFO source=llama_server.go:1189 msg="waiting for llama-server to become available" status="llm server not responding"
common_params_print_info: build 1 (74ade5274) with GNU 11.2.1 for Linux x86_64
log_info: verbosity = 4 (adjust with the `-lv N` CLI arg)
device_info:
- CPU : AMD Ryzen 5 3400G with Radeon Vega Graphics (32016 MiB, 32016 MiB free)
- Vulkan0 : Quadro M4000 (8438 MiB, 6883 MiB free)
system_info: n_threads = 4 (n_threads_batch = 4) / 8 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | REPACK = 1 |
srv init: using 7 threads for HTTP server
srv init: The UI is disabled
srv init: Use --ui/--no-ui (or deprecated --webui/--no-webui) to enable/disable
srv start: binding port with default address family
srv llama_server: loading model
srv load_model: loading model '/home/theuser/.var/app/com.jeffser.Alpaca/data/.ollama/models/blobs/sha256-1eee6953530837b2b17d61a4e6f71a5aa31c9714cfcf3cb141aa5c1972b5116b'
common_init_result: fitting params to device memory ...
common_init_result: (for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on)
common_params_fit_impl: getting device memory data for initial parameters:
time=2026-06-23T12:43:02.190+01:00 level=INFO source=llama_server.go:1189 msg="waiting for llama-server to become available" status="llm server loading model"
common_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
common_memory_breakdown_print: | - Vulkan0 (Quadro M4000) | 8438 = 6873 + (6567 = 4403 + 2048 + 116) + -5003 |
common_memory_breakdown_print: | - Host | 313 = 281 + 0 + 32 |
common_params_fit_impl: projected to use 6567 MiB of device memory vs. 6874 MiB of free device memory
common_params_fit_impl: cannot meet free memory target of 1024 MiB, need to reduce device memory by 717 MiB
common_params_fit_impl: context size set by user to 16384 -> no change
common_params_fit_impl: id=0, target=5850 MiB
common_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
common_memory_breakdown_print: | - Vulkan0 (Quadro M4000) | 8438 = 6872 + ( 160 = 0 + 0 + 160) + 1405 |
common_memory_breakdown_print: | - Host | 6765 = 4685 + 2048 + 32 |
common_params_fit_impl: memory for test allocation by device:
common_params_fit_impl: id=0, n_layer= 0, n_part= 0, overflow_type=4, mem= 160 MiB
common_params_fit_impl: filling dense layers back-to-front:
common_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
common_memory_breakdown_print: | - Vulkan0 (Quadro M4000) | 8438 = 6872 + (6567 = 4403 + 2048 + 116) + -5001 |
common_memory_breakdown_print: | - Host | 313 = 281 + 0 + 32 |
common_params_fit_impl: memory for test allocation by device:
common_params_fit_impl: id=0, n_layer=33, n_part= 0, overflow_type=4, mem= 6567 MiB
common_params_fit_impl: start filling device 0, delta=33
common_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
common_memory_breakdown_print: | - Vulkan0 (Quadro M4000) | 8438 = 6872 + (5825 = 3873 + 1792 + 160) + -4259 |
common_memory_breakdown_print: | - Host | 1099 = 811 + 256 + 32 |
common_params_fit_impl: memory for test allocation by device:
common_params_fit_impl: id=0, n_layer=29, n_part= 0, overflow_type=4, mem= 5825 MiB
common_params_fit_impl: set ngl_per_device[0].n_layer=29
common_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
common_memory_breakdown_print: | - Vulkan0 (Quadro M4000) | 8438 = 6872 + (6022 = 4005 + 1856 + 160) + -4456 |
common_memory_breakdown_print: | - Host | 903 = 679 + 192 + 32 |
common_params_fit_impl: memory for test allocation by device:
common_params_fit_impl: id=0, n_layer=30, n_part= 0, overflow_type=4, mem= 6022 MiB
common_params_fit_impl: set ngl_per_device_high[0].n_layer=30
common_params_fit_impl: - Vulkan0 (Quadro M4000): 29 layers, 5825 MiB used, 1048 MiB free
common_fit_params: successfully fit params to free device memory
common_fit_params: fitting params to free memory took 2.51 seconds
llama_model_loader: loaded meta data with 77 key-value pairs and 292 tensors from /home/theuser/.var/app/com.jeffser.Alpaca/data/.ollama/models/blobs/sha256-1eee6953530837b2b17d61a4e6f71a5aa31c9714cfcf3cb141aa5c1972b5116b (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 = Dolphin 3.0 Llama 3.1 8B
llama_model_loader: - kv 3: general.organization str = Cognitivecomputations
llama_model_loader: - kv 4: general.basename str = Dolphin-3.0-Llama-3.1
llama_model_loader: - kv 5: general.size_label str = 8B
llama_model_loader: - kv 6: general.license str = llama3.1
llama_model_loader: - kv 7: general.base_model.count u32 = 1
llama_model_loader: - kv 8: general.base_model.0.name str = Llama 3.1 8B
llama_model_loader: - kv 9: general.base_model.0.organization str = Meta Llama
llama_model_loader: - kv 10: general.base_model.0.repo_url str = https://huggingface.co/meta-llama/Lla...
llama_model_loader: - kv 11: general.dataset.count u32 = 13
llama_model_loader: - kv 12: general.dataset.0.name str = Opc Sft Stage1
llama_model_loader: - kv 13: general.dataset.0.organization str = OpenCoder LLM
llama_model_loader: - kv 14: general.dataset.0.repo_url str = https://huggingface.co/OpenCoder-LLM/...
llama_model_loader: - kv 15: general.dataset.1.name str = Opc Sft Stage2
llama_model_loader: - kv 16: general.dataset.1.organization str = OpenCoder LLM
llama_model_loader: - kv 17: general.dataset.1.repo_url str = https://huggingface.co/OpenCoder-LLM/...
llama_model_loader: - kv 18: general.dataset.2.name str = Orca Agentinstruct 1M v1
llama_model_loader: - kv 19: general.dataset.2.version str = v1
llama_model_loader: - kv 20: general.dataset.2.organization str = Microsoft
llama_model_loader: - kv 21: general.dataset.2.repo_url str = https://huggingface.co/microsoft/orca...
llama_model_loader: - kv 22: general.dataset.3.name str = Orca Math Word Problems 200k
llama_model_loader: - kv 23: general.dataset.3.organization str = Microsoft
llama_model_loader: - kv 24: general.dataset.3.repo_url str = https://huggingface.co/microsoft/orca...
llama_model_loader: - kv 25: general.dataset.4.name str = Hermes Function Calling v1
llama_model_loader: - kv 26: general.dataset.4.version str = v1
llama_model_loader: - kv 27: general.dataset.4.organization str = NousResearch
llama_model_loader: - kv 28: general.dataset.4.repo_url str = https://huggingface.co/NousResearch/h...
llama_model_loader: - kv 29: general.dataset.5.name str = NuminaMath CoT
llama_model_loader: - kv 30: general.dataset.5.organization str = AI MO
llama_model_loader: - kv 31: general.dataset.5.repo_url str = https://huggingface.co/AI-MO/NuminaMa...
llama_model_loader: - kv 32: general.dataset.6.name str = NuminaMath TIR
llama_model_loader: - kv 33: general.dataset.6.organization str = AI MO
llama_model_loader: - kv 34: general.dataset.6.repo_url str = https://huggingface.co/AI-MO/NuminaMa...
llama_model_loader: - kv 35: general.dataset.7.name str = Tulu 3 Sft Mixture
llama_model_loader: - kv 36: general.dataset.7.organization str = Allenai
llama_model_loader: - kv 37: general.dataset.7.repo_url str = https://huggingface.co/allenai/tulu-3...
llama_model_loader: - kv 38: general.dataset.8.name str = Dolphin Coder
llama_model_loader: - kv 39: general.dataset.8.organization str = Cognitivecomputations
llama_model_loader: - kv 40: general.dataset.8.repo_url str = https://huggingface.co/cognitivecompu...
llama_model_loader: - kv 41: general.dataset.9.name str = Smoltalk
llama_model_loader: - kv 42: general.dataset.9.organization str = HuggingFaceTB
llama_model_loader: - kv 43: general.dataset.9.repo_url str = https://huggingface.co/HuggingFaceTB/...
llama_model_loader: - kv 44: general.dataset.10.name str = Samantha Data
llama_model_loader: - kv 45: general.dataset.10.organization str = Cognitivecomputations
llama_model_loader: - kv 46: general.dataset.10.repo_url str = https://huggingface.co/cognitivecompu...
llama_model_loader: - kv 47: general.dataset.11.name str = CodeFeedback Filtered Instruction
llama_model_loader: - kv 48: general.dataset.11.organization str = M A P
llama_model_loader: - kv 49: general.dataset.11.repo_url str = https://huggingface.co/m-a-p/CodeFeed...
llama_model_loader: - kv 50: general.dataset.12.name str = Code Feedback
llama_model_loader: - kv 51: general.dataset.12.organization str = M A P
llama_model_loader: - kv 52: general.dataset.12.repo_url str = https://huggingface.co/m-a-p/Code-Fee...
llama_model_loader: - kv 53: general.languages arr[str,1] = ["en"]
llama_model_loader: - kv 54: llama.block_count u32 = 32
llama_model_loader: - kv 55: llama.context_length u32 = 131072
llama_model_loader: - kv 56: llama.embedding_length u32 = 4096
llama_model_loader: - kv 57: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 58: llama.attention.head_count u32 = 32
llama_model_loader: - kv 59: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 60: llama.rope.freq_base f32 = 500000.000000
llama_model_loader: - kv 61: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 62: llama.attention.key_length u32 = 128
llama_model_loader: - kv 63: llama.attention.value_length u32 = 128
llama_model_loader: - kv 64: general.file_type u32 = 15
llama_model_loader: - kv 65: llama.vocab_size u32 = 128258
llama_model_loader: - kv 66: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 67: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 68: tokenizer.ggml.pre str = llama-bpe
llama_model_loader: - kv 69: tokenizer.ggml.tokens arr[str,128258] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 70: tokenizer.ggml.token_type arr[i32,128258] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 71: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv 72: tokenizer.ggml.bos_token_id u32 = 128000
llama_model_loader: - kv 73: tokenizer.ggml.eos_token_id u32 = 128256
llama_model_loader: - kv 74: tokenizer.ggml.padding_token_id u32 = 128001
llama_model_loader: - kv 75: tokenizer.chat_template str = {% if not add_generation_prompt is de...
llama_model_loader: - kv 76: general.quantization_version u32 = 2
llama_model_loader: - type f32: 66 tensors
llama_model_loader: - type q4_K: 193 tensors
llama_model_loader: - type q6_K: 33 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q4_K - Medium
print_info: file size = 4.58 GiB (4.89 BPW)
llama_prepare_model_devices: using device Vulkan0 (Quadro M4000) (0000:01:00.0) - 6872 MiB free
load: 0 unused tokens
load: printing all EOG tokens:
load: - 128001 ('<|end_of_text|>')
load: - 128008 ('<|eom_id|>')
load: - 128009 ('<|eot_id|>')
load: - 128256 ('<|im_end|>')
load: special tokens cache size = 258
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_inp = 4096
print_info: n_embd = 4096
print_info: n_embd_out = 4096
print_info: n_layer = 32
print_info: n_layer_all = 32
print_info: n_head = 32
print_info: n_head_kv = 8
print_info: n_rot = 128
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 4
print_info: n_embd_k_gqa = 1024
print_info: n_embd_v_gqa = 1024
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 = 14336
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 = 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 = 8B
print_info: model params = 8.03 B
print_info: general.name = Dolphin 3.0 Llama 3.1 8B
print_info: vocab type = BPE
print_info: n_vocab = 128258
print_info: n_merges = 280147
print_info: BOS token = 128000 '<|begin_of_text|>'
print_info: EOS token = 128256 '<|im_end|>'
print_info: EOT token = 128256 '<|im_end|>'
print_info: EOM token = 128008 '<|eom_id|>'
print_info: PAD token = 128001 '<|end_of_text|>'
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: EOG token = 128256 '<|im_end|>'
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 28 repeating layers to GPU
load_tensors: offloaded 29/33 layers to GPU
load_tensors: CPU_Mapped model buffer size = 811.82 MiB
load_tensors: Vulkan0 model buffer size = 3873.50 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
common_init_result: added <|im_end|> logit bias = -inf
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 16384
llama_context: n_ctx_seq = 16384
llama_context: n_batch = 512
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = auto
llama_context: kv_unified = false
llama_context: freq_base = 500000.0
llama_context: freq_scale = 1
llama_context: n_rs_seq = 0
llama_context: n_outputs_max = 1
llama_context: n_ctx_seq (16384) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
llama_context: Vulkan_Host output buffer size = 0.49 MiB
llama_kv_cache: CPU KV buffer size = 256.00 MiB
llama_kv_cache: Vulkan0 KV buffer size = 1792.00 MiB
llama_kv_cache: size = 2048.00 MiB ( 16384 cells, 32 layers, 1/1 seqs), K (f16): 1024.00 MiB, V (f16): 1024.00 MiB
llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 128
llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 128
sched_reserve: reserving ...
sched_reserve: Flash Attention was auto, set to enabled
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: Vulkan0 compute buffer size = 160.00 MiB
sched_reserve: Vulkan_Host compute buffer size = 32.01 MiB
sched_reserve: graph nodes = 998
sched_reserve: graph splits = 51 (with bs=512), 2 (with bs=1)
sched_reserve: reserve took 33.67 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 = 1
common_speculative_init: no implementations specified for speculative decoding
slot load_model: id 0 | task -1 | new slot, n_ctx = 16384
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
srv load_model: context checkpoints enabled, max = 32, min spacing = 256
srv init: idle slots will be saved to prompt cache upon starting a new task
init: chat template, example_format: '<|im_start|>system
You are a helpful assistant<|im_end|>
<|im_start|>user
Hello<|im_end|>
<|im_start|>assistant
Hi there<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant
'
srv init: init: chat template, thinking = 0
srv llama_server: model loaded
srv llama_server: server is listening on http://127.0.0.1:38615
srv update_slots: all slots are idle
time=2026-06-23T12:43:08.473+01:00 level=INFO source=llama_server.go:1201 msg="llama-server started in 6.53 seconds"
time=2026-06-23T12:43:08.594+01:00 level=INFO source=images.go:354 msg="template selection" model=registry.ollama.ai/library/pancake-bot:custom selected=go_template renderer="" parser="" go_template=[completion] chat_template=[completion] harmony=null renderer_parser=null
time=2026-06-23T12:43:08.594+01:00 level=INFO source=sched.go:739 msg="loaded runners" count=1
time=2026-06-23T12:43:08.594+01:00 level=INFO source=llama_server.go:1134 msg="waiting for llama-server to start responding"
time=2026-06-23T12:43:08.594+01:00 level=INFO source=llama_server.go:1201 msg="llama-server started in 6.66 seconds"
slot get_availabl: id 0 | task -1 | selected slot by LRU, t_last = -1
srv get_availabl: updating prompt cache
srv load: - looking for better prompt, base f_keep = -1.000, sim = 0.000
srv update: - cache state: 0 prompts, 0.000 MiB (limits: 8192.000 MiB, 16384 tokens, 8589934592 est)
srv get_availabl: prompt cache update took 0.01 ms
slot launch_slot_: id 0 | 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 0 | task -1 | sampler params:
repeat_last_n = 64, repeat_penalty = 1.100, frequency_penalty = 0.000, presence_penalty = 0.000
dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 16384
top_k = 40, top_p = 0.900, min_p = 0.000, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 0.200
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000, adaptive_target = -1.000, adaptive_decay = 0.900
slot launch_slot_: id 0 | task 0 | processing task, is_child = 0
slot update_slots: id 0 | task 0 | new prompt, n_ctx_slot = 16384, n_keep = 4, task.n_tokens = 62
slot update_slots: id 0 | task 0 | cached n_tokens = 0, memory_seq_rm [0, end)
slot init_sampler: id 0 | task 0 | init sampler, took 0.02 ms, tokens: text = 62, total = 62
slot print_timing: id 0 | task 0 | prompt eval time = 913.37 ms / 62 tokens ( 14.73 ms per token, 67.88 tokens per second)
[GIN] 2026/06/23 - 12:43:11 | 200 | 10.474139815s | 127.0.0.1 | POST "/api/chat"
slot print_timing: id 0 | task 0 | eval time = 1905.69 ms / 20 tokens ( 95.28 ms per token, 10.49 tokens per second)
INFO [_client.py | _send_single_request] HTTP Request: POST http://127.0.0.1:11435/api/chat "HTTP/1.1 200 OK"
slot print_timing: id 0 | task 0 | total time = 2819.06 ms / 82 tokens
slot print_timing: id 0 | task 0 | graphs reused = 19
slot release: id 0 | task 0 | stop processing: n_tokens = 81, truncated = 0
srv update_slots: all slots are idle
slot get_availabl: id 0 | task -1 | selected slot by LCP similarity, sim_best = 0.667 (> 0.100 thold), f_keep = 0.222
srv get_availabl: updating prompt cache
srv prompt_save: - saving prompt with length 81, total state size = 10.127 MiB (draft: 0.000 MiB)
srv load: - looking for better prompt, base f_keep = 0.222, sim = 0.667
srv update: - cache state: 1 prompts, 10.127 MiB (limits: 8192.000 MiB, 16384 tokens, 65525 est)
srv update: - prompt 0x179c820: 81 tokens, checkpoints: 0, 10.127 MiB
srv get_availabl: prompt cache update took 6.32 ms
slot launch_slot_: id 0 | 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 0 | task -1 | sampler params:
repeat_last_n = 64, repeat_penalty = 1.100, frequency_penalty = 0.000, presence_penalty = 0.000
dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 16384
top_k = 40, top_p = 0.900, min_p = 0.000, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 0.800
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000, adaptive_target = -1.000, adaptive_decay = 0.900
slot launch_slot_: id 0 | task 21 | processing task, is_child = 0
slot update_slots: id 0 | task 21 | new prompt, n_ctx_slot = 16384, n_keep = 4, task.n_tokens = 27
slot update_slots: id 0 | task 21 | cached n_tokens = 18, memory_seq_rm [18, end)
slot init_sampler: id 0 | task 21 | init sampler, took 0.02 ms, tokens: text = 27, total = 27
INFO [_client.py | _send_single_request] HTTP Request: POST http://127.0.0.1:11435/api/chat "HTTP/1.1 200 OK"
slot print_timing: id 0 | task 21 | prompt eval time = 692.92 ms / 9 tokens ( 76.99 ms per token, 12.99 tokens per second)
slot print_timing: id 0 | task 21 | eval time = 3323.28 ms / 40 tokens ( 83.08 ms per token, 12.04 tokens per second)
slot print_timing: id 0 | task 21 | total time = 4016.20 ms / 49 tokens
slot print_timing: id 0 | task 21 | graphs reused = 57
slot release: id 0 | task 21 | stop processing: n_tokens = 66, truncated = 0
srv update_slots: all slots are idle
[GIN] 2026/06/23 - 12:43:15 | 200 | 14.493019763s | 127.0.0.1 | POST "/api/chat"
Describe the bug
Hi, I've noticed recently that when adding a model (either by making a child of an existing model, or by creating one from a GGUF), that the initial context I add seems to be completely ignored by the model. This has been the case for numerous models I have tried (Qwen2.5, Qwen 3.5, Gemma 4, Dolphin3 (Uncensored Llama)). This doesn't apply to previously created children of models that I made ~10ish months ago, where the context I added meaningfully altered the responses of the model. Apologies if this is an error in how I'm using the application.
Expected behavior
Models adhere to the system context I give them when creating them.
Screenshots
Demonstrated here with Dolphin, as I know it works completely fine on older child models of Dolphin I created.
Creation of custom model:
Two attempts to recall context:
Blank context when attempting to create child of new custom model:
Debugging information
If relevant, when creating a child of a new custom model, the context field is blank, rather than being prefilled with the custom model's current context to change for the child model.
Not sure if debug log is relevant here, but I added it either way. This is the debug for creating the custom model (as a child of Dolphin), and attempting to use it.