$ llama-cli -m gpt-oss-20b-MXFP4.gguf --verbose-prompt --flash-attn auto -c 32768 --temp 1.0 --top-k 40 --top-p 1.0 --min-p 0.0 --repeat-penalty 1.0 --chat-template-kwargs '{"reasoning_effort": "medium"}' --verbosity 3
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GB10, compute capability 12.1, VMM: yes
build: 7373 (b7f5f46e0) with GNU 13.3.0 for Linux aarch64
Loading model... |srv load_model: loading model 'gpt-oss-20b-MXFP4.gguf'
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GB10) (000f:01:00.0) - 117771 MiB free
llama_model_loader: loaded meta data with 33 key-value pairs and 459 tensors from gpt-oss-20b-MXFP4.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 = gpt-oss
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Openai_Gpt Oss 20b
llama_model_loader: - kv 3: general.basename str = openai_gpt-oss
llama_model_loader: - kv 4: general.size_label str = 20B
llama_model_loader: - kv 5: gpt-oss.block_count u32 = 24
llama_model_loader: - kv 6: gpt-oss.context_length u32 = 131072
llama_model_loader: - kv 7: gpt-oss.embedding_length u32 = 2880
llama_model_loader: - kv 8: gpt-oss.feed_forward_length u32 = 2880
llama_model_loader: - kv 9: gpt-oss.attention.head_count u32 = 64
llama_model_loader: - kv 10: gpt-oss.attention.head_count_kv u32 = 8
llama_model_loader: - kv 11: gpt-oss.rope.freq_base f32 = 150000.000000
llama_model_loader: - kv 12: gpt-oss.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 13: gpt-oss.expert_count u32 = 32
llama_model_loader: - kv 14: gpt-oss.expert_used_count u32 = 4
llama_model_loader: - kv 15: gpt-oss.attention.key_length u32 = 64
llama_model_loader: - kv 16: gpt-oss.attention.value_length u32 = 64
llama_model_loader: - kv 17: gpt-oss.attention.sliding_window u32 = 128
llama_model_loader: - kv 18: gpt-oss.expert_feed_forward_length u32 = 2880
llama_model_loader: - kv 19: gpt-oss.rope.scaling.type str = yarn
llama_model_loader: - kv 20: gpt-oss.rope.scaling.factor f32 = 32.000000
llama_model_loader: - kv 21: gpt-oss.rope.scaling.original_context_length u32 = 4096
llama_model_loader: - kv 22: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 23: tokenizer.ggml.pre str = gpt-4o
llama_model_loader: - kv 24: tokenizer.ggml.tokens arr[str,201088] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 25: tokenizer.ggml.token_type arr[i32,201088] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 26: tokenizer.ggml.merges arr[str,446189] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv 27: tokenizer.ggml.bos_token_id u32 = 199998
llama_model_loader: - kv 28: tokenizer.ggml.eos_token_id u32 = 200002
llama_model_loader: - kv 29: tokenizer.ggml.padding_token_id u32 = 199999
llama_model_loader: - kv 30: tokenizer.chat_template str = {#-\n In addition to the normal input...
llama_model_loader: - kv 31: general.quantization_version u32 = 2
llama_model_loader: - kv 32: general.file_type u32 = 38
llama_model_loader: - type f32: 289 tensors
llama_model_loader: - type q8_0: 98 tensors
llama_model_loader: - type mxfp4: 72 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = MXFP4 MoE
print_info: file size = 11.27 GiB (4.63 BPW) \load: printing all EOG tokens:
load: - 199999 ('<|endoftext|>')
load: - 200002 ('<|return|>')
load: - 200007 ('<|end|>')
load: - 200012 ('<|call|>')
load: special_eog_ids contains both '<|return|>' and '<|call|>' tokens, removing '<|end|>' token from EOG list
load: special tokens cache size = 21 |load: token to piece cache size = 1.3332 MB
print_info: arch = gpt-oss
print_info: vocab_only = 0
print_info: n_ctx_train = 131072
print_info: n_embd = 2880
print_info: n_embd_inp = 2880
print_info: n_layer = 24
print_info: n_head = 64
print_info: n_head_kv = 8
print_info: n_rot = 64
print_info: n_swa = 128
print_info: is_swa_any = 1
print_info: n_embd_head_k = 64
print_info: n_embd_head_v = 64
print_info: n_gqa = 8
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 = 2880
print_info: n_expert = 32
print_info: n_expert_used = 4
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 = 2
print_info: rope scaling = yarn
print_info: freq_base_train = 150000.0
print_info: freq_scale_train = 0.03125
print_info: n_ctx_orig_yarn = 4096
print_info: rope_yarn_log_mul= 0.0000
print_info: rope_finetuned = unknown
print_info: model type = 20B
print_info: model params = 20.91 B
print_info: general.name = Openai_Gpt Oss 20b
print_info: n_ff_exp = 2880
print_info: vocab type = BPE
print_info: n_vocab = 201088
print_info: n_merges = 446189
print_info: BOS token = 199998 '<|startoftext|>'
print_info: EOS token = 200002 '<|return|>'
print_info: EOT token = 199999 '<|endoftext|>'
print_info: PAD token = 199999 '<|endoftext|>'
print_info: LF token = 198 'Ċ'
print_info: EOG token = 199999 '<|endoftext|>'
print_info: EOG token = 200002 '<|return|>'
print_info: EOG token = 200012 '<|call|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = true) -load_tensors: offloading 24 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 25/25 layers to GPU
load_tensors: CPU_Mapped model buffer size = 586.82 MiB
load_tensors: CUDA0 model buffer size = 10949.38 MiB
\|./..-.\..|./.-..\..|./.-..\..|./..-.\.|../..-.\.|../.-..\.|../.-.\..|../.-.\./.
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 32768
llama_context: n_ctx_seq = 32768
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = auto
llama_context: kv_unified = false
llama_context: freq_base = 150000.0
llama_context: freq_scale = 0.03125
llama_context: n_ctx_seq (32768) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 0.77 MiB
llama_kv_cache_iswa: creating non-SWA KV cache, size = 32768 cells
llama_kv_cache: CUDA0 KV buffer size = 768.00 MiB
llama_kv_cache: size = 768.00 MiB ( 32768 cells, 12 layers, 1/1 seqs), K (f16): 384.00 MiB, V (f16): 384.00 MiB
llama_kv_cache_iswa: creating SWA KV cache, size = 768 cells
llama_kv_cache: CUDA0 KV buffer size = 18.00 MiB
llama_kv_cache: size = 18.00 MiB ( 768 cells, 12 layers, 1/1 seqs), K (f16): 9.00 MiB, V (f16): 9.00 MiB
llama_context: Flash Attention was auto, set to enabled
llama_context: CUDA0 compute buffer size = 398.38 MiB
llama_context: CUDA_Host compute buffer size = 71.15 MiB
llama_context: graph nodes = 1352
llama_context: graph splits = 2
common_init_from_params: added <|endoftext|> logit bias = -inf
common_init_from_params: added <|return|> logit bias = -inf
common_init_from_params: added <|call|> logit bias = -inf
common_init_from_params: setting dry_penalty_last_n to ctx_size = 32768
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 = 32768
srv init: prompt cache is enabled, size limit: 8192 MiB
srv init: use `--cache-ram 0` to disable the prompt cache
srv init: for more info see https://github.com/ggml-org/llama.cpp/pull/16391
srv init: thinking = 0
init: chat template, chat_template: {#-
In addition to the normal inputs of `messages` and `tools`, this template also accepts the
following kwargs:
- "builtin_tools": A list, can contain "browser" and/or "python".
- "model_identity": A string that optionally describes the model identity.
- "reasoning_effort": A string that describes the reasoning effort, defaults to "medium".
#}
{#- Tool Definition Rendering ============================================== #}
{%- macro render_typescript_type(param_spec, required_params, is_nullable=false) -%}
{%- if param_spec.type == "array" -%}
{%- if param_spec['items'] -%}
{%- if param_spec['items']['type'] == "string" -%}
{{- "string[]" }}
{%- elif param_spec['items']['type'] == "number" -%}
{{- "number[]" }}
{%- elif param_spec['items']['type'] == "integer" -%}
{{- "number[]" }}
{%- elif param_spec['items']['type'] == "boolean" -%}
{{- "boolean[]" }}
{%- else -%}
{%- set inner_type = render_typescript_type(param_spec['items'], required_params) -%}
{%- if inner_type == "object | object" or inner_type|length > 50 -%}
{{- "any[]" }}
{%- else -%}
{{- inner_type + "[]" }}
{%- endif -%}
{%- endif -%}
{%- if param_spec.nullable -%}
{{- " | null" }}
{%- endif -%}
{%- else -%}
{{- "any[]" }}
{%- if param_spec.nullable -%}
{{- " | null" }}
{%- endif -%}
{%- endif -%}
{%- elif param_spec.type is defined and param_spec.type is iterable and param_spec.type is not string and param_spec.type is not mapping and param_spec.type[0] is defined -%}
{#- Handle array of types like ["object", "object"] from Union[dict, list] #}
{%- if param_spec.type | length > 1 -%}
{{- param_spec.type | join(" | ") }}
{%- else -%}
{{- param_spec.type[0] }}
{%- endif -%}
{%- elif param_spec.oneOf -%}
{#- Handle oneOf schemas - check for complex unions and fallback to any #}
{%- set has_object_variants = false -%}
{%- for variant in param_spec.oneOf -%}
{%- if variant.type == "object" -%}
{%- set has_object_variants = true -%}
{%- endif -%}
{%- endfor -%}
{%- if has_object_variants and param_spec.oneOf|length > 1 -%}
{{- "any" }}
{%- else -%}
{%- for variant in param_spec.oneOf -%}
{{- render_typescript_type(variant, required_params) -}}
{%- if variant.description %}
{{- "// " + variant.description }}
{%- endif -%}
{%- if variant.default is defined %}
{{ "// default: " + variant.default|tojson }}
{%- endif -%}
{%- if not loop.last %}
{{- " | " }}
{% endif -%}
{%- endfor -%}
{%- endif -%}
{%- elif param_spec.type == "string" -%}
{%- if param_spec.enum -%}
{{- '"' + param_spec.enum|join('" | "') + '"' -}}
{%- else -%}
{{- "string" }}
{%- if param_spec.nullable %}
{{- " | null" }}
{%- endif -%}
{%- endif -%}
{%- elif param_spec.type == "number" -%}
{{- "number" }}
{%- elif param_spec.type == "integer" -%}
{{- "number" }}
{%- elif param_spec.type == "boolean" -%}
{{- "boolean" }}
{%- elif param_spec.type == "object" -%}
{%- if param_spec.properties -%}
{{- "{
" }}
{%- for prop_name, prop_spec in param_spec.properties.items() -%}
{{- prop_name -}}
{%- if prop_name not in (param_spec.required or []) -%}
{{- "?" }}
{%- endif -%}
{{- ": " }}
{{ render_typescript_type(prop_spec, param_spec.required or []) }}
{%- if not loop.last -%}
{{-", " }}
{%- endif -%}
{%- endfor -%}
{{- "}" }}
{%- else -%}
{{- "object" }}
{%- endif -%}
{%- else -%}
{{- "any" }}
{%- endif -%}
{%- endmacro -%}
{%- macro render_tool_namespace(namespace_name, tools) -%}
{{- "## " + namespace_name + "
" }}
{{- "namespace " + namespace_name + " {
" }}
{%- for tool in tools %}
{%- set tool = tool.function %}
{{- "// " + tool.description + "
" }}
{{- "type "+ tool.name + " = " }}
{%- if tool.parameters and tool.parameters.properties %}
{{- "(_: {
" }}
{%- for param_name, param_spec in tool.parameters.properties.items() %}
{%- if param_spec.description %}
{{- "// " + param_spec.description + "
" }}
{%- endif %}
{{- param_name }}
{%- if param_name not in (tool.parameters.required or []) -%}
{{- "?" }}
{%- endif -%}
{{- ": " }}
{{- render_typescript_type(param_spec, tool.parameters.required or []) }}
{%- if param_spec.default is defined -%}
{%- if param_spec.enum %}
{{- ", // default: " + param_spec.default }}
{%- elif param_spec.oneOf %}
{{- "// default: " + param_spec.default }}
{%- else %}
{{- ", // default: " + param_spec.default|tojson }}
{%- endif -%}
{%- endif -%}
{%- if not loop.last %}
{{- ",
" }}
{%- else %}
{{- "
" }}
{%- endif -%}
{%- endfor %}
{{- "}) => any;
" }}
{%- else -%}
{{- "() => any;
" }}
{%- endif -%}
{%- endfor %}
{{- "} // namespace " + namespace_name }}
{%- endmacro -%}
{%- macro render_builtin_tools(browser_tool, python_tool) -%}
{%- if browser_tool %}
{{- "## browser
" }}
{{- "// Tool for browsing.
" }}
{{- "// The `cursor` appears in brackets before each browsing display: `[{cursor}]`.
" }}
{{- "// Cite information from the tool using the following format:
" }}
{{- "// `【{cursor}†L{line_start}(-L{line_end})?】`, for example: `【6†L9-L11】` or `【8†L3】`.
" }}
{{- "// Do not quote more than 10 words directly from the tool output.
" }}
{{- "// sources=web (default: web)
" }}
{{- "namespace browser {
" }}
{{- "// Searches for information related to `query` and displays `topn` results.
" }}
{{- "type search = (_: {
" }}
{{- "query: string,
" }}
{{- "topn?: number, // default: 10
" }}
{{- "source?: string,
" }}
{{- "}) => any;
" }}
{{- "// Opens the link `id` from the page indicated by `cursor` starting at line number `loc`, showing `num_lines` lines.
" }}
{{- "// Valid link ids are displayed with the formatting: `【{id}†.*】`.
" }}
{{- "// If `cursor` is not provided, the most recent page is implied.
" }}
{{- "// If `id` is a string, it is treated as a fully qualified URL associated with `source`.
" }}
{{- "// If `loc` is not provided, the viewport will be positioned at the beginning of the document or centered on the most relevant passage, if available.
" }}
{{- "// Use this function without `id` to scroll to a new location of an opened page.
" }}
{{- "type open = (_: {
" }}
{{- "id?: number | string, // default: -1
" }}
{{- "cursor?: number, // default: -1
" }}
{{- "loc?: number, // default: -1
" }}
{{- "num_lines?: number, // default: -1
" }}
{{- "view_source?: boolean, // default: false
" }}
{{- "source?: string,
" }}
{{- "}) => any;
" }}
{{- "// Finds exact matches of `pattern` in the current page, or the page given by `cursor`.
" }}
{{- "type find = (_: {
" }}
{{- "pattern: string,
" }}
{{- "cursor?: number, // default: -1
" }}
{{- "}) => any;
" }}
{{- "} // namespace browser
" }}
{%- endif -%}
{%- if python_tool %}
{{- "## python
" }}
{{- "Use this tool to execute Python code in your chain of thought. The code will not be shown to the user. This tool should be used for internal reasoning, but not for code that is intended to be visible to the user (e.g. when creating plots, tables, or files).
" }}
{{- "When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 120.0 seconds. The drive at '/mnt/data' can be used to save and persist user files. Internet access for this session is UNKNOWN. Depends on the cluster.
" }}
{%- endif -%}
{%- endmacro -%}
{#- System Message Construction ============================================ #}
{%- macro build_system_message() -%}
{%- if model_identity is not defined %}
{%- set model_identity = "You are ChatGPT, a large language model trained by OpenAI." %}
{%- endif %}
{{- model_identity + "
" }}
{{- "Knowledge cutoff: 2024-06
" }}
{{- "Current date: " + strftime_now("%Y-%m-%d") + "
" }}
{%- if reasoning_effort is not defined %}
{%- set reasoning_effort = "medium" %}
{%- endif %}
{{- "Reasoning: " + reasoning_effort + "
" }}
{%- if builtin_tools %}
{{- "# Tools
" }}
{%- set available_builtin_tools = namespace(browser=false, python=false) %}
{%- for tool in builtin_tools %}
{%- if tool == "browser" %}
{%- set available_builtin_tools.browser = true %}
{%- elif tool == "python" %}
{%- set available_builtin_tools.python = true %}
{%- endif %}
{%- endfor %}
{{- render_builtin_tools(available_builtin_tools.browser, available_builtin_tools.python) }}
{%- endif -%}
{{- "# Valid channels: analysis, commentary, final. Channel must be included for every message." }}
{%- if tools -%}
{{- "
Calls to these tools must go to the commentary channel: 'functions'." }}
{%- endif -%}
{%- endmacro -%}
{#- Main Template Logic ================================================= #}
{#- Set defaults #}
{#- Render system message #}
{{- "<|start|>system<|message|>" }}
{{- build_system_message() }}
{{- "<|end|>" }}
{#- Extract developer message #}
{%- if messages[0].role == "developer" or messages[0].role == "system" %}
{%- set developer_message = messages[0].content %}
{%- set loop_messages = messages[1:] %}
{%- else %}
{%- set developer_message = "" %}
{%- set loop_messages = messages %}
{%- endif %}
{#- Render developer message #}
{%- if developer_message or tools %}
{{- "<|start|>developer<|message|>" }}
{%- if developer_message %}
{{- "# Instructions
" }}
{{- developer_message }}
{%- endif %}
{%- if tools -%}
{{- "
" }}
{{- "# Tools
" }}
{{- render_tool_namespace("functions", tools) }}
{%- endif -%}
{{- "<|end|>" }}
{%- endif %}
{#- Render messages #}
{%- set last_tool_call = namespace(name=none) %}
{%- for message in loop_messages -%}
{#- At this point only assistant/user/tool messages should remain #}
{%- if message.role == 'assistant' -%}
{#- Checks to ensure the messages are being passed in the format we expect #}
{%- if "content" in message %}
{%- if false %}
{{- raise_exception("You have passed a message containing <|channel|> tags in the content field. Instead of doing this, you should pass analysis messages (the string between '<|message|>' and '<|end|>') in the 'thinking' field, and final messages (the string between '<|message|>' and '<|end|>') in the 'content' field.") }}
{%- endif %}
{%- endif %}
{%- if "thinking" in message %}
{%- if "<|channel|>analysis<|message|>" in message.thinking or "<|channel|>final<|message|>" in message.thinking %}
{{- raise_exception("You have passed a message containing <|channel|> tags in the thinking field. Instead of doing this, you should pass analysis messages (the string between '<|message|>' and '<|end|>') in the 'thinking' field, and final messages (the string between '<|message|>' and '<|end|>') in the 'content' field.") }}
{%- endif %}
{%- endif %}
{%- if "tool_calls" in message %}
{#- We assume max 1 tool call per message, and so we infer the tool call name #}
{#- in "tool" messages from the most recent assistant tool call name #}
{%- set tool_call = message.tool_calls[0] %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{%- if message.content and message.thinking %}
{{- raise_exception("Cannot pass both content and thinking in an assistant message with tool calls! Put the analysis message in one or the other, but not both.") }}
{%- elif message.content %}
{{- "<|start|>assistant<|channel|>analysis<|message|>" + message.content + "<|end|>" }}
{%- elif message.thinking %}
{{- "<|start|>assistant<|channel|>analysis<|message|>" + message.thinking + "<|end|>" }}
{%- endif %}
{{- "<|start|>assistant to=" }}
{{- "functions." + tool_call.name + "<|channel|>commentary " }}
{{- (tool_call.content_type if tool_call.content_type is defined else "json") + "<|message|>" }}
{{- tool_call.arguments|tojson }}
{{- "<|call|>" }}
{%- set last_tool_call.name = tool_call.name %}
{%- elif loop.last and not add_generation_prompt %}
{#- Only render the CoT if the final turn is an assistant turn and add_generation_prompt is false #}
{#- This is a situation that should only occur in training, never in inference. #}
{%- if "thinking" in message %}
{{- "<|start|>assistant<|channel|>analysis<|message|>" + message.thinking + "<|end|>" }}
{%- endif %}
{#- <|return|> indicates the end of generation, but <|end|> does not #}
{#- <|return|> should never be an input to the model, but we include it as the final token #}
{#- when training, so the model learns to emit it. #}
{{- "<|start|>assistant<|channel|>final<|message|>" + message.content + "<|return|>" }}
{%- else %}
{#- CoT is dropped during all previous turns, so we never render it for inference #}
{{- "<|start|>assistant<|channel|>final<|message|>" + message.content + "<|end|>" }}
{%- set last_tool_call.name = none %}
{%- endif %}
{%- elif message.role == 'tool' -%}
{%- if last_tool_call.name is none %}
{{- raise_exception("Message has tool role, but there was no previous assistant message with a tool call!") }}
{%- endif %}
{{- "<|start|>functions." + last_tool_call.name }}
{{- " to=assistant<|channel|>commentary<|message|>" + message.content|tojson + "<|end|>" }}
{%- elif message.role == 'user' -%}
{{- "<|start|>user<|message|>" + message.content + "<|end|>" }}
{%- endif -%}
{%- endfor -%}
{#- Generation prompt #}
{%- if add_generation_prompt -%}
<|start|>assistant
{%- endif -%}, example_format: '<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.
Knowledge cutoff: 2024-06
Current date: 2025-12-28
Reasoning: medium
# Valid channels: analysis, commentary, final. Channel must be included for every message.<|end|><|start|>developer<|message|># Instructions
You are a helpful assistant<|end|><|start|>user<|message|>Hello<|end|><|start|>assistant<|channel|>final<|message|>Hi there<|end|><|start|>user<|message|>How are you?<|end|><|start|>assistant'
srv update_slots: all slots are idle
▄▄ ▄▄
██ ██
██ ██ ▀▀█▄ ███▄███▄ ▀▀█▄ ▄████ ████▄ ████▄
██ ██ ▄█▀██ ██ ██ ██ ▄█▀██ ██ ██ ██ ██ ██
██ ██ ▀█▄██ ██ ██ ██ ▀█▄██ ██ ▀████ ████▀ ████▀
██ ██
▀▀ ▀▀
build : b7373-b7f5f46e0
model : gpt-oss-20b-MXFP4.gguf
modalities : text
available commands:
/exit or Ctrl+C stop or exit
/regen regenerate the last response
/clear clear the chat history
/read add a text file
> 研修医が主人公の物語を書いて
|slot get_availabl: id 0 | task -1 | selected slot by LRU, t_last = -1
slot launch_slot_: id 0 | task -1 | sampler chain: logits -> logit-bias -> penalties -> dry -> top-n-sigma -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist
slot launch_slot_: id 0 | task 0 | processing task
slot update_slots: id 0 | task 0 | new prompt, n_ctx_slot = 32768, n_keep = 0, task.n_tokens = 78
slot update_slots: id 0 | task 0 | n_tokens = 0, memory_seq_rm [0, end)
slot update_slots: id 0 | task 0 | prompt processing progress, n_tokens = 14, batch.n_tokens = 14, progress = 0.179487
slot update_slots: id 0 | task 0 | n_tokens = 14, memory_seq_rm [14, end)
slot update_slots: id 0 | task 0 | prompt processing progress, n_tokens = 78, batch.n_tokens = 64, progress = 1.000000
slot update_slots: id 0 | task 0 | prompt done, n_tokens = 78, batch.n_tokens = 64 <|channel|>analysis<|message|>The user wants a story with a resident physician (研修医) as the protagonist. They wrote in Japanese: "研修医が主人公の物語を書いて" which says "Write a story with a resident doctor as the protagonist." They probably want a narrative. The story can be in Japanese (since the prompt is Japanese). I should write a story about a medical resident, possibly in a hospital. It could involve their daily life, challenges, growth, maybe a specific case, personal development. Provide some content, maybe about a specific patient, a moral, or a learning moment.
The user didn't specify length, but a moderate story. Perhaps about a young Japanese medical resident, their experiences, a critical moment, maybe with Japanese cultural aspects. They may want drama. We can go with a story of a resident working overtime, a challenging case that makes them question empathy, and how they overcome. Let's do first-person narrative to bring intimacy. Use vivid details.
In Japanese: Start with "私は…". Provide a setting: a tertiary hospital. The protagonist is a junior resident (研修医). Maybe talk about a case: an elderly woman with heart failure, the resident's struggle. The story includes a mentor or senior doctor, their learning. It could also touch on the Japanese work culture, "jikan no kabe", etc. It should be compelling. Provide maybe an ending with a lesson.
It is okay. Let's include some typical themes: 12-hour shifts, emotional toll.
Make it engaging. Add introspective elements, mention the protagonist's name, maybe a mentor. Good structure: beginning (introduction, environment, protagonist's initial state), middle (conflict/incident), climax (decision or event), resolution (growth, new understanding). Use descriptive language, possibly incorporate internal monologue.
Let's write it. We need to maintain Japanese language. Let's use a moderate length, maybe ~800-1000 characters (~500-600 Japanese characters). The story may be a short story.
We can include an ending with a reflection on the profession. Let's do it.
We'll use first-person perspective. Also, we can add a twist: a patient, the doctor's inner conflict, and an outcome that inspires.
Let's proceed.<|end|><|start|>assistant<|channel|>final<|message|>私は研修医の安藤翔太。臨床第２年目の夜勤、白衣に濡れる汗。外観は変わらないが、心の奥は急に揺れていた。いつもは「残業が終われば家に帰れ」と考えていた私は、高橋医科大学病院の救急外来を抜け、急患センターへ向かった。
その日午後、急にエマージェンシーが入った―70歳超の女性、佐藤昭子さん（仮名）が呼吸困難で来院。心電図は不整脈、血圧は安定そうに見えたが胸部X線では肺水腫の兆し。私は担当を任せられた。心臓病専門医の佐藤医師が指示を出す前に、彼女の手の震えに気づいた。『大丈夫、落ち着いて』私は小声で言う。だが、彼女は『もう死にたくない』と涙を流した。
私は患者の記録を確認した。彼女は長年高血圧を患い、心不全を患ったこともある。手術も避けられず、医療費の支払いに悩み、夫の生活に迷いを抱えていた。こうした背景を考えると、単なる医学的処置では足りないと痛感した。医学の“人”としての役割が頭に浮かんだ。
その場で、私は心肺蘇生を行い、緊急の人工呼吸器を接続した。生命を保てたかどうか、心に無理があった。だが、救命処置後に佐藤さんは「大丈夫よ」と笑った。私は胸が熱くなるのを感じ、胸痛に耐える患者と自分の胸が同じように痛むことに気づいた。
その晩、私はベッドサイドで彼女に話しの機会を設けた。彼女は「医者になってくれてありがとう」―と、ひそかに涙を流したのだ。医者として、血圧を下げる薬を投与するだけではなく、心の痛みにも寄り添うべきだ。――と胸に沁みた。私はこれまで、医学は数字と検査だと考えていた。今、私はそれを超える存在だと改めて悟った。
翌朝、病棟に戻ると、先輩の安藤三郎先生が「大丈夫か？」と声をかけた。私は恥ずかしそうにうなずくと、今日の体験を共有した。「患者の心も治療の一部だと実感した」と。三郎先生はしばらく黙った後、静かに言った。「自分の仕事は診療だけじゃない。人の想いに耳を傾けることも診療だ。お前はその点で素晴らしい。」
私はその言葉を胸に、次の夜の患者にもう一度立ち向かった。今夜、私は心から“患者さんの言葉に耳を傾ける”ことを決意し、医師としての本当の使命を感じた。医者としての道は長い。残っている時間は、人と人の絆を糸で結ぶためのものだったと改めて気づいたのでした。slot print_timing: id 0 | task 0 |
prompt eval time = 211.05 ms / 78 tokens ( 2.71 ms per token, 369.58 tokens per second)
eval time = 15030.66 ms / 1262 tokens ( 11.91 ms per token, 83.96 tokens per second)
total time = 15241.71 ms / 1340 tokens
slot release: id 0 | task 0 | stop processing: n_tokens = 1339, truncated = 0
srv update_slots: all slots are idle
[ Prompt: 369.6 t/s | Generation: 84.0 t/s ]
>
Last active
December 28, 2025 14:14
-
-
Save gpsnmeajp/5e2a9eec32fb8ea5ed7f5da6f13cf757 to your computer and use it in GitHub Desktop.
llamacpp gpt-oss:20b t=1.0
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment