Instructions to use TeichAI/Gemma-4-31B-Fable-5-Agent-Distill-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TeichAI/Gemma-4-31B-Fable-5-Agent-Distill-LoRA with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TeichAI/Gemma-4-31B-Fable-5-Agent-Distill-LoRA", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use TeichAI/Gemma-4-31B-Fable-5-Agent-Distill-LoRA with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for TeichAI/Gemma-4-31B-Fable-5-Agent-Distill-LoRA to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for TeichAI/Gemma-4-31B-Fable-5-Agent-Distill-LoRA to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for TeichAI/Gemma-4-31B-Fable-5-Agent-Distill-LoRA to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="TeichAI/Gemma-4-31B-Fable-5-Agent-Distill-LoRA", max_seq_length=2048, )
File size: 27,771 Bytes
f6730c6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 | {%- macro format_parameters(properties, required, filter_keys=false) -%}
{%- set standard_keys = ['description', 'type', 'properties', 'required', 'nullable'] -%}
{%- set ns = namespace(found_first=false) -%}
{%- for key, value in properties | dictsort -%}
{%- set add_comma = false -%}
{%- if not filter_keys or key not in standard_keys -%}
{%- if ns.found_first %},{% endif -%}
{%- set ns.found_first = true -%}
{{ key }}:{
{%- if value['description'] -%}
description:<|"|>{{ value['description'] }}<|"|>
{%- set add_comma = true -%}
{%- endif -%}
{%- if (value['type'] | default('')) | upper == 'STRING' -%}
{%- if value['enum'] -%}
{%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
enum:{{ format_argument(value['enum']) }}
{%- endif -%}
{%- elif (value['type'] | default('')) | upper == 'ARRAY' -%}
{%- if value['items'] is mapping and value['items'] -%}
{%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
items:{
{%- set ns_items = namespace(found_first=false) -%}
{%- for item_key, item_value in value['items'] | dictsort -%}
{%- if item_value is not none -%}
{%- if ns_items.found_first %},{% endif -%}
{%- set ns_items.found_first = true -%}
{%- if item_key == 'properties' -%}
properties:{
{%- if item_value is mapping -%}
{{- format_parameters(item_value, value['items']['required'] | default([])) -}}
{%- endif -%}
}
{%- elif item_key == 'required' -%}
required:[
{%- for req_item in item_value -%}
<|"|>{{- req_item -}}<|"|>
{%- if not loop.last %},{% endif -%}
{%- endfor -%}
]
{%- elif item_key == 'type' -%}
{%- if item_value is string -%}
type:{{ format_argument(item_value | upper) }}
{%- else -%}
type:{{ format_argument(item_value | map('upper') | list) }}
{%- endif -%}
{%- else -%}
{{ item_key }}:{{ format_argument(item_value) }}
{%- endif -%}
{%- endif -%}
{%- endfor -%}
}
{%- endif -%}
{%- endif -%}
{%- if value['nullable'] %}
{%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
nullable:true
{%- endif -%}
{%- if (value['type'] | default('')) | upper == 'OBJECT' -%}
{%- if value['properties'] is defined and value['properties'] is mapping -%}
{%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
properties:{
{{- format_parameters(value['properties'], value['required'] | default([])) -}}
}
{%- elif value is mapping -%}
{%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
properties:{
{{- format_parameters(value, value['required'] | default([]), filter_keys=true) -}}
}
{%- endif -%}
{%- if value['required'] -%}
{%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
required:[
{%- for item in value['required'] | default([]) -%}
<|"|>{{- item -}}<|"|>
{%- if not loop.last %},{% endif -%}
{%- endfor -%}
]
{%- endif -%}
{%- endif -%}
{%- if value['type'] is defined and value['type'] is not none -%}
{%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
type:<|"|>{{ value['type'] | upper }}<|"|>
{%- endif -%}
}
{%- endif -%}
{%- endfor -%}
{%- endmacro -%}
{%- macro format_function_declaration(tool_data) -%}
declaration:{{- tool_data['function']['name'] -}}{description:<|"|>{{- tool_data['function']['description'] -}}<|"|>
{%- set params = tool_data['function']['parameters'] -%}
{%- if params -%}
,parameters:{
{%- if params['properties'] -%}
properties:{ {{- format_parameters(params['properties'], params['required']) -}} },
{%- endif -%}
{%- if params['required'] -%}
required:[
{%- for item in params['required'] -%}
<|"|>{{- item -}}<|"|>
{{- ',' if not loop.last -}}
{%- endfor -%}
],
{%- endif -%}
{%- if params['type'] is defined and params['type'] is not none -%}
type:<|"|>{{- params['type'] | upper -}}<|"|>
{%- endif -%}
}
{%- endif -%}
{%- if 'response' in tool_data['function'] -%}
{%- set response_declaration = tool_data['function']['response'] -%}
,response:{
{%- if response_declaration['description'] -%}
description:<|"|>{{- response_declaration['description'] -}}<|"|>,
{%- endif -%}
{%- if (response_declaration['type'] | default('')) | upper == 'OBJECT' -%}
type:<|"|>{{- response_declaration['type'] | upper -}}<|"|>
{%- endif -%}
}
{%- endif -%}
}
{%- endmacro -%}
{%- macro format_argument(argument, escape_keys=True) -%}
{#- P1 (public fork): emit JSON null for None values rather than the
bare string "None". Jinja's default coercion of Python's None
goes through str(None) -> "None", which then leaks into the
Gemma 4 DSL as a literal token the model has never been trained
on. Common bite path: a coding tool's optional argument
(language=null in a find-files call, after=null in a search,
etc.) → upstream emits after:None in the DSL → model
confusion. We emit after:null instead, matching the JSON wire
format the model has actually seen.
Branch ordering: `is none` must precede `is string`, `is
mapping`, `is iterable`, etc., because None matches NONE of
them in Jinja's type tests but the final else-branch
({{ argument }}) would otherwise stringify it. -#}
{%- if argument is none -%}
{{- 'null' -}}
{%- elif argument is string -%}
{{- '<|"|>' + argument + '<|"|>' -}}
{%- elif argument is boolean -%}
{{- 'true' if argument else 'false' -}}
{%- elif argument is mapping -%}
{{- '{' -}}
{%- set ns = namespace(found_first=false) -%}
{%- for key, value in argument | dictsort -%}
{%- if ns.found_first %},{% endif -%}
{%- set ns.found_first = true -%}
{%- if escape_keys -%}
{{- '<|"|>' + key + '<|"|>' -}}
{%- else -%}
{{- key -}}
{%- endif -%}
:{{- format_argument(value, escape_keys=escape_keys) -}}
{%- endfor -%}
{{- '}' -}}
{%- elif argument is iterable -%}
{{- '[' -}}
{%- for item in argument -%}
{{- format_argument(item, escape_keys=escape_keys) -}}
{%- if not loop.last %},{% endif -%}
{%- endfor -%}
{{- ']' -}}
{%- else -%}
{{- argument -}}
{%- endif -%}
{%- endmacro -%}
{%- macro strip_thinking(text) -%}
{%- set ns = namespace(result='') -%}
{%- for part in text.split('<channel|>') -%}
{%- if '<|channel>' in part -%}
{%- set ns.result = ns.result + part.split('<|channel>')[0] -%}
{%- else -%}
{%- set ns.result = ns.result + part -%}
{%- endif -%}
{%- endfor -%}
{{- ns.result | trim -}}
{%- endmacro -%}
{%- macro format_tool_response_block(tool_name, response) -%}
{{- '<|tool_response>' -}}
{%- if response is mapping -%}
{{- 'response:' + tool_name + '{' -}}
{%- for key, value in response | dictsort -%}
{{- key -}}:{{- format_argument(value, escape_keys=False) -}}
{%- if not loop.last %},{% endif -%}
{%- endfor -%}
{{- '}' -}}
{%- else -%}
{{- 'response:' + tool_name + '{value:' + format_argument(response, escape_keys=False) + '}' -}}
{%- endif -%}
{{- '<tool_response|>' -}}
{%- endmacro -%}
{%- set ns = namespace(prev_message_type=None) -%}
{%- set loop_messages = messages -%}
{#- P2 (public fork): default enable_thinking to TRUE.
Why: Gemma 4's upstream template defaults enable_thinking to False
(or undefined). This is wrong for agentic coding harnesses for two
reasons:
1. Google's own model card: thinking "significantly enhances
function-calling accuracy" — and tool calling IS the core
contract that coding harnesses use the model for. Defaulting it
off means most opencode/pi users see degraded tool accuracy and
have no obvious way to fix it.
2. Most OpenAI-compatible SDKs (notably Vercel AI SDK used by
opencode) strip unknown request fields, so a harness that tries
to pass chat_template_kwargs.enable_thinking=true per request
has it silently dropped. See:
https://github.com/anomalyco/opencode/issues/24264
Flipping the SERVER-SIDE default to True makes "the agentic
happy-path" the default and lets harnesses that explicitly want
chat-only behaviour override it to false per request:
{"extra_body":{"chat_template_kwargs":{"enable_thinking":false}}}
After this `set`, enable_thinking is unconditionally defined as a
bool, so downstream `is defined` guards are dropped. -#}
{%- set enable_thinking = enable_thinking | default(true) -%}
{{- bos_token -}}
{#- Handle System/Tool Definitions Block -#}
{%- if enable_thinking or tools or messages[0]['role'] in ['system', 'developer'] -%}
{{- '<|turn>system\n' -}}
{#- Inject Thinking token at the very top of the FIRST system turn -#}
{%- if enable_thinking -%}
{{- '<|think|>\n' -}}
{%- set ns.prev_message_type = 'think' -%}
{%- endif -%}
{%- if messages[0]['role'] in ['system', 'developer'] -%}
{%- if messages[0]['content'] is string -%}
{{- messages[0]['content'] | trim -}}
{%- elif messages[0]['content'] is iterable -%}
{%- for item in messages[0]['content'] -%}
{{- item['text'] | trim + ' '-}}
{%- endfor -%}
{%- endif -%}
{%- set loop_messages = messages[1:] -%}
{%- endif -%}
{%- if tools -%}
{%- for tool in tools %}
{{- '<|tool>' -}}
{{- format_function_declaration(tool) | trim -}}
{{- '<tool|>' -}}
{%- endfor %}
{%- set ns.prev_message_type = 'tool' -%}
{%- endif -%}
{{- '<turn|>\n' -}}
{%- endif %}
{#- P4 (public fork): preserve_thinking kwarg, default TRUE.
Why: upstream's reasoning re-emission gate fires only when an
assistant message (a) carries `reasoning`/`reasoning_content`,
(b) has tool_calls, AND (c) is AFTER the last user message. That
third clause is what causes the canonical multi-turn-tool-loop
breakage:
User: "find files matching '*.py' in src"
Assistant: (reasoning=...calling find_files...) tool_call:
find_files(pattern='*.py', dir='src')
Tool: [result list]
User: "now look for '*.ts' too"
Assistant: (reasoning=...) tool_call: find_files(pattern={}, dir={})
↑↑↑ arguments collapse to empty here because the prior
reasoning the model would have learned to imitate is
invisible — the previous-turn <|channel> was dropped.
The same shape was reported on Qwen3.6 and resolved by the
preserve_thinking kwarg there:
https://github.com/earendil-works/pi/issues/3325
Gemma 4's model card says "historical model output should only
include the final response" — that guidance is correct for plain
chat but actively harmful for multi-turn agentic tool calling. P4
optionally drops the (c) gate so prior reasoning stays visible to
the model on subsequent turns.
Set preserve_thinking=false to recover upstream behaviour exactly
(used by the conformance suite to verify byte-identity). -#}
{%- set preserve_thinking = preserve_thinking | default(true) -%}
{#- Pre-scan: find last user message index for reasoning guard -#}
{%- set ns_turn = namespace(last_user_idx=-1) -%}
{%- for i in range(loop_messages | length) -%}
{%- if loop_messages[i]['role'] == 'user' -%}
{%- set ns_turn.last_user_idx = i -%}
{%- endif -%}
{%- endfor -%}
{#- Loop through messages -#}
{%- for message in loop_messages -%}
{%- if message['role'] != 'tool' -%}
{%- set ns.prev_message_type = None -%}
{%- set role = 'model' if message['role'] == 'assistant' else message['role'] -%}
{#- Detect continuation: suppress duplicate <|turn>model when previous non-tool message was also assistant -#}
{%- set prev_nt = namespace(role=None, found=false) -%}
{%- if loop.index0 > 0 -%}
{%- for j in range(loop.index0 - 1, -1, -1) -%}
{%- if not prev_nt.found -%}
{%- if loop_messages[j]['role'] != 'tool' -%}
{%- set prev_nt.role = loop_messages[j]['role'] -%}
{%- set prev_nt.found = true -%}
{%- endif -%}
{%- endif -%}
{%- endfor -%}
{%- endif -%}
{%- set continue_same_model_turn = (role == 'model' and prev_nt.role == 'assistant') -%}
{%- if not continue_same_model_turn -%}
{{- '<|turn>' + role + '\n' }}
{%- endif -%}
{#- Render reasoning/reasoning_content as thinking channel.
Upstream gate (all three required to re-emit):
(a) the message carries reasoning or reasoning_content,
(b) the message has tool_calls,
(c) the message is after the last user message in history.
P4 (public fork): when preserve_thinking is true (default), drop
clause (c) so prior assistant turns' <|channel> blocks survive.
See the long P4 comment above the pre-scan for why this matters
for agentic tool loops. The (b) gate stays — re-emitting a
<|channel> on a finalised text-only assistant turn is not in
the model's training distribution. -#}
{%- set thinking_text = message.get('reasoning') or message.get('reasoning_content') -%}
{%- set thinking_gate = (loop.index0 > ns_turn.last_user_idx) or preserve_thinking -%}
{%- if thinking_text and thinking_gate and message.get('tool_calls') -%}
{{- '<|channel>thought\n' + thinking_text + '\n<channel|>' -}}
{%- endif -%}
{%- if message['tool_calls'] -%}
{%- for tool_call in message['tool_calls'] -%}
{%- set function = tool_call['function'] -%}
{{- '<|tool_call>call:' + function['name'] + '{' -}}
{%- if function['arguments'] is mapping -%}
{%- set ns_args = namespace(found_first=false) -%}
{%- for key, value in function['arguments'] | dictsort -%}
{%- if ns_args.found_first %},{% endif -%}
{%- set ns_args.found_first = true -%}
{{- key -}}:{{- format_argument(value, escape_keys=False) -}}
{%- endfor -%}
{%- elif function['arguments'] is none -%}
{#- P3 (public fork): None / missing arguments is
valid (means: call this tool with no args).
Emit an empty {} via the empty for-loop above. -#}
{%- else -%}
{#- P3 (public fork): refuse string (or any other
non-mapping) arguments rather than silently
corrupting the prompt.
Bug surface: many OpenAI-compatible SDKs (most
notably Vercel AI SDK, used by opencode) hand
tool_call.arguments back as a JSON-encoded
STRING — e.g. '{"city":"Tokyo"}' — rather
than the already-deserialized object. The
upstream Gemma 4 template silently emits this
string verbatim inside an extra pair of
braces, producing invalid Gemma 4 DSL:
call:fn{{"city":"Tokyo"}}
(nested braces, JSON colons, quoted keys —
none of which the model has been trained on).
The model usually still produces a plausible
response, which makes the bug INSIDIOUS: it
looks like a quality problem with the model,
not a prompt-corruption bug in the harness.
Fix: harnesses MUST deserialize
tool_calls[].function.arguments
exactly once on ingest and store the object.
See the canonical pi-side discussion:
https://github.com/earendil-works/pi/issues/3325
We raise here so the bug surfaces at the
server (an obvious HTTP error to debug)
rather than as a quiet model-output
regression. -#}
{{- raise_exception(
"custom_pub_chat_template_gemma4: "
"tool_calls[].function.arguments must be a JSON "
"object (mapping). Got a "
~ (function['arguments'] | string | length | string)
~ "-char "
~ (function['arguments'].__class__.__name__ if function['arguments'].__class__ is defined else 'non-mapping')
~ ". This is almost always the harness handing back "
"a JSON-encoded STRING rather than the deserialized "
"object. Deserialize once on ingest and store the "
"object. See: github.com/earendil-works/pi/issues/3325"
) -}}
{%- endif -%}
{{- '}<tool_call|>' -}}
{%- endfor -%}
{%- set ns.prev_message_type = 'tool_call' -%}
{%- endif -%}
{%- set ns_tr_out = namespace(flag=false) -%}
{%- if message.get('tool_responses') -%}
{#- Legacy: tool_responses embedded on the assistant message (Google/Gemma native) -#}
{%- for tool_response in message['tool_responses'] -%}
{{- format_tool_response_block(tool_response['name'] | default('unknown'), tool_response['response']) -}}
{%- set ns_tr_out.flag = true -%}
{%- set ns.prev_message_type = 'tool_response' -%}
{%- endfor -%}
{%- elif message.get('tool_calls') -%}
{#- OpenAI Chat Completions: forward-scan consecutive role:tool messages -#}
{%- set ns_tool_scan = namespace(stopped=false) -%}
{%- for k in range(loop.index0 + 1, loop_messages | length) -%}
{%- if ns_tool_scan.stopped -%}
{%- elif loop_messages[k]['role'] != 'tool' -%}
{%- set ns_tool_scan.stopped = true -%}
{%- else -%}
{%- set follow = loop_messages[k] -%}
{#- Resolve tool_call_id to function name -#}
{%- set ns_tname = namespace(name=follow.get('name') | default('unknown')) -%}
{%- for tc in message['tool_calls'] -%}
{%- if tc.get('id') == follow.get('tool_call_id') -%}
{%- set ns_tname.name = tc['function']['name'] -%}
{%- endif -%}
{%- endfor -%}
{#- Handle content as string or content-parts array -#}
{%- set tool_body = follow.get('content') -%}
{%- if tool_body is string -%}
{{- format_tool_response_block(ns_tname.name, tool_body) -}}
{%- elif tool_body is iterable and tool_body is not string -%}
{%- set ns_txt = namespace(s='') -%}
{%- for part in tool_body -%}
{%- if part.get('type') == 'text' -%}
{%- set ns_txt.s = ns_txt.s + (part.get('text') | default('')) -%}
{%- endif -%}
{%- endfor -%}
{{- format_tool_response_block(ns_tname.name, ns_txt.s) -}}
{%- for part in tool_body -%}
{%- if part.get('type') == 'image' -%}
{{- '<|image|>' -}}
{%- elif part.get('type') == 'audio' -%}
{{- '<|audio|>' -}}
{%- elif part.get('type') == 'video' -%}
{{- '<|video|>' -}}
{%- endif -%}
{%- endfor -%}
{%- else -%}
{{- format_tool_response_block(ns_tname.name, tool_body) -}}
{%- endif -%}
{%- set ns_tr_out.flag = true -%}
{%- set ns.prev_message_type = 'tool_response' -%}
{%- endif -%}
{%- endfor -%}
{%- endif -%}
{%- set captured_content -%}
{%- if message['content'] is string -%}
{%- if role == 'model' -%}
{{- strip_thinking(message['content']) -}}
{%- else -%}
{{- message['content'] | trim -}}
{%- endif -%}
{%- elif message['content'] is iterable -%}
{%- for item in message['content'] -%}
{%- if item['type'] == 'text' -%}
{%- if role == 'model' -%}
{{- strip_thinking(item['text']) -}}
{%- else -%}
{{- item['text'] | trim -}}
{%- endif -%}
{%- elif item['type'] == 'image' -%}
{{- '<|image|>' -}}
{%- set ns.prev_message_type = 'image' -%}
{%- elif item['type'] == 'audio' -%}
{{- '<|audio|>' -}}
{%- set ns.prev_message_type = 'audio' -%}
{%- elif item['type'] == 'video' -%}
{{- '<|video|>' -}}
{%- set ns.prev_message_type = 'video' -%}
{%- endif -%}
{%- endfor -%}
{%- endif -%}
{%- endset -%}
{{- captured_content -}}
{%- set has_content = captured_content | trim | length > 0 -%}
{#- P5 (public fork): symmetric continuation close-suppression
for HF discussion #62.
The bug: upstream's open suppression at the top of this
iteration drops the `<|turn>model\n` header when the
previous non-tool message was also assistant — but the
close below ALWAYS emits `<turn|>\n`. Two back-to-back
text-only assistant messages therefore render as:
<|turn>model\npart 1<turn|>\npart 2<turn|>\n
That's one open, two closes — malformed. The model
(Google-confirmed in HF discussion #62) sees it as a
truncated and re-opened turn, which destabilises long
multi-step agentic histories that accumulate consecutive
assistant messages.
Fix: forward-scan for the next non-tool message. If it is
another assistant AND this iteration is a TEXT-ONLY
assistant message (no tool_calls, no tool_responses), the
next iteration will continue this same turn frame, so
suppress this iteration's close and emit a single `\n` so
the two contents don't byte-glue together.
The narrowing condition (`not message.get('tool_calls')
and not ns_tr_out.flag`) is critical: the tool-call +
tool-response chain MUST close normally so the model still
sees a balanced turn frame around the `<|tool_response>`
block. Conformance test T13 locks this in. -#}
{%- set next_nt = namespace(role=None, found=false) -%}
{%- for j in range(loop.index0 + 1, loop_messages | length) -%}
{%- if not next_nt.found -%}
{%- if loop_messages[j]['role'] != 'tool' -%}
{%- set next_nt.role = loop_messages[j]['role'] -%}
{%- set next_nt.found = true -%}
{%- endif -%}
{%- endif -%}
{%- endfor -%}
{%- set continues_into_next = (
role == 'model'
and next_nt.role == 'assistant'
and not message.get('tool_calls')
and not ns_tr_out.flag
) -%}
{%- if ns.prev_message_type == 'tool_call' and not ns_tr_out.flag -%}
{{- '<|tool_response>' -}}
{%- elif continues_into_next -%}
{{- '\n' -}}
{%- elif not (ns_tr_out.flag and not has_content) -%}
{{- '<turn|>\n' -}}
{%- endif -%}
{%- endif -%}
{%- endfor -%}
{%- if add_generation_prompt -%}
{%- if ns.prev_message_type != 'tool_response' and ns.prev_message_type != 'tool_call' -%}
{{- '<|turn>model\n' -}}
{#- When thinking is disabled, the upstream contract is to
pre-fill an empty `<|channel>thought\n<channel|>` block so
the model skips reasoning. After P2's set at the top of
the file, `enable_thinking` is unconditionally a bool, so
the upstream `| default(false)` is unnecessary. (It also
had a Jinja precedence trap: `|` binds tighter than `not`,
parsing as `not (enable_thinking | default(false))`. The
simple `not enable_thinking` form is equivalent and
clearer.) -#}
{%- if not enable_thinking -%}
{{- '<|channel>thought\n<channel|>' -}}
{%- endif -%}
{%- endif -%}
{%- endif -%} |