SpoomplesMaxx-Whiskeyjack-12B
"Camp Robber"
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SpoomplesMaxx is a generalist model line with
primary strengths in creative writing and roleplay,
plus competence at instruction following,
reasoning, and tool calling. Whiskeyjack brings the
corvid line to Gemma: a full-parameter SFT of
gemma-4-12B
, trained in both
thinking and non-thinking modes, with thinking off
by default.
Named for
Perisoreus canadensis
— the
Canada jay, better known as the whisky jack or camp
robber. A corvid bold enough to land on your hand
and fly off with your lunch. The 35B got the
jackdaw; the 12B gets the smaller, friendlier
thief.
Prompt format
Gemma 4 uses a new turn format. It shares nothing
with Gemma 3 — there is no
<start_of_turn>
— and the
assistant role is spelled
model
:
<|turn>user
...<turn|>
<|turn>model
<|channel>thought
...reasoning...
<channel|>...answer...<turn|>
STOPS: stop on <turn|> (id 106). <eos> (id 1) is kept as a secondary
EOS, but never set <eos> alone -- turns end on <turn|>.
The control tokens (
<turn|>
,
<|channel>
/
<channel|>
,
<|tool_call>
/
<tool_call|>
)
were audited before training and re-verified after
it: stop battery, boundary probes, and a tool-call
battery all pass on the published checkpoint.
Thinking behavior
Thinking is opt-in and
off by
default
. A
<|think|>
marker at the top of the
system
turn switches it on;
apply_chat_template(enable_thinking=True)
injects it for you. Both modes share the same bare
<|turn>model
generation prefix —
the model decides on its own whether to open
<|channel>thought
.
MODE CONTROL:
(default) thinking OFF -- no marker, no thought channel
enable_thinking=True injects <|think|> into the system turn; the
model opens <|channel>thought on its own
PARSER NOTE: reasoning sits between <|channel>thought and <channel|>;
the visible answer follows <channel|> in the same turn
The chat template is not stock Gemma 4
Upstream Gemma 4 appends an empty thought
channel
(
<|channel>thought\n<channel|>
)
to non-thinking turns. That form shows up
0 times in 1,000 training turns
of this corpus — a no-thoughts turn simply
carries no channel — so the template here drops
it. The stock template ships alongside as
chat_template.gemma-it-original.jinja
.
Restore it and you push the model out of
distribution: reasoning leaks into the answer
and tool calls lose their opener.
What the thoughts look like depends on the system
prompt. Under a SillyTavern-style character card
the model writes a structured planner (
750 chars;
23/23 of the cards that opened a channel). Under
the corpus's own RP framing it writes short
first-person interiority (
90 chars). The model
learned both forms separately, and the prompt picks
which one you get.
The planner, when it shows up:
SCENE: where/when, atmosphere, key environmental details currently in play
CHARACTERS: who is present and their current physical/emotional state and motivation
CONTINUITY: established facts that must stay consistent
THREADS: active tensions and where they stand right now
PLAN: what THIS turn needs to accomplish and the approach it takes
One more thing to expect: a conversational
companion persona usually produces no thought
channel at all (0/6 in testing), even with thinking
on. Companion rows in the corpus are mostly
non-thinking, and the model follows the data.
Tool calling
Gemma 4 tool calls use a DSL,
not
JSON
:
FORM: <|tool_call>call:NAME{key:<|"|>value<|"|>}<tool_call|>
EXAMPLE: <|tool_call>call:get_weather{city:<|"|>Lisbon<|"|>}<tool_call|>
Serve tool calls inside one turn
In the training corpus a whole tool episode
lives inside a single
<|turn>model
, with
<|tool_response>
blocks
interleaved inline. The model never emitted
<turn|>
after a call, so it
never learned to yield there. A harness that
waits for
<turn|>
will hang
while the model keeps generating plausible
calls — the classic infinite tool loop.
SERVE WITH: stop=["<tool_call|>"]
THEN: inject <|tool_response>response:NAME{...}<tool_response|>
and continue the SAME turn
NEVER: wait for <turn|> after a tool call
Key Details
BASE MODEL: google/gemma-4-12B
LICENSE: gemma
NOTE: the base is multimodal, so the checkpoint loads with
AutoModelForImageTextToText (see Quickstart)
Training
METHOD: FULL-PARAMETER SFT -- ms-swift (swift sft), DeepSpeed ZeRO-2,
torch SDPA attention, custom liger fused CE
STAGES: three, each tagged in this repo; main = stage 3
stage 1 (v1-baseline-rp) aviary burn corpus, 1 epoch
1,917 steps @ lr 1e-5 eval 1.311 tok-acc 0.6465
stage 2 (v2-corrected-rp) thinking-weighted resample
568 steps @ lr 2e-6 eval 1.3067 tok-acc 0.6479
stage 3 (main) + 4,000 converted RP-reasoning rows
574 steps @ lr 2e-6 eval 1.301 tok-acc 0.6491
Why there is a stage 3
Stage 2 could think, but only under one prompt
shape: 19,605 of the corpus's 20,666
thought-bearing rows share a single RP framing.
So the model opened a thought channel on 8/8
in-corpus rows — and on 1 of 25 real character
cards. Stage 3 mixed in RP-reasoning rows under
~4,000 distinct character cards so that
thinking no longer depends on one specific
prompt.
opens a thought channel on 25 held-out character cards
(846-5,053 chars, short opening message):
stage 2: 1/25 (4%)
stage 3: 23/25 (92%)
unchanged across the pass:
stop rate 10/10 in both thinking and non-thinking modes
tool-call round trip passes
stray channels with thinking off: 0/3
P(<channel|>) at the true close: 1.000
Sampling
Use the defaults in
generation_config.json
.
"temperature": 1.0,
"top_k": 64,
"top_p": 0.95,
Quickstart
from transformers import AutoModelForImageTextToText, AutoTokenizer
tok = AutoTokenizer.from_pretrained("aimeri/spoomplesmaxx-whiskeyjack-12B")
model = AutoModelForImageTextToText.from_pretrained(
"aimeri/spoomplesmaxx-whiskeyjack-12B",
dtype="bfloat16", device_map="auto")
msgs = [{"role": "user", "content": "Solve (x + 2)^2 = 0."}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True,
enable_thinking=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=512)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=False))
This one will hear how unhinged you are