aimeri/spoomplesmaxx-whiskeyjack-12B

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