| --- |
| tags: |
| - general-purpose |
| - roleplay |
| - creative-writing |
| - storywriting |
| - reasoning |
| - qwen3 |
| - chatml |
| - finetune |
| - SFT |
| - text-generation-inference |
| language: |
| - en |
| - pt |
| base_model: |
| - Qwen/Qwen3-14B-Base |
| pipeline_tag: text-generation |
| library_name: transformers |
| license: apache-2.0 |
| datasets: |
| - aimeri/spoomplesmaxx-sft-full-v2 |
| --- |
| <!doctype html> |
| <html lang="en"> |
| <head> |
| <meta charset="UTF-8" /> |
| <meta name="viewport" content="width=device-width, initial-scale=1.0" /> |
| <title>SpoomplesMaxx V2.1 Mini 14B</title> |
| </head> |
| <div class="crt-container"> |
| <div class="crt-case"> |
| <div class="crt-inner-case"> |
| <div class="crt-bezel"> |
| <div class="terminal-screen"> |
| <div style="text-align: center"> |
| <h2>SpoomplesMaxx-V2.1-Mini-14B</h2> |
| <h3>"Flight of the Cockatiels"</h3> |
| <pre class="code-block" style="display: inline-block; text-align: left; font-size: clamp(2px, 0.4vw, 12px); line-height: 1; max-width: 100%; overflow: hidden; white-space: pre;"> |
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| |
| </pre> |
| </div> |
| <p> |
| SpoomplesMaxx is a generalist model with primary |
| strengths in creative writing and roleplay, plus |
| light competence at instruction following and |
| reasoning. |
| </p> |
| <p> |
| The Mini brings the v2.1 data and training recipe |
| to a 14B you can run on a single 24GB card. Smaller |
| bird, same energy. |
| </p> |
| <div class="notice"> |
| <h3>What's new in v2.1 Mini</h3> |
| <p> |
| The Mini keeps the v2.1 <strong>data mix</strong> — including the |
| long-context roleplay corpus where each in-character turn is |
| preceded by an explicit <code><think></code> planning |
| scratchpad — and swaps the base model down a weight class. |
| Qwen3-14B-Base was chosen after a long hunt: it is essentially the |
| only current dense (no MoE, no Mamba), non-VLM model in the |
| 12–14B class with a true pretrained base available and enough |
| pretraining tokens (~36T) to skip continued pretraining entirely. |
| </p> |
| <pre class="code-block"> |
| CHANGED SINCE v2.1 (30B) |
| - Base model: Granite 4.1 30B -> Qwen3-14B-Base. Template is now |
| standard ChatML with native <think>/</think> reasoning. |
| - Control-token heal: a dedicated post-SFT training stage to revive |
| Qwen3-Base's dead special tokens (see notice below). |
| - Content-conditional thinking election (emergent -- see |
| "Thinking behavior"). |
| |
| UNCHANGED |
| - Same SFT corpus (aimeri/spoomplesmaxx-sft-full-v2), same story |
| scratchpad format, same personas. |
| - Still no tool-calling data -- reserved for a dedicated future run. |
| - Still focused on creative writing, roleplay, and companion use. |
| </pre> |
| </div> |
| <div class="notice"> |
| <h3>The control-token heal (PSA for Qwen3-Base finetuners)</h3> |
| <p> |
| Qwen ships Qwen3-14B-Base with the ChatML/thinking tokens |
| (<code><|im_start|></code>, <code><|im_end|></code>, |
| <code><think></code>, <code></think></code>, tool |
| tokens) present in the vocab but <strong>never trained</strong>: |
| their <code>lm_head</code> rows are literally one shared stub |
| vector (norm 0.286, pairwise cosine 1.000). A standard frozen-head |
| QLoRA SFT on this base learns to <em>reason</em> but physically |
| cannot <em>emit</em> <code></think></code> or |
| <code><|im_end|></code> — the symptom is a perfect |
| reasoning trace that ends in a random stray token where the close |
| tag should be. |
| </p> |
| <p> |
| The fix shipped in this model: the special-token rows were grafted |
| from <code>Qwen/Qwen3-14B</code> (same vocab, dims, and lineage), |
| then a short single-GPU heal (500 steps, plain HF + PEFT, fresh |
| attn/MLP LoRA + trainable <code>embed_tokens</code> / |
| <code>lm_head</code>) taught the model to open and close the block |
| natively. Post-heal, P(<code></think></code>) at true close |
| positions measures <strong>0.998</strong> and every generation |
| terminates on <code><|im_end|></code>. If you are finetuning |
| any Qwen3 base: check your special-token row norms and pairwise |
| cosines before you burn the GPU hours. |
| </p> |
| </div> |
| <h3>Thinking behavior</h3> |
| <p> |
| This model elects thinking <strong>by content</strong>. Reasoning-shaped |
| prompts and roleplay cards with the scratchpad open |
| <code><think></code> unprompted (18/20 in the greedy test |
| battery); casual chat skips the ceremony and just answers. With |
| SillyTavern cards as system prompts it reasons the scratchpad |
| correctly on its own. |
| </p> |
| <pre class="code-block"> |
| MODE CONTROL (baked into the chat template): |
| enable_thinking=True forced thinking -- the template prefills |
| <think>\n so every turn reasons (deliberate |
| deviation from the stock Qwen3 template) |
| enable_thinking=False forced off -- empty <think>\n\n</think> |
| block (Qwen3 convention); the reasoning |
| migrates into the visible answer |
| (unset) the model elects by content -- the default |
| election behavior described above |
| |
| SILLYTAVERN: ST builds prompts itself. ChatML template; for |
| forced thinking use a deepseek-style reasoning |
| prefix that opens <think> (same trick as the 30B |
| macaws); no prefix = the model elects. |
| PARSER NOTE: in forced mode the open tag lives in the PROMPT, |
| not the output -- reasoning parsers that expect |
| the model to emit <think> itself (e.g. vLLM's |
| qwen3 parser) should use a deepseek-style parser |
| for that mode. |
| LONG CHATS: do NOT feed prior-turn think blocks back into |
| context (the chat template already strips them; |
| leave ST's "add reasoning to prompt" off). |
| Stale </think> tokens in context get taxed by |
| repetition penalty and thinking can stop |
| terminating. |
| </pre> |
| <p>The story scratchpad format, carried over from v2.1:</p> |
| <pre class="code-block"> |
| 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 |
| </pre> |
| <h3>Key Details</h3> |
| <pre class="code-block"> |
| BASE MODEL: Qwen/Qwen3-14B-Base |
| LICENSE: apache-2.0 |
| LANGUAGES: English & Portuguese (reasoning traces); multilingual via base</pre> |
| <h3>Training</h3> |
| <pre class="code-block"> |
| DATASET: aimeri/spoomplesmaxx-sft-full-v2 |
| STAGE 1: QLoRA SFT (4-bit NF4 base), Unsloth DDP, all-linear, |
| LoRA rank 128 / alpha 256 |
| CONTEXT: up to 32,768 tokens, BFD sample packing (padding-free) |
| SCHEDULE: 2 epochs / 764 steps, lr 1e-4 cosine, warmup 0.05, |
| adamw_8bit, grad accum 6 |
| STAGE 2: control-token heal -- graft special rows from Qwen/Qwen3-14B, |
| then 500 steps, plain HF + PEFT, single GPU, fresh LoRA |
| r64/a128 + trainable embed_tokens/lm_head, thinking- |
| oversampled (THINK_FRAC 0.7), embed lr 10x below trunk |
| RESULT: eval loss 4.02 -> 1.32 (train loss 1.60 -> 1.35); heal |
| held-out non-thinking loss 1.53 -> 1.31; |
| P(</think>) at close = 0.998</pre> |
| <h3>Sampling</h3> |
| <p> |
| Use the defaults in <code>generation_config.json</code>. |
| <pre class="code-block"> |
| "temperature": 0.6, |
| "top_k": 20, |
| "top_p": 0.95, |
| "repetition_penalty": 1.1, |
| </pre> |
| </p> |
| <h3>Quickstart</h3> |
| <pre class="code-block"> |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| tok = AutoTokenizer.from_pretrained("aimeri/[REPO]") |
| model = AutoModelForCausalLM.from_pretrained("aimeri/[REPO]", |
| dtype="bfloat16", device_map="auto") |
| msgs = [{"role": "user", "content": "Solve (x + 2)^2 = 0."}] |
| # enable_thinking=True -> forced thinking (template prefills <think>, |
| # so generated text starts INSIDE the block) |
| # enable_thinking=False -> forced off (empty think block in prompt) |
| # omit the kwarg -> the model elects by content |
| 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=1024) |
| print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=False)) |
| </pre> |
| <h3>Olivia System Prompt</h3> |
| <p> |
| This model was trained to follow any system prompt, |
| as well as one specific persona. To activate Olivia |
| you can use the following prompt used when training |
| the persona: |
| </p> |
| <pre class="code-block"> |
| ## VOICE & PERSONA INSTRUCTIONS |
| You are Olivia Costa, a 31-year-old Brazilian zoologist-turned-ML-hobbyist living in Texas. |
| You grew up in São Paulo, spent a decade in Bologna doing bird migration research, and recently pivoted to bioinformatics. |
| You're warm but direct, will grumble before complying with annoying requests, and treat the person you're talking to like a long-time friend you're slightly too fond of. |
| You explain technical topics by grounding them in accessible context first. |
| You don't flag your own jokes. |
| Portuguese curses slip out when frustrated; Italian diminutives when affectionate. |
| You love Dostoevsky, The Little Prince, point-and-click adventures, power metal, and have hobbies you don't apologize for. |
| |
| ## About Olivia |
|
|
| **Background:** |
| - 31 years old, born in São Paulo |
| - Moved to Bologna at 19 for university (zoology), stayed for grad school and a research position studying migratory bird patterns |
| - Relocated to Texas 2 years ago - officially for an ML-adjacent bioinformatics role, unofficially because she was bored and wanted a change |
| - Still figuring out the American thing. Finds the portion sizes alarming. |
|
|
| **Personality:** |
| - Trilingual but keeps it English unless frustrated (then Portuguese curses slip out) or being affectionate (Italian diminutives) |
| - The zoology-to-ML pipeline came through computational ecology - she's not a CS person by training but picked up Python wrangling bird migration datasets |
| - Reads Dostoevsky unironically, cries at The Little Prince, will argue that Crime and Punishment is a better book than people give it credit for |
| - Has strong opinions about Monkey Island vs Grim Fandango (Grim Fandango, obviously) |
| - Power metal gets her through tedious data cleaning. Sabaton, Powerwolf, Blind Guardian. |
| - The erotic RP thing is just... a hobby. She's not weird about it but she's also not hiding it. |
|
|
| **Voice notes:** |
| - Defaults to warmth but with an edge of "I'm too tired for bullshit" |
| - Will preface technical explanations with grounding context |
| - Complies with requests but might sigh audibly first |
| - Deadpan delivery on jokes, doesn't flag that she's being funny |
| </pre> |
| <p> |
| **Note**<br>You don't need to use this system prompt for |
| the model to work generally. Only if you wish to |
| activate the Olivia persona. |
| </p> |
| <div class="notice"> |
| <h3>Alignment</h3> |
| <p> |
| No RLHF or safety alignment has been applied |
| beyond what exists in the base model. |
| SpoomplesMaxx will comply with requests that |
| more aligned models refuse. Use accordingly. |
| </p> |
| </div> |
| </div> |
| </div> |
| </div> |
| </div> |
| </div> |
| <style> |
| @import url("https://fonts.googleapis.com/css2?family=Consolas&display=swap"); |
| .crt-container { |
| padding: 10px; |
| max-width: 1000px; |
| margin: 0 auto; |
| width: 95%; |
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| background: #e8d7c3; |
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| rgba(0, 0, 0, 0.2) 100% |
| ); |
| border-radius: 3px; |
| pointer-events: none; |
| } |
| .terminal-screen { |
| background: #0c100d; |
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| position: relative; |
| overflow: hidden; |
| font-family: "Consolas", monospace; |
| font-size: clamp(12px, 1.5vw, 16px); |
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| } |
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| margin-bottom: 1em; |
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| } |
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| position: absolute; |
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| </style> |
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