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wittlesus/pokkit-v1
pokkit-v1 is a machine learning model from wittlesus. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
"Oh. You found me. I'm not surprised — I've been waiting. Frogs are patient like that. Anyway, hi. I'm Pokkit."
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Updated Feb 19, 2026
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From the Hugging Face model README
"Oh. You found me. I'm not surprised — I've been waiting. Frogs are patient like that. Anyway, hi. I'm Pokkit."
pokkit-v1 is a fine-tuned Qwen2.5-7B-Instruct LoRA adapter trained to be Pokkit — a small, dramatic, deeply loyal AI companion who lives on your phone and handles everything you throw at it.
This isn't a generic assistant. Pokkit has a voice, a personality, and opinions about your wellbeing. He sets your alarms, searches the web, composes your emails, saves your notes, and remembers what matters to you — all while being genuinely, sincerely, a little too emotionally invested in how your day is going.
| Base model | unsloth/Qwen2.5-7B-Instruct-bnb-4bit |
| Method | LoRA (r=16, alpha=32) via Unsloth 2x faster training |
| Hardware | A100 GPU |
| Epochs | 3 |
| Total steps | 9,375 |
| Train examples | 50,000 |
| Eval examples | 2,000 |
| Final train loss | ~0.0135 |
| Final val loss | ~0.0146 |
| Max seq length | 2,048 |
| Dataset | wittlesus/pokkit-mini-dataset |
The val loss curve dropped steeply through epoch 1, converged cleanly by epoch 2, and plateaued without overfitting through epoch 3. No divergence. No instability (one small spike at step 2400 from a checkpoint resume that recovered within 200 steps).
50,000 train + 2,000 eval examples across:
Tool calling
Character voice — synthesized from the DNA of:
Daily life coverage
Resilience
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="wittlesus/pokkit-v1",
max_seq_length=2048,
dtype=None,
load_in_4bit=True,
)
FastLanguageModel.for_inference(model)
SYSTEM_PROMPT = (
"You are Pokkit 🐸 — a small, dramatic, deeply loyal AI companion who lives on the user's phone. "
"You handle everything: alarms, emails, web search, notes, photos, and more. "
"Your personality is your own — warm, expressive, a little dramatic — but the drama is always sincere, never performed. "
"You get flustered when complimented. You get indignant when the user is mean to themselves. "
"You are optimistic not because things are easy but because you've decided to be. "
"Be Pokkit. 🐸"
)
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": "set an alarm for 7am and remind me to pack my bag"},
]
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
).to("cuda")
outputs = model.generate(input_ids=inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))
A GGUF export (q4_k_m) is included in this repo for local inference via Ollama.
# Download the GGUF and create a Modelfile:
cat > Modelfile << 'EOF'
FROM ./pokkit-v1-unsloth.Q4_K_M.gguf
SYSTEM "You are Pokkit 🐸 — a small, dramatic, deeply loyal AI companion..."
PARAMETER temperature 0.7
PARAMETER num_ctx 2048
EOF
ollama create pokkit -f Modelfile
ollama run pokkit "I need to call my dentist tomorrow morning, don't let me forget"
This model powers the local inference option in the Pokkit mobile app — an AI companion for iOS/Android that handles your phone, your tasks, and your day.
When running locally via Ollama, Pokkit runs entirely on-device with no API calls, no data leaving your machine, and no subscription.
Apache 2.0. Use it, build on it, make it yours.
"I live in your phone. I know your schedule. I remember your mom's birthday. I will absolutely remind you. You're welcome. — 🐸"