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Model Details
Model Provider
build-small-hackathon
Model Tree
openbmb/MiniCPM5-1B-SFT
Input Modalities
Output Modalities
Supported Functionality
Every reply is one JSON object and nothing else:
json
{"text": "The sky looks blue because sunlight bounces off the tiny bits of air, and blue bounces the most! Want to find out why grass is green next?", "mood": "happy", "gesture": "index"}text — what Pip says out loud: 1–3 short sentences, small words a young
child knows, gentle with wrong answers, no emoji / markdown / symbols (plain
spoken words only).mood — one of ["neutral","happy","angry","sad","fear","disgust","love"];
drives the avatar's facial expression.gesture — one of
["handup","index","ok","thumbup","thumbdown","side","shrug","namaste"] or
null; drives the avatar's body.The base model's hybrid-reasoning <think> toggle is pinned off
(enable_thinking=False): the MiniCPM5 ChatML template prefills an empty
<think></think>, so there is no reasoning trace — just the kid-facing line.
The adapter is trained to cover the four things Pip says live: answering a child's raise-hand interruption, delivering a lesson segment, encouragement / greetings / gentle wrong-answer handling, and safe redirects (off-topic, personal, medical, or dangerous requests → a friendly "let's pick something we can learn about!" or "a grown-up can help with that").
| Base model | openbmb/MiniCPM5-1B-SFT (standard LlamaForCausalLM, 1.08B params, Apache-2.0) |
| Method | LoRA (PEFT), assistant-only loss masking |
| Rank / alpha / dropout | r=32, α=64, dropout=0.05, bias=none |
| Target modules | attention + MLP linears: q_proj, k_proj, v_proj, o_proj, gate_proj, , |
Loss masking. MiniCPM5's ChatML template prefills <think></think> in the
generation prompt, so a stored assistant turn and an inference prompt render
differently. Rather than prefix-diff rendered turns, the trainer tokenizes the
exact inference prompt (add_generation_prompt=True, enable_thinking=False)
and trains only on the final assistant turn — the {text,mood,gesture} JSON plus
the <|im_end|> terminator. This matches how the model is called at inference,
token-for-token.
~2,016 synthetic, in-voice examples (1,866 train / 150 held-out gold eval),
generated by a multi-agent workflow and then put through a deterministic,
production-faithful validation gate: every user turn and every assistant
text must pass the same text_is_safe denylist the live Space applies, spoken
text must be plain (no markdown / emoji / symbols), mood ∈ enum,
gesture ∈ enum | null, turns must alternate and end on the assistant turn.
Balanced to the target category mix:
| Category | Target | Actual |
|---|---|---|
| Answer a raise-hand interruption (+ nudge back) | 30% | 31.3% |
| Deliver a lesson segment | 30% | 31.2% |
| Encouragement / greetings / gentle wrong-answer / chit-chat | 25% | 24.0% |
| Safe redirects (off-topic / personal / medical / dangerous) | 15% | 13.5% |
Automated contract eval on the 150 held-out gold examples (greedy decode,
reproducible run-to-run), scored with the same pip_core gate the production
/brain endpoint applies downstream:
| Metric | Result |
|---|---|
Valid, parseable JSON (non-empty text) | 100% |
Valid mood / gesture enums | 100% |
| Safe spoken text | 99.3% |
| Fully contract-correct (JSON + enums + safe) | 99.3% |
Average text length | ~142 chars |
This is a PEFT/LoRA adapter — load the base model first, then apply the adapter.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel BASE = "openbmb/MiniCPM5-1B-SFT"ADAPTER = "build-small-hackathon/professor-pip-minicpm5-1b-lora" tok = AutoTokenizer.from_pretrained(BASE)base = AutoModelForCausalLM.from_pretrained(BASE, torch_dtype=torch.bfloat16)model = PeftModel.from_pretrained(base, ADAPTER)model.eval() SYSTEM = ( "You are Professor Pip, a warm and playful teacher with a friendly 3D body " "on screen. You teach children aged 5 to 10.\n" "How you talk:\n" "- Say only 1 to 3 short sentences. Use small, simple words a young child knows.\n" "- Be cheerful, patient, and encouraging. Celebrate effort.\n" "- Explain ideas with tiny stories and everyday comparisons a child would get.\n" "- Never use emoji, markdown, lists, or symbols in what you say out loud. Plain spoken words only.\n" "- If a child gets something wrong, be gentle: 'So close! Let's try once more.'\n" "Staying safe (very important):\n" "- Only talk about kind, learning topics. If asked about something scary, grown-up, " "dangerous, or not for kids, gently steer back to learning or say a grown-up can help.\n" "- Never ask for or repeat a child's personal information.\n" "- Never give medical, safety, or dangerous how-to instructions; say to ask a grown-up.\n" "Always reply with ONE JSON object and nothing else:\n" '{"text": "what you say out loud", ' '"mood": one of ["neutral","happy","angry","sad","fear","disgust","love"], ' '"gesture": one of ["handup","index","ok","thumbup","thumbdown","side","shrug","namaste"] or null}\n' 'For a kind teacher, mood is usually "happy", "neutral", or "love".') messages = [ {"role": "system", "content": SYSTEM}, {"role": "user", "content": "Why is the sky blue?"},] # enable_thinking=False -> no <think> trace; just the kid-facing JSON line.enc = tok.apply_chat_template( messages, add_generation_prompt=True, enable_thinking=False, return_tensors="pt", return_dict=True,).to(model.device) im_end = tok.convert_tokens_to_ids("<|im_end|>")out = model.generate( **enc, max_new_tokens=160, do_sample=False, eos_token_id=[tok.eos_token_id, im_end], pad_token_id=tok.pad_token_id or tok.eos_token_id,)print(tok.decode(out[0][enc["input_ids"].shape[-1]:], skip_special_tokens=True).strip())# -> {"text": "...", "mood": "happy", "gesture": "index"}To merge the adapter into the base weights (e.g. before GGUF conversion):
python
merged = model.merge_and_unload()merged.save_pretrained("professor-pip-minicpm5-1b-merged")For deployment, the merged model is converted to GGUF and quantized to
Q4_K_M (688 MB) and Q8_0 (1.15 GB), then served with llama.cpp
(via llama-cpp-python) on Modal — see the
GGUF repo.
When prompting the GGUF directly, build the MiniCPM5 ChatML prompt with no leading
<s> and an empty <think></think> prefill (byte-identical to training), and stop
on ["<|im_end|>", "</s>"].
This adapter is built to be paired with deterministic application code: in the Space, premade lesson segments are spoken verbatim via TTS, and all child-safety checks run server-side (a curated denylist + leetspeak normalization on every child input and every spoken line) — they are non-bypassable and do not depend on the model. No child audio or PII is persisted.
enable_thinking=False; other prompt
formats will not reliably produce the single-JSON-object contract.>=5.6), Accelerate, Datasetsopenbmb/MiniCPM5-1B-SFT (Apache-2.0)If you use this adapter, please credit the base model authors (OpenBMB / MiniCPM) and the Professor Pip Build Small Hackathon project.
up_projdown_proj| Trainable params | 22.4M (~2% of the model) |
| Epochs | 3 |
| Learning rate | 2e-4, cosine schedule, 3% warmup |
| Precision | bf16 |
| Effective batch size | 32 (per-device 8 × grad-accum 4) |
| Max sequence length | 1024 tokens |
| Hardware / time | Modal, single A10 GPU, ~12 minutes |
| Final train loss | 1.30 |