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build-small-hackathon
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Model Details
Model Provider
build-small-hackathon
Model Tree
openbmb/MiniCPM5-1B
Input Modalities
Output Modalities
Supported Functionality
json
{ "intent": "inspect_prop", "line": "This rubber duck squeaks exactly like a guilty witness.", "emotion": "investigative", "gesture": "leans toward the glowing prop", "stage_effect": "prop_table_glow", "memory_update": "Noted that the rubber duck behaved like evidence.", "tool_request": { "tool": "inspect_prop", "args": {"prop": "rubber duck"}, "reason": "The prop may reveal a stage clue." }}tool_request may be null. Current AI Puppet Theater runtime tool schemas:
inspect_prop: {"prop": "..."}consult_stage_oracle: {"question": "..."}change_lighting: {"mood": "..."}This LoRA adapter is the v0 Actor adapter used for the hackathon demo and downstream GGUF conversion.
On a 40-prompt held-out Actor eval, the LoRA / merged model produced:
| Check | Result |
|---|---|
| Extractable JSON | 35/40, 87.5% |
| Required fields present | 34/40, 85.0% |
| Exact top-level schema | 34/40, 85.0% |
| Sanitized Actor JSON usable | 34/40, 85.0% |
Strict tool_request valid | 34/40, 85.0% |
Sanitized tool_request usable | 35/40, 87.5% |
The later GGUF evaluation improved usability with the final llama.cpp prompt/runtime settings. See the GGUF model card and fine-tuning blog for local inference details.
Runtime should still use JSON extraction, strict validation, sanitized Actor JSON, tool-argument normalization, and deterministic fallback.
text
premise: A moon mayor denies stealing the town's last spoonshow_state JSON: {"story_phase":"complication","latest_prop":"silver spoon",...}actor JSON: {"name":"Mina Moonbutton","goal":"Find the emotional truth...",...}director_instruction: Use the latest prop as evidence and request inspection if useful.Expected assistant response:
json
{"intent":"inspect_prop","line":"This silver spoon squeaks exactly like a guilty witness.","emotion":"investigative","gesture":"leans toward the glowing prop","stage_effect":"prop_table_glow","memory_update":"Noted that the silver spoon behaved like evidence.","tool_request":{"tool":"inspect_prop","args":{"prop":"silver spoon"},"reason":"The prop may reveal one concrete stage clue."}}python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base_model = "openbmb/MiniCPM5-1B"adapter_id = "build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-LoRA" tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)base = AutoModelForCausalLM.from_pretrained( base_model, device_map="auto", trust_remote_code=True,)model = PeftModel.from_pretrained(base, adapter_id)model.eval() messages = [ { "role": "system", "content": ( "You are an Actor agent in AI Puppet Theater. " "Return only one valid JSON object. No markdown. No commentary. " "Keep the puppet line short, theatrical, and speakable." ), }, { "role": "user", "content": ( "premise: ...\n" "show_state JSON: {...}\n" "actor JSON: {...}\n" "director_instruction: ..." ), },] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True,)inputs = tokenizer(prompt, return_tensors="pt").to(model.device)outputs = model.generate( **inputs, max_new_tokens=192, do_sample=False,)print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))This adapter is released under Apache-2.0. The base model openbmb/MiniCPM5-1B is also published under Apache-2.0; users should review the base model license before downstream use.