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build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face
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AI Puppet Theater MiniCPM5 Actor GGUF

Q4KM GGUF export of the AI Puppet Theater Actor model.

This model was fine-tuned from openbmb/MiniCPM5-1B using LoRA/QLoRA Actor SFT, merged back into the base model, converted to GGUF, and quantized for llama.cpp.

It is designed to generate one short Actor JSON object for AI Puppet Theater. It is not a general assistant model.

For the training, merge, GGUF conversion, and evaluation story, see the fine-tuning blog linked below.

Intended Use

Generate one Actor response for a single AI Puppet Theater beat. The runtime should pass a premise, show state JSON, actor JSON, and director instruction, then extract and validate the returned JSON before using it.

Expected top-level schema:

json
{
  "intent": "react_to_event",
  "line": "A short puppet line tied to the scene.",
  "emotion": "curious",
  "gesture": "tilts head toward the stage lights",
  "stage_effect": "spotlight_glow",
  "memory_update": null,
  "tool_request": null
}

tool_request may be null or an object using one of the AI Puppet Theater tool schemas:

  • —inspect_prop: {"prop": "..."}
  • —consult_stage_oracle: {"question": "..."}
  • —change_lighting: {"mood": "..."}

Recommended llama.cpp Usage

Use llama-completion, not interactive llama-cli, for one-shot Actor JSON generation. For the local build used during evaluation, -no-cnv was required to avoid conversation mode.

Recommended settings:

  • —binary: llama-completion
  • —mode: -no-cnv
  • —reasoning: --reasoning off --reasoning-budget 0
  • —prompt format: chatml
  • —temperature: 0

Example:

bash
llama-completion \
  -m minicpm5-actor-q4_k_m.gguf \
  -no-cnv \
  -n 160 \
  --temp 0 \
  --reasoning off \
  --reasoning-budget 0 \
  -p '<|im_start|>system
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.
<|im_end|>
<|im_start|>user
premise: A moon mayor denies stealing the town last spoon
show_state JSON: {"story_phase":"complication","latest_prop":"silver spoon","finale_requested":false}
actor JSON: {"name":"Mina Moonbutton","mood":"curious","tools":["inspect_prop"]}
director_instruction: Inspect the latest prop and keep the line short.

Return exactly one JSON object with exactly these keys: intent, line, emotion, gesture, stage_effect, memory_update, tool_request. Do not omit stage_effect.
<|im_end|>
<|im_start|>assistant
'

GGUF Eval Summary

Full GGUF eval used llama-completion with -no-cnv, reasoning disabled, and chatml prompt formatting.

CheckResult
Total generations40
Extracted JSON parse39/40, 97.5%
Required fields present39/40, 97.5%
Exact top-level schema39/40, 97.5%
Sanitized Actor JSON usable39/40, 97.5%
Strict tool_request valid35/40, 87.5%
Sanitized tool_request usable39/40, 97.5%
Line length pass39/40, 97.5%
Interactive marker seen0/40, 0.0%
Runtime error0/40, 0.0%
Timeout0/40, 0.0%
[Start thinking] seen0/40, 0.0%

Runtime Notes

Use first-balanced-JSON extraction, Actor JSON schema validation, tool request sanitization, and deterministic fallback in the application runtime. This is important because local LLM outputs can include extra text, malformed tool arguments, or occasional missing fields.

Caveats

  • —Demo/hackathon fine-tune.
  • —Synthetic Actor SFT data; not broad creative-writing training.
  • —Actor-only behavior; Director planning is handled by the app.
  • —Not a general chat or instruction model.
  • —Validate all tool calls before execution.
  • —Best results depend on the documented llama.cpp prompt/runtime settings.

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