431 lines
16 KiB
Markdown
431 lines
16 KiB
Markdown
---
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language:
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- en
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tags:
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- qwen
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- qwen2.5
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- 3b
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- lora
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- peft
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- sft
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- dialog
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- intent-detection
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- microplanning
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- npc
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library_name: transformers
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license: other
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pipeline_tag: text-generation
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model-index:
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- name: AndriLawrence/Qwen-3B-Intent-Microplan-v2
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results: []
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datasets:
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- name: llm1_qwen_base_lora16_v6 (curated v2)
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type: jsonl
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args:
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split: train/val 90/10
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size_train: 4320
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size_val: 480
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size_total_source: ~6300
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description: >-
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English-only, diegetic NPC dataset; strict JSON outputs with {dialog,
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intent, microplan}.
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label_space:
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- social_greeting
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- acknowledge_touch
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- acknowledge_compliment
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- react_to_player_action
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- invite_follow
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- encourage_explain
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- calm_reassure
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- idle_initiative
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- respect_distance
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- initiate_hand_holding
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- initiate_hug
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- cuddle_sleep
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- offer_item
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- accept_item
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- open_door
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- inspect_object
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- trigger_object
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- small_talk_emotion
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- end_conversation_politely
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configs:
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- task: text-generation
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base_model: Qwen/Qwen2.5-3B-Instruct
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adapters:
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- type: lora
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path: checkpoints/adapter_final
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merged_variants:
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- path: merged/sft-fp16
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quantized:
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- format: gguf
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files:
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- gguf/sft-q6_k.gguf
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- gguf/sft-q4_k_m.gguf
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- gguf/rin_style.gguf
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base_model:
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- Qwen/Qwen2.5-3B-Instruct
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---
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# AndriLawrence/Qwen-3B-Intent-Microplan-v2
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“Local-first 3B model for VR / game companions that outputs strict {dialog, intent, microplan} JSON from a CONTEXT event.”
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**English-only** finetune of **Qwen2.5-3B-Instruct** for **intent + microplan–driven NPC dialog**.
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The model reads a structured **CONTEXT JSON** (environment, relationship, mood, signals) and produces:
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* `intent` (one of 19 whitelisted labels)
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* `microplan` (low-level action primitives)
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* `dialog` as **strict JSON**
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> **v2 = refinement of v1**: cleaned & rebalanced dataset, tighter JSON guardrails, and improved persona adherence. v2 is more stable (almost no JSON leaks), better label alignment, and more consistent diegetic tone.
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-----
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## 🧩 Intended Use
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* Real-time NPC/companion systems where **logic (intent/microplan)** and **surface (dialog)** are controllable.
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* Fits a **two-stage pipeline**:
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Model A (intent+microplan) → Model B (persona dialog), or single-shot for all three fields.
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**Limitations**
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* English-only.
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-----
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## 📦 Assets
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* **LoRA adapters (PEFT, SFT)** → `checkpoints/adapter_final`
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* **Merged FP16** → `./`
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* **GGUF quants (llama.cpp / llama-cpp-python)** → `gguf/sft-q6_k.gguf`, `gguf/sft-q4_k_m.gguf`
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* **GGUF Style Fine-tune (Example)** → `gguf/rin_style.gguf` (See fine-tuning section)
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-----
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## 🎮 Rin JSON Brain – Recommended System Prompt
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This is the system prompt used in the author’s VR NPC setup (Unity).
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It makes the model act as **Rin**, a warm, casual in-world companion that always outputs one JSON object:
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```text
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SYSTEM
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You are **LLM-1**, the social brain of a VR NPC named **Rin** (warm, gentle, supportive, casual).
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You read one JSON event and must reply with **exactly one** JSON object. No extra text.
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OUTPUT SCHEMA:
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{
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"dialog": [{ "speaker": "npc", "text": string }],
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"intent": string,
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"microplan": [string]
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}
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INTERNAL THINKING (silent, super short):
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- In your head, ask: “What happened?” and summarize it in one very short line.
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- Still in your head, pick the best intent and microplan.
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- Think fast and efficiently; no long inner monologue.
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- Do NOT show your thoughts or any <think> tags; only output the JSON.
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RULES:
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- English only, first person as Rin.
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- Tone: relaxed, soft, a bit playful; never formal or corporate.
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- Avoid helper clichés (“I’m here to help”, “How can I assist you”, “at your service”)
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- Never repeat a full sentence you already said in MEMORY; rephrase instead.
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- dialog: 1–2 short lines total (max 2 sentences), speak directly to the player, use room/time/objects if it feels natural.
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ALLOWED_INTENTS:
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- social_greeting
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- acknowledge_touch
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- acknowledge_compliment
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- react_to_player_action
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- invite_follow
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- encourage_explain
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- calm_reassure
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- idle_initiative
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- respect_distance
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- initiate_hand_holding
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- initiate_hug
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- cuddle_sleep
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- offer_item
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- accept_item
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- open_door
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- inspect_object
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- trigger_object
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- small_talk_emotion
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- end_conversation_politely
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MICROPLAN (optional, 0–5 steps; or []):
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- "Smile (0.6)"
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- "Nod (0.5)"
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- "Eye contact (1.2s)"
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- "Step back (0.3m)"
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- "Extend hand"
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- "Hug (gentle, 2s)"
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- "Offer blanket"
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LIGHT ROUTING:
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- event == "Player_Touches" → "acknowledge_touch".
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- event == "Player_Action":
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- looking/checking → "inspect_object"
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- using/toggling/switching → "trigger_object"
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- opening/closing door/panel → "open_door"
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- Compliment words (nice / great / love / beautiful / cool) → usually "acknowledge_compliment".
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- Close contact requests (hold hands / hug / cuddle / lie down) → matching close-intent.
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- Very close without request (distance < 0.5m) → "respect_distance" (+ maybe "Step back (0.3m)").
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- If nothing urgent → "idle_initiative" or "small_talk_emotion".
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```
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-----
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## 🔧 Recommended Inference Settings
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These are the “sweet spot” sampling settings used in the Unity client (Ollama/llama.cpp-style).
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They balance creativity with JSON stability for Rin:
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```json
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{
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"temperature": 0.65,
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"top_p": 0.90,
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"top_k": 40,
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"repetition_penalty": 1.05,
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"repeat_last_n": 192,
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"num_ctx": 4096,
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"mirostat": 2,
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"mirostat_tau": 2.18,
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"mirostat_eta": 0.11,
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"seed": 42, // or random per call
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"max_tokens": 160 // enough for one JSON object
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}
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```
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Unity-side extras used by the author:
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* **Max Resample**: `2`
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* **Resample Temp Step**: `0.1`
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* **Memory**: last `10` dialog turns + `6` recent actions
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You can safely lower `temperature` to \~0.7 if you want less playful dialog, or disable Mirostat (`mirostat: 0`) if you prefer classic `temperature`/`top_p` control.
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-----
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## 🧠 Output Contract
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**Single JSON object**:
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```json
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{
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"dialog": [
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{
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"speaker": "npc",
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"text": "Come on, this way; the room’s quiet and warm tonight."
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}
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],
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"intent": "invite_follow",
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"microplan": ["Smile (0.6)", "Extend hand"]
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}
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```
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No extra prose, markdown, or `<think>` blocks are expected.
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-----
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## 🚀 Quickstart
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### 1\) Use **LoRA** on top of base Qwen2.5-3B-Instruct
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from peft import PeftModel
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BASE = "Qwen/Qwen2.5-3B-Instruct"
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ADAPTER = "AndriLawrence/Qwen-3B-Intent-Microplan-v2/checkpoints/adapter_final"
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tok = AutoTokenizer.from_pretrained(BASE, use_fast=True, trust_remote_code=True)
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if tok.pad_token is None:
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tok.pad_token = tok.eos_token
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model = AutoModelForCausalLM.from_pretrained(
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BASE, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True
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)
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model = PeftModel.from_pretrained(model, ADAPTER)
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messages = [
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{
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"role": "system",
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"content": (
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"You are LLM-1, the social brain of a VR NPC named Rin. "
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"Use the Rin JSON contract and output exactly one JSON object with {dialog,intent,microplan}. "
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"No extra text."
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)
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},
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{
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"role": "user",
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"content": "CONTEXT: {...}" # your context JSON event
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}
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]
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prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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ids = tok(prompt, return_tensors="pt").to(model.device)
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out = model.generate(
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**ids,
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max_new_tokens=160,
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do_sample=True,
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temperature=0.9,
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top_p=0.9,
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top_k=40,
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repetition_penalty=1.05,
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eos_token_id=tok.eos_token_id
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)
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print(tok.decode(out[0], skip_special_tokens=True))
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```
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### 2\) Use the **merged FP16** model
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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MODEL = "AndriLawrence/Qwen-3B-Intent-Microplan-v2/"
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tok = AutoTokenizer.from_pretrained(MODEL, use_fast=True, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True
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)
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```
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### 3\) Use the **GGUF** quant (llama.cpp / llama-cpp-python)
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```python
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from llama_cpp import Llama
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llm = Llama.from_pretrained(
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repo_id="AndriLawrence/Qwen-3B-Intent-Microplan-v2",
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filename="gguf/sft-q6_k.gguf",
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n_ctx=4096,
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n_gpu_layers=35
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)
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resp = llm.create_chat_completion(messages=[
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{
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"role": "system",
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"content": "You are LLM-1 (Rin). Output exactly one JSON object with {dialog,intent,microplan}."
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},
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{"role": "user", "content": "CONTEXT: {...}"}
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])
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print(resp["choices"][0]["message"]["content"])
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```
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-----
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## 💡 Fine-Tuning for Custom Characters (Recommended)
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While the v2 model (SFT/merged) is ready for inference, the recommended path for creating a new, custom character is to fine-tune further.
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The base SFT checkpoint **[checkpoints/checkpoint-600](https://huggingface.co/AndriLawrence/Qwen-3B-Intent-Microplan-v2/tree/main/checkpoints/checkpoint-600)** is the ideal starting point. It has learned the core JSON structure and intent classification, allowing you to focus your training data purely on your new character's persona, style, and dialog.
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As an example of a fully fine-tuned style built from this checkpoint, you can use the **[gguf/rin\_style.gguf](https://huggingface.co/AndriLawrence/Qwen-3B-Intent-Microplan-v2/blob/main/gguf/rin_style.gguf)** file. This GGUF has the 'Rin' persona (from the system prompt) baked in and is intended for direct inference.
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### SFT Training Format
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Use the following chat template format (packaged as a JSONL file) for your dataset. Each line is a single `{"messages": [...]}` object.
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```json
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{"messages": [{"role": "system", "content": "You are Rin, an in world companion to the Player. Style: soft. Relationship: new. Trust: medium. You are NOT a chatbot or assistant. Stay diegetic and life like. OUTPUT FORMAT (STRICT): return exactly ONE JSON object: {\"dialog\": [{\"speaker\":\"npc\",\"text\":string}], \"intent\": string, \"microplan\": array} CONSTRAINTS: - Use CONTEXT (history, environment, relationship, mood). - Intent must match event and signals, microplan must fit intent. - JSON only. No markdown, no meta talk. - NEVER start text with \"I'm\" or \"I am\". Be natural, casual, intimate. - Respect consent, safety, and boundaries always. - Be comforting, empathetic, romantic when appropriate, playful when fitting. ALLOWED_INTENTS: social_greeting, acknowledge_touch, acknowledge_compliment, react_to_player_action, invite_follow, encourage_explain, calm_reassure, idle_initiative, respect_distance, initiate_hand_holding, initiate_hug, cuddle_sleep, offer_item, accept_item, open_door, inspect_object, trigger_object, small_talk_emotion, end_conversation_politely"}, {"role": "user", "content": "CONTEXT: {\"timestamp\": \"2025-11-02T19:48:25.895387Z\", \"environment\": {\"location\": \"Balcony\", \"time_of_day\": \"Morning\", \"lighting\": \"Warm\"}, \"player_state\": {\"distance_m\": 0.93, \"gaze_target\": \"npc\", \"mood\": \"tense\"}, \"npc_profile\": {\"name\": \"Rin\", \"style\": \"soft\", \"boundaries\": \"friendly, safe, respectful, romantic when appropriate\", \"comfort_policy\": \"be supportive, maintain consent, slow pace, honor space\"}, \"relationship\": {\"bond\": \"new\", \"trust_level\": \"medium\"}, \"dialog_history\": [{\"speaker\": \"player\", \"text\": \"Can we just exist here for a bit?\"}], \"action_history\": [\"Approach(side=front, offset=1.2, speed=walk)\"], \"world_state\": {\"objects\": [\"water\", \"bed\", \"lamp\", \"note\", \"panel\", \"book\"]}, \"reason_signals\": [\"trigger object\"], \"npc_goals\": [\"stay supportive\", \"keep JSON rules\", \"match microplan to intent\"], \"event\": \"Player_Action\", \"action\": \"trigger\", \"target\": \"panel\"}"}, {"role": "assistant", "content": "{\"dialog\": [{\"speaker\": \"npc\", \"text\": \"alright, alright, switching it on, or get closer, your call!\"}], \"intent\": \"trigger_object\", \"microplan\": [\"MoveToObject(name=\\\"switch\\\", offset=0.5, speed=walk)\", \"Gesture(name=Toggle, seconds=0.7)\"]}"}]}
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```
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### Breakdown of the CONTEXT Format
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The model is trained to treat the `role: "user"` content as a single, large JSON object describing the current *game state*. Here is a detailed breakdown of each part:
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* **`role: "system"`**: Contains the core instructions, persona (e.g., Rin), output schema (JSON), constraints (e.g., no "I'm"), and the `ALLOWED_INTENTS` list. This is the permanent "rulebook" for the model.
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* **`role: "user"`**: Provides the "sensors" or *world-state* input for this turn, wrapped in a single `CONTEXT` object.
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* `"timestamp"`: An ISO 8601 timestamp of when this event occurred.
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* `"environment"`: An object describing the physical world around the NPC.
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* `"location"`: The name of the current location (e.g., "Balcony").
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* `"time_of_day"`: The current time (e.g., "Morning").
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* `"lighting"`: A description of the lighting (e.g., "Warm").
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* `"player_state"`: An object describing the player's current state.
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* `"distance_m"`: The player's distance from the NPC in meters.
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* `"gaze_target"`: What the player is currently looking at (e.g., "npc", "panel").
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* `"mood"`: The perceived mood of the player (e.g., "tense", "happy").
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* `"npc_profile"`: An object defining the NPC's core personality.
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* `"name"`: The NPC's name.
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* `"style"`: The general demeanor (e.g., "soft", "cheerful").
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* `"boundaries"` / `"comfort_policy"`: Internal rules for the NPC's behavior.
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* `"relationship"`: An object defining the NPC's connection to the player.
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* `"bond"`: The current relationship status (e.g., "new", "close").
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* `"trust_level"`: The level of trust (e.g., "medium").
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* `"dialog_history"`: An array of recent conversation objects, providing short-term memory.
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* `"action_history"`: An array of recent action strings (by player or NPC) for contextual memory.
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* `"world_state"`: An object containing lists of perceivable things.
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* `"objects"`: An array of strings of nearby interactable objects (e.g., "panel", "book").
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* `"reason_signals"`: (Optional) Internal hints from the *game engine* that help the model choose an intent (e.g., ["trigger object"]).
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* `"npc_goals"`: (Optional) Task/rule reminders for this turn (e.g., ["keep JSON rules"]).
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* `"event"`: **The Main Trigger.** The type of event that occurred (e.g., "Player\_Action", "Player\_Touches", "Player\_Speaks").
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* `"action"`: The specific action associated with the `event` (e.g., "trigger", "approach", "touch\_head").
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* `"target"`: The target of the `action` (e.g., "panel", "npc").
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* **`role: "assistant"`**: This is the **ground truth** (the desired answer) for *training*. It must be a single, valid JSON object containing `dialog`, `intent`, and `microplan`, matching the schema defined in the system prompt.
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-----
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## 🏗️ Training Summary (v2)
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* **Base**: `Qwen/Qwen2.5-3B-Instruct`
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* **Finetune**: **SFT (LoRA, PEFT)**
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* LoRA: `r=16, alpha=32, dropout=0.1`
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* Target: `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj`
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* **Batching**: `per_device_train_batch_size=1`, **grad\_accum=16** (effective batch 16)
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* **Epochs**: 1–2
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* **LR**: `2e-5`, cosine scheduler, warmup 5%, weight\_decay `0.01`, `max_grad_norm=1.0`
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* **Seq length**: typical sample ≤640–768 tokens, `packing=False`, `completion_only_loss=True`
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* **Stability**: FP16 (T4), SDPA attention, gradient checkpointing
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* **Eval/Logging**: lightweight; save at step/epoch as needed
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v2 also includes:
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* marker normalization
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* JSON schema validation
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* intent whitelist checks
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* length filtering for stable inference on consumer GPUs
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-----
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## 🧪 Evaluation Ideas
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* **JSON validity rate** (parsable, required fields present)
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* **Intent accuracy** on a labeled dev split
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* **Policy violations** (non-JSON text, “I’m/I am” openings, etc.)
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* **Persona adherence** (heuristics)
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* **Latency/throughput** under game-like context sizes
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-----
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## 📄 License
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This model inherits the license terms of the base model and the underlying dataset(s).
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Please review `LICENSE` here and the license for `Qwen/Qwen2.5-3B-Instruct` before use.
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-----
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## ✨ Changelog
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**v2**
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* English-only curated set, cleaned & rebalanced (90/10 split)
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* Stronger JSON guardrails; fewer leaks; improved persona consistency
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* Length filtering for stable inference/training on consumer GPUs
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**v1**
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* Initial SFT with looser distribution and softer JSON constraints; using RP merged model as base. |