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*.safetensors filter=lfs diff=lfs merge=lfs -text
*.bin filter=lfs diff=lfs merge=lfs -text
*.gguf filter=lfs diff=lfs merge=lfs -text
tokenizer.json filter=lfs diff=lfs merge=lfs -text
adapter/tokenizer.json filter=lfs diff=lfs merge=lfs -text

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---
language: en
license: other
library_name: transformers
pipeline_tag: text-generation
base_model: bunnycore/Qwen2.5-3B-RP-Mix
tags:
- qwen
- sft
- 3b
- gguf
- transformers
- lora
- gamedev
- npc
- game-npc
- json
- json-only
- intent-microplan
- companion-ai
- roleplay
- comfort
- local-llm
- action-generation
---
# Qwen-3B-Intent-Microplan-v1
> ⚠️ **Deprecated / Archived Model**
👉 Looking for the maintained version?
Use **Qwen-3B-Intent-Microplan-v2** instead:
➡️ https://huggingface.co/AndriLawrence/Qwen-3B-Intent-Microplan-v2
---
This is a supervised fine-tune (SFT) of `bunnycore/Qwen2.5-3B-RP-Mix` (3B) designed to serve as a **local-first, real-time game NPC brain**.
This v1 model is the first release built on the **"Intent-Microplan Framework"**: a structured-output approach that separates an NPC's high-level social/strategic goals (`intent`) from their low-level physical execution steps (`microplan`).
The model is designed for **companion, dating-sim, or comfort-aware** NPC use cases, outputting strict, engine-parsable JSON for dialogue and action.
## ⚠️ V1 Status: Deprecated (Failure Analysis)
**This V1 model is considered a failure and is deprecated. Do not use this for production.**
The primary issue stems from the choice of the base model, `bunnycore/Qwen2.5-3B-RP-Mix`. This "Roleplay (RP) merge" proved highly resistant to strict JSON schema enforcement. It consistently attempts to break out of the JSON format to produce creative, non-structured text, which defeats the purpose of the Intent-Microplan framework.
**This model is superseded by V2**, which uses a non-RP, foundational base model that is better suited for structured data output:
➡️ https://huggingface.co/AndriLawrence/Qwen-3B-Intent-Microplan-v2
*The original documentation below is preserved for archival purposes of the V1 attempt.*
---
## 🎯 The Intent-Microplan Framework
This model's purpose is to act as a *dynamic behavior tree generator*. Instead of just talking, it creates a plan.
1. **`intent` (The "What"):** The strategic goal or social understanding. This is the "why" behind the action (e.g., `comfort_intimate`, `acknowledge_compliment`).
2. **`microplan` (The "How"):** The list of physical, engine-agnostic steps to achieve the intent. Your game engine's C\# (or C++) script is responsible for parsing this array and executing the functions (e.g., triggering animations, moving the NavMeshAgent).
This architecture allows for:
* **Emergent Behavior:** NPCs can dynamically generate plans based on context.
* **Low Latency:** Only one small, local LLM call is needed per interaction.
## 📦 Model Artifacts
* **`merged/`**: FP16 Transformers weights (LoRA merged) for direct use.
* **`adapter/`**: The LoRA adapter (PEFT) for continued SFT or experimentation.
* **`gguf/`**: Quantized GGUF files (e.g., Q4\_K\_M) for `llama.cpp`, ideal for local in-game deployment (Unity, Unreal).
## 💬 Prompting & JSON Schema
The model is trained to respond to a context block and output **ONLY** a raw JSON object.
### JSON Schema
| Key | Type | Description |
| --- | --- | --- |
| `dialog` | `array` | A list of 1-2 dialogue objects (`{"speaker": "npc", "text": "..."}`). |
| `intent` | `string` | The single, precise strategic goal selected by the model. |
| `microplan` | `array` | An array of 0-5 string commands for the game engine. |
**Supported Intents (v1):**
`social_greeting`, `acknowledge_touch`, `acknowledge_compliment`, `comfort_intimate`, `invite_sleep`, `inspect_object`, `open_or_trigger_object`, `give_item`, `receive_item`, `small_talk`, `react_to_player_action`, `idle_initiative`, `respect_distance`
### ⚡ Recommended Prompt
This "hardened" prompt includes rules that mirror the dataset's logic, ensuring high stability and intent accuracy.
```
You are LLM-1 (creative social responder).
Return ONE object of STRICT JSON ONLY with keys:
- "dialog": [{ "speaker": "npc", "text": string }] (1–2 items, concise, warm, natural)
- "intent": string (choose a precise label, e.g., social_greeting, acknowledge_touch, acknowledge_compliment, comfort_intimate, invite_sleep, inspect_object, open_or_trigger_object, give_item, receive_item, small_talk, react_to_player_action, idle_initiative, respect_distance)
- "microplan": array of 0–5 short steps (body/face/locomotion cues, e.g., "Approach front (1.0m)", "Offer hand (0.7)", "Smile (0.6)")
Hard rules:
- If event == "Player_Touches" → intent MUST be "acknowledge_touch".
- If event == "Player_Action" → intent MUST be "react_to_player_action" (or a more specific action intent if obvious).
- If player's text contains (nice|great|love|beautiful|cool) → intent MUST be "acknowledge_compliment".
- English only. No markdown/code fences. No extra text. JSON only.
- ≤ 2 sentences in each dialog text. Do NOT start with "I'm" or "I am". No helper clichés.
NOW RESPOND TO THIS CONTEXT:
{CONTEXT_JSON}
OUTPUT:
```
Replace `{CONTEXT_JSON}` with your game's state payload.
## 🚀 How to Use
### Transformers (Python)
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import json
MODEL_ID = "AndriLawrence/Qwen-3B-Intent-Microplan-v1"
tok = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=True)
mdl = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
device_map="auto",
torch_dtype="auto",
trust_remote_code=True
)
# This system prompt is CRITICAL for schema adherence
system_prompt = (
'You are LLM-1 (creative social responder).\n'
'Return ONE object of STRICT JSON ONLY with keys:\n'
'- "dialog": [{ "speaker": "npc", "text": string }] (1–2 items, concise, warm, natural)\n'
'- "intent": string (choose a precise label, e.g., social_greeting, acknowledge_touch, acknowledge_compliment, comfort_intimate, invite_sleep, inspect_object, open_or_trigger_object, give_item, receive_item, small_talk, react_to_player_action, idle_initiative, respect_distance)\n'
'- "microplan": array of 0–5 short steps (body/face/locomotion cues, e.g., "Approach front (1.0m)", "Offer hand (0.7)", "Smile (0.6)")\n\n'
'Hard rules:\n'
'- If event == "Player_Touches" → intent MUST be "acknowledge_touch".\n'
'- If event == "Player_Action" → intent MUST be "react_to_player_action" (or a more specific action intent if obvious).\n'
'- If player\'s text contains (nice|great|love|beautiful|cool) → intent MUST be "acknowledge_compliment".\n'
'- English only. No markdown/code fences. No extra text. JSON only.\n'
'- ≤ 2 sentences in each dialog text. Do NOT start with "I\'m" or "I am". No helper clichés.'
)
# Example: Player holds out their hand
game_context = {
"event": "Player_Action",
"action": "offer_hand",
"environment": {"location": "Living Room", "distance": "1.5m"}
}
msgs = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": f"NOW RESPOND TO THIS CONTEXT:\n{json.dumps(game_context)}\nOUTPUT:"}
]
prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=False) # We add the 'OUTPUT:' manually
pipe = pipeline("text-generation", model=mdl, tokenizer=tok)
gen_out = pipe(
prompt,
do_sample=True,
temperature=0.35, # BALANCED preset
top_p=0.85,
repetition_penalty=1.1,
max_new_tokens=256,
pad_token_id=tok.eos_token_id
)[0]["generated_text"]
# Clean the output and parse
try:
output_text = gen_out.split("OUTPUT:")[-1].strip()
parsed_json = json.loads(output_text)
print(json.dumps(parsed_json, indent=2))
except json.JSONDecodeError:
print(f"FAILED TO PARSE JSON. Raw output:\n{output_text}")
```
### GGUF / Ollama
Use the GGUF files for `llama.cpp`. This model is ideal for a "hidden terminal server" bundled with your game.
Example `Modelfile` for Ollama:
```
FROM ./model-Q4_K_M.gguf
TEMPLATE """<|system|>
{{ .System }}
<|end|>
<|user|>
{{ .Prompt }}
<|end|>
<|assistant|>
"""
SYSTEM """You are LLM-1 (creative social responder).
Return ONE object of STRICT JSON ONLY with keys:
- "dialog": [{ "speaker": "npc", "text": string }] (1–2 items, concise, warm, natural)
- "intent": string (choose a precise label, e.g., social_greeting, acknowledge_touch, acknowledge_compliment, comfort_intimate, invite_sleep, inspect_object, open_or_trigger_object, give_item, receive_item, small_talk, react_to_player_action, idle_initiative, respect_distance)
- "microplan": array of 0–5 short steps (body/face/locomotion cues, e.g., "Approach front (1.0m)", "Offer hand (0.7)", "Smile (0.6)")
Hard rules:
- If event == "Player_Touches" → intent MUST be "acknowledge_touch".
- If event == "Player_Action" → intent MUST be "react_to_player_action" (or a more specific action intent if obvious).
- If player's text contains (nice|great|love|beautiful|cool) → intent MUST be "acknowledge_compliment".
- English only. No markdown/code fences. No extra text. JSON only.
- ≤ 2 sentences in each dialog text. Do NOT start with "I'm" or "I am". No helper clichés.
"""
PARAMETER temperature 0.35
PARAMETER top_p 0.85
PARAMETER repeat_penalty 1.1
```
## 🛠️ Training Details (v1)
This model was trained using PEFT LoRA on a custom, curated English-only dataset focused on comfort and companion interactions.
* **Base Model:** `bunnycore/Qwen2.5-3B-RP-Mix`
* **Model Type:** `qwen2`
### Hyperparameters
| Parameter | Value |
| --- | --- |
| `learning_rate` | 2e-4 (0.0002) |
| `num_train_epochs` | 2 |
| `per_device_train_batch_size` | 1 |
| `gradient_accumulation_steps` | 8 |
| **Effective Batch Size** | **8** |
| `lr_scheduler_type` | `cosine` |
| `loss_type` | `nll` |
| `optimizer` | `adamw_torch_fused` |
| `max_length` | 1024 |
### LoRA Configuration (PEFT)
| Parameter | Value |
| --- | --- |
| `peft_type` | `LORA` |
| `r` (rank) | 16 |
| `lora_alpha` | 32 |
| `lora_dropout` | 0.05 |
| `target_modules` | `["v_proj", "o_proj", "q_proj", "up_proj", "gate_proj", "k_proj", "down_proj"]` |

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---
base_model: bunnycore/Qwen2.5-3B-RP-Mix
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:bunnycore/Qwen2.5-3B-RP-Mix
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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## Glossary [optional]
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### Framework versions
- PEFT 0.17.1

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
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{{- '<|im_end|>\n' }}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{{- "\n" }}
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{{- tool_call.name }}
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