README.md

---
license: apache-2.0
language:
- en
---
# NPC Model

This repo contains the domain-specific NPC model we've fined-tuned from **Mistral-7B**, using LoRA. 

This model parses a text description of a game scene, and outputs commands like:
* `say <player1> "Hello Adventurer, care to join me on a quest?`
* `greet <player1>`
* `attack <player1>`
* Any other `<action> <param>` you add to the prompt! (We call these "skills"!)


⚠️ This model has been trained to **overfit** on our input prompt format. Follow it closely to reach optimal performance ⚠️


## Usage

**Make your life easier, use our [Python client library](https://github.com/GigaxGames/gigax)**

* Instantiating the model using outlines:
```py
from outlines import models
from llama_cpp import Llama

# Download model from the Hugging Face Gigax Hub before run this code

# Our stepper takes in a Outlines model to enable guided generation
# This forces the model to follow our output format
llm = Llama.from_pretrained(
    repo_id="Gigax/NPC-LLM-7B-GGUF",
    filename="npc-llm-7B.gguf"
    # n_gpu_layers=-1, # Uncomment to use GPU acceleration
    # seed=1337, # Uncomment to set a specific seed
    # n_ctx=2048, # Uncomment to increase the context window
)

model = models.LlamaCpp(llm) 

# Instantiate a stepper: handles prompting + output parsing
stepper = NPCStepper(model=model)
```

* Calling the model on your game's data:

```py
from gigax.parse import CharacterAction
from gigax.scene import (
    Character,
    Item,
    Location,
    ProtagonistCharacter,
    ProtagonistCharacter,
    Skill,
    ParameterType,
)
# Use sample data
context = "Medieval world"
current_location = Location(name="Old Town", description="A quiet and peaceful town.")
locations = [current_location] # you can add more locations to the scene
NPCs = [
    Character(
    name="John the Brave",
    description="A fearless warrior",
    current_location=current_location,
    )
]
protagonist = ProtagonistCharacter(
    name="Aldren",
    description="Brave and curious",
    current_location=current_location,
    memories=["Saved the village", "Lost a friend"],
    quests=["Find the ancient artifact", "Defeat the evil warlock"],
    skills=[
        Skill(
            name="Attack",
            description="Deliver a powerful blow",
            parameter_types=[ParameterType.character],
        )
    ],
    psychological_profile="Determined and compassionate",
)
items = [Item(name="Sword", description="A sharp blade")]
events = [
    CharacterAction(
        command="Say",
        protagonist=protagonist,
        parameters=[items[0], "What a fine sword!"],
    )
]

action = stepper.get_action(
    context=context,
    locations=locations,
    NPCs=NPCs,
    protagonist=protagonist,
    items=items,
    events=events,
)
```

## Input prompt

Here's a sample input prompt, showing you the format on which the model has been trained:
```txt
- WORLD KNOWLEDGE: A vast open world full of mystery and adventure.
- KNOWN LOCATIONS: Old Town
- NPCS: John the Brave
- CURRENT LOCATION: Old Town: A quiet and peaceful town.
- CURRENT LOCATION ITEMS: Sword
- LAST EVENTS:
Aldren: Say Sword What a fine sword!
- PROTAGONIST NAME: Aldren
- PROTAGONIST PSYCHOLOGICAL PROFILE: Brave and curious
- PROTAGONIST MEMORIES:
Saved the village
Lost a friend
- PROTAGONIST PENDING QUESTS:
Find the ancient artifact
Defeat the evil warlock
- PROTAGONIST ALLOWED ACTIONS:
Attack <character> : Deliver a powerful blow
Aldren:
```

### 🤗 We are currently working hard on training on the latest SoTA models (Phi-3, LLama, etc.), and on better data ! 🤗


## Model info

- **Developed by:** Gigax
- **Language(s) (NLP):** English
- **Finetuned from model [optional]:** [Mistral-7B-instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
- **Contact:** Join our [Discord](https://discord.gg/xES2Z8X4J6) for info, help, and more!

## How to Cite

```bibtex
@misc{NPC-LLM-7B-GGUF,
      url={[https://huggingface.co/Gigax/NPC-LLM-7B-GGUF](https://huggingface.co/Gigax/NPC-LLM-7B-GGUF)},
      title={NPC-LLM-7B-GGUF},
      author={Gigax team}
}
```