NPC-LLM-7B-imatrix-GGUF

Quantizations of https://huggingface.co/Gigax/NPC-LLM-7B

Inference Clients/UIs

• llama.cpp
• KoboldCPP
• ollama
• text-generation-webui
• jan
• GPT4All

---

From original readme

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

• Instantiating the model using outlines:

from outlines import models
from gigax.step import NPCStepper

# Download model from the Hub
model_name = "Gigax/NPC-LLM-7B"
llm = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Our stepper takes in a Outlines model to enable guided generation
# This forces the model to follow our output format
model = models.Transformers(llm, tokenizer)

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

• Calling the model on your game's data:

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:

- 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: