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README.md
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---
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license: mit
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---
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---
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license: mit
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language:
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- en
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---
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# NPC Model
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This repo contains the domain-specific NPC model we've fined-tuned from **Phi-3**, using LoRA.
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This model parses a text description of a game scene, and outputs commands like:
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* `say <player1> "Hello Adventurer, care to join me on a quest?`
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* `greet <player1>`
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* `attack <player1>`
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* Any other `<action> <param>` you add to the prompt! (We call these "skills"!)
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⚠️ This model has been trained to **overfit** on our input prompt format. Follow it closely to reach optimal performance ⚠️
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## Usage
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**Make your life easier, use our [Python client library](https://github.com/GigaxGames/gigax)**
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* Instantiating the model using outlines:
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```py
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from outlines import models
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from gigax.step import NPCStepper
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# Download model from the Hub
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model_name = "Gigax/NPC-LLM-7B"
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llm = AutoModelForCausalLM.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Our stepper takes in a Outlines model to enable guided generation
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# This forces the model to follow our output format
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model = models.Transformers(llm, tokenizer)
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# Instantiate a stepper: handles prompting + output parsing
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stepper = NPCStepper(model=model)
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```
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* Calling the model on your game's data:
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```py
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from gigax.parse import CharacterAction
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from gigax.scene import (
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Character,
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Item,
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Location,
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ProtagonistCharacter,
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ProtagonistCharacter,
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Skill,
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ParameterType,
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)
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# Use sample data
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current_location = Location(name="Old Town", description="A quiet and peaceful town.")
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NPCs = [
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Character(
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name="John the Brave",
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description="A fearless warrior",
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current_location=current_location,
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)
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]
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protagonist = ProtagonistCharacter(
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name="Aldren",
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description="Brave and curious",
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current_location=current_location,
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memories=["Saved the village", "Lost a friend"],
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quests=["Find the ancient artifact", "Defeat the evil warlock"],
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skills=[
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Skill(
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name="Attack",
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description="Deliver a powerful blow",
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parameter_types=[ParameterType.character],
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)
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],
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psychological_profile="Determined and compassionate",
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)
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items = [Item(name="Sword", description="A sharp blade")]
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events = [
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CharacterAction(
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command="Say",
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protagonist=protagonist,
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parameters=[items[0], "What a fine sword!"],
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)
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]
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action = stepper.get_action(
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context=context,
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locations=locations,
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NPCs=NPCs,
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protagonist=protagonist,
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items=items,
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events=events,
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)
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```
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## Input prompt
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Here's a sample input prompt, showing you the format on which the model has been trained:
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```txt
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- WORLD KNOWLEDGE: A vast open world full of mystery and adventure.
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- KNOWN LOCATIONS: Old Town
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- NPCS: John the Brave
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- CURRENT LOCATION: Old Town: A quiet and peaceful town.
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- CURRENT LOCATION ITEMS: Sword
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- LAST EVENTS:
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Aldren: Say Sword What a fine sword!
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- PROTAGONIST NAME: Aldren
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- PROTAGONIST PSYCHOLOGICAL PROFILE: Brave and curious
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- PROTAGONIST MEMORIES:
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Saved the village
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Lost a friend
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- PROTAGONIST PENDING QUESTS:
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Find the ancient artifact
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Defeat the evil warlock
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- PROTAGONIST ALLOWED ACTIONS:
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Attack <character> : Deliver a powerful blow
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Aldren:
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```
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### 🤗 We are currently working hard on training on the latest SoTA models (Phi-3, LLama, etc.), and on better data ! 🤗
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## Model info
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- **Developed by:** Gigax
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- **Language(s) (NLP):** English
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- **Finetuned from model [optional]:** [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
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- **Contact:** Join our [Discord](https://discord.gg/xES2Z8X4J6) for info, help, and more!
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## How to Cite
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```bibtex
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@misc{NPC-LLM-3_8B,
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url={[https://huggingface.co/Gigax/NPC-LLM-7B](https://huggingface.co/Gigax/NPC-LLM-3_8B)},
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title={NPC-LLM-3_8B},
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author={Gigax team}
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}
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```
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