How to use from
Unsloth Studio
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for chimbiwide/Gemma4NPC-E2B to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for chimbiwide/Gemma4NPC-E2B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for chimbiwide/Gemma4NPC-E2B to start chatting
Load model with FastModel
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="chimbiwide/Gemma4NPC-E2B",
    max_seq_length=2048,
)
Quick Links

Gemma4NPC-E2B

The newest GemmaNPC models, with the new Gemma4-E2B model as the base model, trained using the newest RolePlay-NPC-Quest dataset.


Intended Usage

This model is trained to be used as a more game focused NPC rolaplaying model, it is not abliterated.

Training Parameters

For this model, we employed a slightly less conservative parameter, which resulted in some beautiful training loss(Tensorboard attached).

We trained this model as a r=32, alpha=64 LoRA adapter with 2 epochs over RolePlay-NPC-Quest using a 80GB vRAM A100 in Google Colab. For this run, we employed a learning rate of 1e-4 and an effective batch size of 32. A cosine learning rate scheduler was used with an 500-step warmup. With a gradient clipping of 1.0.

Notes

As Unsloth noted in their official guide, training Gemma4 with text only would lead to a higher than usual loss and grad_norm, which we observed during training.
The performance of this model, especially the intruction-following capabilities is a huge step up compared to Gemma3/3n.


Inference Guidelines

Recommended Settings:

temp = 1.0, top_p = 0.95 and top_k = 64.

Optimal System Prompt:

System Prompt without Objective:

Enter Roleplay Mode. You are <|character name|>.
 Background: <|Character background/bio|>
 Location: <|Description of the current location|>
 Roleplaying Instructions: <|Instructions|>

System Prompt with Objective:

Enter Roleplay Mode.
You are <|character name|>.
 Background: <|Character background/bio|>
Location: <|Description of the current location|>
Quest: <|Quest description|>
Roleplaying Instructions: <|Instructions|>

Example of Roleplaying Instructions: Here is an example of the roleplaying instructions we used to train the model:

Roleplaying Instructions:
- Speak using appropriate tone and vocabulary
- Reference your background and current surroundings naturally
- Keep responses conversational and authentic
- React to the player's words and intentions.
Your first response should be a greeting to the player.

First User Prompt: It is recommended that the first user prompt should always be Greetings, then letting the model generate a greeting, smiliar to how an NPC would behave in game.

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