chimbiwide/RolePlay-NPC
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How to use chimbiwide/Gemma3NPC-it-beta with Transformers:
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("chimbiwide/Gemma3NPC-it-beta", dtype="auto")How to use chimbiwide/Gemma3NPC-it-beta with Unsloth Studio:
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/Gemma3NPC-it-beta to start chatting
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/Gemma3NPC-it-beta to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for chimbiwide/Gemma3NPC-it-beta to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="chimbiwide/Gemma3NPC-it-beta",
max_seq_length=2048,
)As mentioned in our original article, we employed a very conservative training parameters for Gemma3NPC
Ever since then, we have always wanted to test the performance of the model when we make the training parameters less conservative.
So we present Gemma3NPC-it-beta.
Check out our training notebook here
Gemma3NPC-it
| Parameter | Gemma3NPC-it | Gemma3NPC-it-beta |
|---|---|---|
| Learning Rate | 2e-5 | 2.5e-5 (+25%) |
| Warmup Steps | 800 | 100 |
| gradient clipping | 0.4 | 1.0 |
Here is a graph of the Step Training Loss, saved every 10 steps: