chimbiwide/pippa_filtered
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How to use chimbiwide/Gemma3NPC-filtered with Transformers:
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("chimbiwide/Gemma3NPC-filtered", dtype="auto")How to use chimbiwide/Gemma3NPC-filtered 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-filtered 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-filtered to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for chimbiwide/Gemma3NPC-filtered to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="chimbiwide/Gemma3NPC-filtered",
max_seq_length=2048,
)We trained this model as a rank-12 LoRA adapter with one epoch over pippa_filtered using a 40GB vRAM A100 in Google Colab. For this run, we employed a learning rate of 2e-5 and a total batch size of 1 and gradient accumulation steps of 16. A cosine learning rate scheduler was used with a 150-step warmup. With a gradient clipping of 0.5.
Check out our training notebook here.
Here is a graph of the Step Training Loss, saved every 5 steps: