Instructions to use Havoc999/gemma-4-E2b-it-megacode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Havoc999/gemma-4-E2b-it-megacode with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Havoc999/gemma-4-E2b-it-megacode", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Havoc999/gemma-4-E2b-it-megacode with 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 Havoc999/gemma-4-E2b-it-megacode 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 Havoc999/gemma-4-E2b-it-megacode to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Havoc999/gemma-4-E2b-it-megacode to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Havoc999/gemma-4-E2b-it-megacode", max_seq_length=2048, )
Uploaded model
- Developed by: Havoc999
- License: apache-2.0
- Finetuned from model : unsloth/gemma-4-e2b-it-unsloth-bnb-4bit
This gemma4 model was trained 2x faster with Unsloth
This is an adapter only model, you need the base model to work with if you want to use this.
This model was trained on "rombodawg/LosslessMegaCodeTrainingV3_1.6m_Evol" for 100 steps. The model has a loss point of 0.1478. Max Sequence Length: 2048. Training Method: QLoRA (Unsloth FastLanguageModel) targeted across all 7 linear layers (q, k, v, o, gate, up, down).
UPDATE : The merged model : Havoc999/gemma-4-E2b-it-megacode-merged
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