Instructions to use Nonovogo/gemma-3_Python_Trial_2R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nonovogo/gemma-3_Python_Trial_2R with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Nonovogo/gemma-3_Python_Trial_2R", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use Nonovogo/gemma-3_Python_Trial_2R 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 Nonovogo/gemma-3_Python_Trial_2R 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 Nonovogo/gemma-3_Python_Trial_2R to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Nonovogo/gemma-3_Python_Trial_2R to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Nonovogo/gemma-3_Python_Trial_2R", max_seq_length=2048, )
Uploaded model
- Developed by: Nonovogo
- License: apache-2.0
- Finetuned from model : unsloth/gemma-3-270m-it
Use
text = tokenizer.apply_chat_template(
messages,
tokenize = False,
add_generation_prompt = True
).removeprefix('<bos>')
# This forces the model to enter "thinking mode" immediately.
text += "<think>\n"
# 3. Generate
_ = model.generate(
**tokenizer(text, return_tensors="pt").to("cuda"),
max_new_tokens=2048, # Don't let it ramble forever
# --- STABILITY SETTINGS ---
do_sample=True, # Enable sampling to break deterministic loops
temperature=0.1, # Very low temp (focused) but not zero
top_p=0.95, # Standard filtering
repetition_penalty=1.0, # CRITICAL: Disable penalty (1.0 = no penalty)
streamer=TextStreamer(tokenizer, skip_prompt=True),
eos_token_id=tokenizer.eos_token_id # Ensure it knows when to stop
)
For better output
- This gemma3_text model was trained 2x faster with Unsloth and Huggingface's TRL library.
Inference Providers NEW
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# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Nonovogo/gemma-3_Python_Trial_2R", dtype="auto")