Instructions to use chimbiwide/Gemma3NPC-filtered-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chimbiwide/Gemma3NPC-filtered-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="chimbiwide/Gemma3NPC-filtered-v2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("chimbiwide/Gemma3NPC-filtered-v2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use chimbiwide/Gemma3NPC-filtered-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "chimbiwide/Gemma3NPC-filtered-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chimbiwide/Gemma3NPC-filtered-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/chimbiwide/Gemma3NPC-filtered-v2
- SGLang
How to use chimbiwide/Gemma3NPC-filtered-v2 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "chimbiwide/Gemma3NPC-filtered-v2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chimbiwide/Gemma3NPC-filtered-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "chimbiwide/Gemma3NPC-filtered-v2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chimbiwide/Gemma3NPC-filtered-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Studio
How to use chimbiwide/Gemma3NPC-filtered-v2 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 chimbiwide/Gemma3NPC-filtered-v2 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/Gemma3NPC-filtered-v2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for chimbiwide/Gemma3NPC-filtered-v2 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="chimbiwide/Gemma3NPC-filtered-v2", max_seq_length=2048, ) - Docker Model Runner
How to use chimbiwide/Gemma3NPC-filtered-v2 with Docker Model Runner:
docker model run hf.co/chimbiwide/Gemma3NPC-filtered-v2
Gemma3NPC-filtered-v2
Another one of those test models
we were chatting and just decided: "What would happen if we have a higher learning rate and run 3 epochs🤓"
So here it is, the second generation of filtered Gemma3NPC, with bare minimum effort of typing 6 characters and a little patience(3 hours).
Again, our training notebook is on Github.
Training Parameters
| Parameter | Gemma3NPC-Filtered | v2 |
|---|---|---|
| Learning rate | 2e-5 | 4e-5 |
| Warmup Steps | 150 | 100 |
| Gradient clipping | 0.5 | 1.0 |
Training Loss
This time, we accidentally stopped the training when it reach step 200, so when we resumed, the training started from scratch but seems to have used the last checkpoint.
Here is a graph of the training loss, saved after after 5 steps.
Next Steps
Our top priority is now the gathering of more datasets, such as SODA and some real video game data.
We might try to switch to a new model(Qwen?), as the Gemma3n license is a little restrictive.
New methods other than SFT to improve performance, like GRPO.
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