Instructions to use chimbiwide/Gemma4NPC-E4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chimbiwide/Gemma4NPC-E4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="chimbiwide/Gemma4NPC-E4B") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("chimbiwide/Gemma4NPC-E4B") model = AutoModelForImageTextToText.from_pretrained("chimbiwide/Gemma4NPC-E4B") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use chimbiwide/Gemma4NPC-E4B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "chimbiwide/Gemma4NPC-E4B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chimbiwide/Gemma4NPC-E4B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/chimbiwide/Gemma4NPC-E4B
- SGLang
How to use chimbiwide/Gemma4NPC-E4B 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/Gemma4NPC-E4B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chimbiwide/Gemma4NPC-E4B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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/Gemma4NPC-E4B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chimbiwide/Gemma4NPC-E4B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Unsloth Studio new
How to use chimbiwide/Gemma4NPC-E4B 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/Gemma4NPC-E4B 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/Gemma4NPC-E4B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for chimbiwide/Gemma4NPC-E4B to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="chimbiwide/Gemma4NPC-E4B", max_seq_length=2048, ) - Docker Model Runner
How to use chimbiwide/Gemma4NPC-E4B with Docker Model Runner:
docker model run hf.co/chimbiwide/Gemma4NPC-E4B
Gemma4NPC-E4B
The newest GemmaNPC models, with the new Gemma4-E4B model as the base model, trained using the newest RolePlay-NPC-Quest dataset.
Intended Usage
This model is trained to be used as a more game focused NPC rolaplaying model.
Training Parameters
For this model, we employed a slightly less conservative parameter, which resulted in some beautiful training loss(Tensorboard attached).
We trained this model as a r=32, alpha=64 LoRA adapter with 2 epochs over RolePlay-NPC-Quest using a 80GB vRAM A100 in Google Colab. For this run, we employed a learning rate of 1e-4 and an effective batch size of 24. A cosine learning rate scheduler was used with an 500-step warmup. With a gradient clipping of 1.0.
Notes
As Unsloth noted in their official guide, training Gemma4 with text only would lead to a higher than usual loss and grad_norm, which we observed during training.
The performance of this model, especially the intruction-following capabilities is a huge step up compared to Gemma3/3n.
Inference Guidelines
Recommended Settings:
temp = 1.0, top_p = 0.95 and top_k = 64.
Optimal System Prompt:
System Prompt without Objective:
Enter Roleplay Mode. You are <|character name|>.
Background: <|Character background/bio|>
Location: <|Description of the current location|>
Roleplaying Instructions: <|Instructions|>
System Prompt with Objective:
Enter Roleplay Mode.
You are <|character name|>.
Background: <|Character background/bio|>
Location: <|Description of the current location|>
Quest: <|Quest description|>
Roleplaying Instructions: <|Instructions|>
Example of Roleplaying Instructions: Here is an example of the roleplaying instructions we used to train the model:
Roleplaying Instructions:
- Speak using appropriate tone and vocabulary
- Reference your background and current surroundings naturally
- Keep responses conversational and authentic
- React to the player's words and intentions.
Your first response should be a greeting to the player.
First User Prompt: It is recommended that the first user prompt should always be Greetings, then letting the model generate a greeting, smiliar to how an NPC would behave in game.
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