Text Classification
Safetensors
GGUF
Portuguese
gemma3_text
llama.cpp
unsloth
intent-detection
gemma-3
delivery
conversational
Instructions to use RiosWesley/gemma-3-270M-Model-Router with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use RiosWesley/gemma-3-270M-Model-Router with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="RiosWesley/gemma-3-270M-Model-Router", filename="gemma-3-270m-it.Q8_0.gguf", )
llm.create_chat_completion( messages = "\"I like you. I love you\"" )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use RiosWesley/gemma-3-270M-Model-Router with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf RiosWesley/gemma-3-270M-Model-Router:Q8_0 # Run inference directly in the terminal: llama-cli -hf RiosWesley/gemma-3-270M-Model-Router:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf RiosWesley/gemma-3-270M-Model-Router:Q8_0 # Run inference directly in the terminal: llama-cli -hf RiosWesley/gemma-3-270M-Model-Router:Q8_0
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf RiosWesley/gemma-3-270M-Model-Router:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf RiosWesley/gemma-3-270M-Model-Router:Q8_0
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf RiosWesley/gemma-3-270M-Model-Router:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf RiosWesley/gemma-3-270M-Model-Router:Q8_0
Use Docker
docker model run hf.co/RiosWesley/gemma-3-270M-Model-Router:Q8_0
- LM Studio
- Jan
- Ollama
How to use RiosWesley/gemma-3-270M-Model-Router with Ollama:
ollama run hf.co/RiosWesley/gemma-3-270M-Model-Router:Q8_0
- Unsloth Studio new
How to use RiosWesley/gemma-3-270M-Model-Router 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 RiosWesley/gemma-3-270M-Model-Router 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 RiosWesley/gemma-3-270M-Model-Router to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for RiosWesley/gemma-3-270M-Model-Router to start chatting
- Docker Model Runner
How to use RiosWesley/gemma-3-270M-Model-Router with Docker Model Runner:
docker model run hf.co/RiosWesley/gemma-3-270M-Model-Router:Q8_0
- Lemonade
How to use RiosWesley/gemma-3-270M-Model-Router with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RiosWesley/gemma-3-270M-Model-Router:Q8_0
Run and chat with the model
lemonade run user.gemma-3-270M-Model-Router-Q8_0
List all available models
lemonade list
| { | |
| "_sliding_window_pattern": 6, | |
| "architectures": [ | |
| "Gemma3ForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "attn_logit_softcapping": null, | |
| "bos_token_id": 2, | |
| "torch_dtype": "bfloat16", | |
| "eos_token_id": 106, | |
| "final_logit_softcapping": null, | |
| "head_dim": 256, | |
| "hidden_activation": "gelu_pytorch_tanh", | |
| "hidden_size": 640, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 2048, | |
| "layer_types": [ | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention" | |
| ], | |
| "max_position_embeddings": 32768, | |
| "model_type": "gemma3_text", | |
| "num_attention_heads": 4, | |
| "num_hidden_layers": 18, | |
| "num_key_value_heads": 1, | |
| "pad_token_id": 0, | |
| "query_pre_attn_scalar": 256, | |
| "rms_norm_eps": 1e-06, | |
| "rope_local_base_freq": 10000.0, | |
| "rope_scaling": null, | |
| "rope_theta": 1000000.0, | |
| "sliding_window": 512, | |
| "transformers_version": "4.57.0", | |
| "unsloth_fixed": true, | |
| "unsloth_version": "2025.11.2", | |
| "use_bidirectional_attention": false, | |
| "use_cache": true, | |
| "vocab_size": 262144 | |
| } |