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
| { | |
| "alpha_pattern": {}, | |
| "auto_mapping": { | |
| "base_model_class": "Gemma3ForCausalLM", | |
| "parent_library": "transformers.models.gemma3.modeling_gemma3", | |
| "unsloth_fixed": true | |
| }, | |
| "base_model_name_or_path": "unsloth/gemma-3-270m-it-unsloth-bnb-4bit", | |
| "bias": "none", | |
| "corda_config": null, | |
| "eva_config": null, | |
| "exclude_modules": null, | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": true, | |
| "layer_replication": null, | |
| "layers_pattern": null, | |
| "layers_to_transform": null, | |
| "loftq_config": {}, | |
| "lora_alpha": 8, | |
| "lora_bias": false, | |
| "lora_dropout": 0, | |
| "megatron_config": null, | |
| "megatron_core": "megatron.core", | |
| "modules_to_save": null, | |
| "peft_type": "LORA", | |
| "qalora_group_size": 16, | |
| "r": 8, | |
| "rank_pattern": {}, | |
| "revision": null, | |
| "target_modules": "(?:.*?(?:language|text).*?(?:self_attn|attention|attn|mlp|feed_forward|ffn|dense).*?(?:q_proj|k_proj|v_proj|o_proj|gate_proj|up_proj|down_proj).*?)|(?:\\bmodel\\.layers\\.[\\d]{1,}\\.(?:self_attn|attention|attn|mlp|feed_forward|ffn|dense)\\.(?:(?:q_proj|k_proj|v_proj|o_proj|gate_proj|up_proj|down_proj)))", | |
| "target_parameters": null, | |
| "task_type": "CAUSAL_LM", | |
| "trainable_token_indices": null, | |
| "use_dora": false, | |
| "use_qalora": false, | |
| "use_rslora": false | |
| } |