Instructions to use the81coder/gemma-3-1b-it-reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use the81coder/gemma-3-1b-it-reasoning with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf the81coder/gemma-3-1b-it-reasoning:Q8_0 # Run inference directly in the terminal: llama cli -hf the81coder/gemma-3-1b-it-reasoning:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf the81coder/gemma-3-1b-it-reasoning:Q8_0 # Run inference directly in the terminal: llama cli -hf the81coder/gemma-3-1b-it-reasoning: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 the81coder/gemma-3-1b-it-reasoning:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf the81coder/gemma-3-1b-it-reasoning: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 the81coder/gemma-3-1b-it-reasoning:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf the81coder/gemma-3-1b-it-reasoning:Q8_0
Use Docker
docker model run hf.co/the81coder/gemma-3-1b-it-reasoning:Q8_0
- LM Studio
- Jan
- vLLM
How to use the81coder/gemma-3-1b-it-reasoning with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "the81coder/gemma-3-1b-it-reasoning" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "the81coder/gemma-3-1b-it-reasoning", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/the81coder/gemma-3-1b-it-reasoning:Q8_0
- Ollama
How to use the81coder/gemma-3-1b-it-reasoning with Ollama:
ollama run hf.co/the81coder/gemma-3-1b-it-reasoning:Q8_0
- Unsloth Studio
How to use the81coder/gemma-3-1b-it-reasoning 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 the81coder/gemma-3-1b-it-reasoning 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 the81coder/gemma-3-1b-it-reasoning to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for the81coder/gemma-3-1b-it-reasoning to start chatting
- Docker Model Runner
How to use the81coder/gemma-3-1b-it-reasoning with Docker Model Runner:
docker model run hf.co/the81coder/gemma-3-1b-it-reasoning:Q8_0
- Lemonade
How to use the81coder/gemma-3-1b-it-reasoning with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull the81coder/gemma-3-1b-it-reasoning:Q8_0
Run and chat with the model
lemonade run user.gemma-3-1b-it-reasoning-Q8_0
List all available models
lemonade list
- Atomic Chat
| base_model: google/gemma-3-1b-it | |
| tags: | |
| - gemma-3 | |
| - text-generation | |
| - reasoning | |
| # gemma-3-1b-it-reasoning | |
| This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) optimized for step-by-step reasoning tasks using the [nohurry/Opus-4.6-Reasoning-3000x-filtered](https://huggingface.co/datasets/nohurry/Opus-4.6-Reasoning-3000x-filtered) dataset. | |
| ### Model Description | |
| - **Developed by:** the81coder | |
| - **Model type:** Gemma 3 | |
| - **Language(s):** English | |
| - **License:** Gemma Terms of Use | |
| - **Fine-tuned from model:** google/gemma-3-1b-it | |
| ### Training Procedure | |
| The model was fine-tuned using QLoRA with the following configurations: | |
| - **Learning Rate:** 1e-5 | |
| - **Batch Size:** 1 (with 4 accumulation steps) | |
| - **Optimizer:** AdamW | |
| - **Precision:** bfloat16 | |
| - **Target Modules:** q_proj, v_proj, k_proj, o_proj | |
| ### Usage | |
| You can use this model with the `transformers` library: | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| model_id = 'the81coder/gemma-3-1b-it-reasoning' | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_id, | |
| torch_dtype=torch.bfloat16, | |
| device_map='auto' | |
| ) | |
| ``` |