Instructions to use Open4bits/Kai-3B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Open4bits/Kai-3B-Instruct-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Open4bits/Kai-3B-Instruct-GGUF", filename="kai-3b-instruct-Q2_K.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Open4bits/Kai-3B-Instruct-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Open4bits/Kai-3B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Open4bits/Kai-3B-Instruct-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Open4bits/Kai-3B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Open4bits/Kai-3B-Instruct-GGUF:Q4_K_M
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 Open4bits/Kai-3B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Open4bits/Kai-3B-Instruct-GGUF:Q4_K_M
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 Open4bits/Kai-3B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Open4bits/Kai-3B-Instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Open4bits/Kai-3B-Instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Open4bits/Kai-3B-Instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Open4bits/Kai-3B-Instruct-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Open4bits/Kai-3B-Instruct-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Open4bits/Kai-3B-Instruct-GGUF:Q4_K_M
- Ollama
How to use Open4bits/Kai-3B-Instruct-GGUF with Ollama:
ollama run hf.co/Open4bits/Kai-3B-Instruct-GGUF:Q4_K_M
- Unsloth Studio new
How to use Open4bits/Kai-3B-Instruct-GGUF 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 Open4bits/Kai-3B-Instruct-GGUF 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 Open4bits/Kai-3B-Instruct-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Open4bits/Kai-3B-Instruct-GGUF to start chatting
- Docker Model Runner
How to use Open4bits/Kai-3B-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/Open4bits/Kai-3B-Instruct-GGUF:Q4_K_M
- Lemonade
How to use Open4bits/Kai-3B-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Open4bits/Kai-3B-Instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Kai-3B-Instruct-GGUF-Q4_K_M
List all available models
lemonade list
| base_model: | |
| - NoesisLab/Kai-3B-Instruct | |
| model-index: | |
| - name: Kai-3B-Instruct | |
| results: | |
| - task: | |
| type: multiple-choice | |
| name: ARC-Challenge | |
| dataset: | |
| name: ARC-Challenge | |
| type: allenai/ai2_arc | |
| config: ARC-Challenge | |
| split: test | |
| metrics: | |
| - type: acc_norm | |
| value: 51.88 | |
| name: Accuracy (normalized) | |
| - task: | |
| type: multiple-choice | |
| name: HellaSwag | |
| dataset: | |
| name: HellaSwag | |
| type: Rowan/hellaswag | |
| split: validation | |
| metrics: | |
| - type: acc_norm | |
| value: 69.53 | |
| name: Accuracy (normalized) | |
| - task: | |
| type: multiple-choice | |
| name: MMLU | |
| dataset: | |
| name: MMLU | |
| type: cais/mmlu | |
| split: test | |
| metrics: | |
| - type: acc | |
| value: 53.62 | |
| name: Accuracy | |
| - task: | |
| type: multiple-choice | |
| name: PIQA | |
| dataset: | |
| name: PIQA | |
| type: piqa | |
| split: validation | |
| metrics: | |
| - type: acc_norm | |
| value: 77.53 | |
| name: Accuracy (normalized) | |
| - task: | |
| type: text-generation | |
| name: HumanEval | |
| dataset: | |
| name: HumanEval | |
| type: openai/openai_humaneval | |
| split: test | |
| metrics: | |
| - type: pass@1 | |
| value: 39.02 | |
| name: Pass@1 | |
| - task: | |
| type: text-generation | |
| name: GSM8K | |
| dataset: | |
| name: GSM8K | |
| type: gsm8k | |
| split: test | |
| metrics: | |
| - type: exact_match | |
| value: 39.27 | |
| name: Exact Match (flexible) | |
| pipeline_tag: text-generation | |
| tags: | |
| - open4bits | |
| - smollm3 | |
| - math | |
| - reasoning | |
| - distilled | |
| - ads | |
| license: apache-2.0 | |
| language: | |
| - en | |