Instructions to use Open4bits/Kai-3B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

Kai-3B-Instruct-GGUF / README.md

fmasterpro27

Update README.md

1053428 verified 3 months ago

preview code

raw

history blame contribute delete

1.87 kB

metadata

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