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

Libraries

How to use SimplySara/Kai-3B-Instruct-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SimplySara/Kai-3B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SimplySara/Kai-3B-Instruct-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SimplySara/Kai-3B-Instruct-GGUF 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 SimplySara/Kai-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SimplySara/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 serve -hf SimplySara/Kai-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SimplySara/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 SimplySara/Kai-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SimplySara/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 SimplySara/Kai-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SimplySara/Kai-3B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/SimplySara/Kai-3B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use SimplySara/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 "SimplySara/Kai-3B-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SimplySara/Kai-3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SimplySara/Kai-3B-Instruct-GGUF:Q4_K_M

SGLang

How to use SimplySara/Kai-3B-Instruct-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SimplySara/Kai-3B-Instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SimplySara/Kai-3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SimplySara/Kai-3B-Instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SimplySara/Kai-3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use SimplySara/Kai-3B-Instruct-GGUF with Ollama:
```
ollama run hf.co/SimplySara/Kai-3B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use SimplySara/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 SimplySara/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 SimplySara/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 SimplySara/Kai-3B-Instruct-GGUF to start chatting

How to use SimplySara/Kai-3B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SimplySara/Kai-3B-Instruct-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "SimplySara/Kai-3B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use SimplySara/Kai-3B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/SimplySara/Kai-3B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use SimplySara/Kai-3B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SimplySara/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

Hermes Agent

How to use SimplySara/Kai-3B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SimplySara/Kai-3B-Instruct-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default SimplySara/Kai-3B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use SimplySara/Kai-3B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SimplySara/Kai-3B-Instruct-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "SimplySara/Kai-3B-Instruct-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Please re-pull weights for Kai-3B-Instruct (v1.1 fixes mode collapse)

by OzTianlu - opened Feb 28

Discussion

OzTianlu

Feb 28

Hi Community !
First of all, thank you so much for your incredible automated quantization work! Your GGUF conversion helped our model, Kai-3B-Instruct, get picked up and recommended on LM Studio today, which is a huge honor for our lab.However, we noticed a critical issue with the currently deployed GGUFs (specifically the Q4_K_S variants), and we kindly request a re-pull of the weights and a small note in your repo.1. Stale Weights (v1.0 vs. v1.1)It appears the current GGUFs were snapshotted from our initial v1.0 push. That version suffered from "logic poisoning" (overfitting to reasoning datasets), causing severe mode collapse where the model forgot how to chat and only spoke in rigid Analysis -> Approach -> Solution templates.We have since completed a 4000-step annealing phase with a balanced SlimOrca mix and pushed the v1.1 weights to our main repo (NoesisLab/Kai-3B-Instruct), which completely restores its conversational sanity while retaining its logical capabilities. Could you please trigger a re-pull and re-quantize using the latest main branch?2. Extreme Sensitivity to 4-bit Quantization (The ADS Algorithm)Unlike standard SFT models, Kai-3B is trained using a novel technique called Adaptive Dual-Search (ADS) Distillation. It uses a parameter-free log-barrier penalty based on Shannon entropy to physically prune the latent space into a sharp, low-entropy manifold (enabling $O(1)$ reasoning without CoT).Because the model's logic crystal is so tightly packed at 3B parameters, aggressive low-bit quantization (like Q4_K_S) introduces quantization noise that acts as artificial entropy. To dodge this entropy penalty, the quantized model instinctively retreats into its rigid fallback templates, breaking the conversational alignment.Requests:Please update the GGUFs with our latest v1.1 weights.If possible, could you add a brief warning in your README? Something like: "Note: Due to the ADS distillation method, this model is highly sensitive to quantization noise. Q8_0 or Q6_K are strongly recommended for preserving both logical integrity and conversational alignment. Q4 variants may exhibit template collapse."Thank you again for empowering the open-source community. We truly appreciate your work!Best regards,
[NoesisLab]

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