Instructions to use kiel2/KielMind-Lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kiel2/KielMind-Lite with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="kiel2/KielMind-Lite",
	filename="KielMind-Lite-Q4.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kiel2/KielMind-Lite 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 kiel2/KielMind-Lite
# Run inference directly in the terminal:
llama cli -hf kiel2/KielMind-Lite

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kiel2/KielMind-Lite
# Run inference directly in the terminal:
llama cli -hf kiel2/KielMind-Lite

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 kiel2/KielMind-Lite
# Run inference directly in the terminal:
./llama-cli -hf kiel2/KielMind-Lite

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 kiel2/KielMind-Lite
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kiel2/KielMind-Lite

Use Docker

docker model run hf.co/kiel2/KielMind-Lite

LM Studio
Jan

vLLM

How to use kiel2/KielMind-Lite with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kiel2/KielMind-Lite"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kiel2/KielMind-Lite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kiel2/KielMind-Lite

Ollama
How to use kiel2/KielMind-Lite with Ollama:
```
ollama run hf.co/kiel2/KielMind-Lite
```

Unsloth Studio

How to use kiel2/KielMind-Lite 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 kiel2/KielMind-Lite 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 kiel2/KielMind-Lite to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kiel2/KielMind-Lite to start chatting

How to use kiel2/KielMind-Lite with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kiel2/KielMind-Lite

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "kiel2/KielMind-Lite"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use kiel2/KielMind-Lite with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kiel2/KielMind-Lite

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 kiel2/KielMind-Lite

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use kiel2/KielMind-Lite with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kiel2/KielMind-Lite

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 "kiel2/KielMind-Lite" \
  --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"

Docker Model Runner
How to use kiel2/KielMind-Lite with Docker Model Runner:
```
docker model run hf.co/kiel2/KielMind-Lite
```

Lemonade

How to use kiel2/KielMind-Lite with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kiel2/KielMind-Lite

Run and chat with the model

lemonade run user.KielMind-Lite-{{QUANT_TAG}}

List all available models

lemonade list

Model Card for KielMind-Lite

KielMind-Lite is a lightweight, hyper-efficient conversational language model fine-tuned specifically to power the text tier of the KielTech AI production API. Built on top of Llama-3.2-3B-Instruct, it balances rapid execution speed with highly coherent multi-turn dialogue capabilities, making it ideal for budget-friendly, serverless deployments (such as RunPod serverless architectures).

Model Details

Model Description

Developed by: KielTech
Shared by: kiel2
Model type: Causal Language Model (Transformer)
Language(s) (NLP): English
License: Apache 2.0
Finetuned from model: croswil/Llama_Llama-3.2-3B-Instruct

Model Sources

Repository: https://huggingface.co/kiel2/KielMind-Lite

Uses

Direct Use

KielMind-Lite is designed to directly handle natural language conversations, multi-turn assistant dialogue, structural data parsing, and instruction-following tasks via the KielTech FastAPI backend.

Out-of-Scope Use

This model should not be used for high-risk automation scenarios without human oversight, malicious content generation, or deployment on systems requiring absolute real-time factuality without a grounding retrieval mechanism (RAG).

Bias, Risks, and Limitations

As a derivative of the Llama-3.2 architecture, KielMind-Lite inherits standard LLM limitations, including potential hallucinations, temporal bias (knowledge cutoff), and sensitivity to prompt wording.

How to Get Started with the Model

You can run this model locally or in the cloud using standard Hugging Face transformers routines:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model_id = "kiel2/KielMind-Lite"

# Optimal setup matching the API environment
quantization_config = BitsAndBytesConfig(load_in_4bit=True)

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    quantization_config=quantization_config,
    device_map="auto"
)

messages = [
    {"role": "user", "content": "Hello! Introduce yourself as the KielMind-Lite assistant."}
]

inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda")
outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Safetensors

Model size

3B params

Tensor type

BF16