Instructions to use kiel2/Kiel-2.1-Sol with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kiel2/Kiel-2.1-Sol with Transformers:

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

pipe = pipeline("text-generation", model="kiel2/Kiel-2.1-Sol")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kiel2/Kiel-2.1-Sol")
model = AutoModelForCausalLM.from_pretrained("kiel2/Kiel-2.1-Sol", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kiel2/Kiel-2.1-Sol 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/Kiel-2.1-Sol:F16
# Run inference directly in the terminal:
llama cli -hf kiel2/Kiel-2.1-Sol:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kiel2/Kiel-2.1-Sol:F16
# Run inference directly in the terminal:
llama cli -hf kiel2/Kiel-2.1-Sol:F16

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/Kiel-2.1-Sol:F16
# Run inference directly in the terminal:
./llama-cli -hf kiel2/Kiel-2.1-Sol:F16

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/Kiel-2.1-Sol:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kiel2/Kiel-2.1-Sol:F16

Use Docker

docker model run hf.co/kiel2/Kiel-2.1-Sol:F16

LM Studio
Jan

vLLM

How to use kiel2/Kiel-2.1-Sol with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kiel2/Kiel-2.1-Sol"
# 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/Kiel-2.1-Sol",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kiel2/Kiel-2.1-Sol:F16

SGLang

How to use kiel2/Kiel-2.1-Sol 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 "kiel2/Kiel-2.1-Sol" \
    --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": "kiel2/Kiel-2.1-Sol",
		"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 "kiel2/Kiel-2.1-Sol" \
        --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": "kiel2/Kiel-2.1-Sol",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use kiel2/Kiel-2.1-Sol with Ollama:
```
ollama run hf.co/kiel2/Kiel-2.1-Sol:F16
```
Unsloth Desktop

How to use kiel2/Kiel-2.1-Sol with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kiel2/Kiel-2.1-Sol:F16

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": "kiel2/Kiel-2.1-Sol:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kiel2/Kiel-2.1-Sol with Docker Model Runner:
```
docker model run hf.co/kiel2/Kiel-2.1-Sol:F16
```

Lemonade

How to use kiel2/Kiel-2.1-Sol with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kiel2/Kiel-2.1-Sol:F16

Run and chat with the model

lemonade run user.Kiel-2.1-Sol-F16

List all available models

lemonade list

Hermes Agent

How to use kiel2/Kiel-2.1-Sol 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/Kiel-2.1-Sol:F16

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/Kiel-2.1-Sol:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kiel2/Kiel-2.1-Sol with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kiel2/Kiel-2.1-Sol:F16

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/Kiel-2.1-Sol:F16" \
  --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"

Kiel-2.1-Sol

Kiel-2.1-Sol is a lightweight, hyper-efficient conversational language model fine-tuned to power the text tier of the KielTech AI production API. Built on top of the Llama 3.2 architecture, 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
Model Type: Causal Language Model (Transformer)
Base Model: meta-llama/Llama-3.2-3B-Instruct
Language(s): English
License: Apache 2.0
Hugging Face Hub: kiel2/Kiel-2.1-Sol

Uses

Direct Use

Natural language conversations, multi-turn assistant dialogue, structural data parsing, and instruction-following tasks via the KielTech FastAPI backend.

Out-of-Scope Use

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 architecture, Kiel-2.1-Sol 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/Kiel-2.1-Sol"

# 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 Kiel-2.1-Sol 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))
Citation
Code snippet
@misc{kiel2-Kiel-2.1-Sol-2026,
  author = {KielTech},
  title = {Kiel-2.1-Sol: Lightweight Conversational Language Model},
  year = {2026},
  publisher = {Hugging Face},
  journal = {Hugging Face Repository},
  howpublished = {\url{[https://huggingface.co/kiel2/Kiel-2.1-Sol](https://huggingface.co/kiel2/Kiel-2.1-Sol)}}
}

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Safetensors

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Tensor type

BF16

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