Instructions to use kiel2/Kiel-2.2-Sol with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kiel2/Kiel-2.2-Sol with Transformers:

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

pipe = pipeline("text-generation", model="kiel2/Kiel-2.2-Sol")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kiel2/Kiel-2.2-Sol")
model = AutoModelForCausalLM.from_pretrained("kiel2/Kiel-2.2-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

vLLM

How to use kiel2/Kiel-2.2-Sol with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kiel2/Kiel-2.2-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-2.2-Sol",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kiel2/Kiel-2.2-Sol

SGLang

How to use kiel2/Kiel-2.2-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-2.2-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-2.2-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-2.2-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-2.2-Sol",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kiel2/Kiel-2.2-Sol with Docker Model Runner:
```
docker model run hf.co/kiel2/Kiel-2.2-Sol
```

Kiel-2.2-Sol

Kiel-2.2-Sol is a high-performance multi-domain instruction model. It is engineered to excel in complex logical reasoning, advanced coding patterns (such as asynchronous Python workflows), and multi-domain problem solving. As part of the Kiel AI ecosystem, it serves as the core text intelligence engine powering responsive chat interfaces and backend generative pipelines.

Model Details

Developed by: KielTech
Model Type: Causal Language Model (Fully Merged Standalone Weights)
Base Model: kiel2/Kiel-2.1-Sol
Language(s): English
Format: Safetensors (FP16 optimized for high-throughput cloud serving)
Hugging Face Hub: kiel2/Kiel-2.2-Sol

Model Sources

Repository: Hugging Face Hub
Ecosystem: Kiel AI Suite (Integrated with KielForge and Kiel-2-OCR/VQA suites)

Uses

Direct Use

Advanced conversational AI assistants and chat sidebars.
Automated code generation, asynchronous programming helpers, and software architecture reasoning.
Multi-domain task execution requiring strict instruction-following.

Downstream Use

Can be deployed as a primary standalone language model backend for commercial APIs using high-throughput serving engines like vLLM and Text Generation Inference (TGI).

How to Get Started with the Model

Because Kiel-2.2-Sol is provided as a fully merged standalone model in safetensors format, you can load and run it directly without any PEFT wrapper dependencies:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID = "kiel2/Kiel-2.2-Sol"

print(f"Loading {MODEL_ID} tokenizer and model...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)
model.eval()

# Test prompt
prompt = (
    "System: You are Kiel-2.2-Sol, an advanced multi-domain AI assistant.\n"
    "User: Write a robust Python function using asyncio and aiohttp to concurrently fetch JSON payloads."
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.7,
        top_p=0.9,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(response)
Citation
Code snippet
@misc{kiel2-Kiel-2.2-Sol-2026,
  author = {KielTech},
  title = {Kiel-2.2-Sol: High-Performance Multi-Domain Language Model},
  year = {2026},
  publisher = {Hugging Face},
  journal = {Hugging Face Repository},
  howpublished = {\url{[https://huggingface.co/kiel2/Kiel-2.2-Sol](https://huggingface.co/kiel2/Kiel-2.2-Sol)}}
}

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Safetensors

Model size

3B params

Tensor type

F16

Model tree for kiel2/Kiel-2.2-Sol

Base model

meta-llama/Llama-3.2-3B-Instruct

Adapter

kiel2/Kiel-2.1-Sol

Finetuned

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