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)}}
}