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kiel2
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Model Details
Model Provider
kiel2
Model Tree
meta-llama/Llama-3.2-3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
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.
You can run this model locally or in the cloud using standard Hugging Face transformers routines:
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig model_id = "kiel2/Kiel-2.1-Sol" # Optimal setup matching the API environmentquantization_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))CitationCode 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)}}}import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig model_id = "kiel2/Kiel-2.1-Sol" # Optimal setup matching the API environmentquantization_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))CitationCode 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)}}}