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))