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

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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).

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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.

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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))