Empowering the open-source community with robust ML pipelines, fine-tuned models, and agentic workflows.
Based in Tunisia, Kamka IT is a specialized consulting firm operating at the intersection of Advanced Backend Engineering and Artificial Intelligence. We build scalable, self-hosted architectures and intelligent agentic systems.
Beyond our enterprise consulting, we are deeply committed to the open-source ethos. Our Hugging Face organization is dedicated to sharing our internal research, fine-tuned models, and end-to-end pipelines with the global AI community.
At Kamka IT, we believe that the future of AI lies in transparency, accessibility, and collaboration. Our open-source objectives on Hugging Face are:
We have invested heavily in the intersection of LLMs and biological data.
BioTATA-7B: A specialized 7B parameter model designed for advanced sequence analysis and biological text generation.shadow-clown-BioMistral-7B-DARE: An experimental merge using the DARE technique to combine robust reasoning with bio-medical knowledge.shadow-clown-BioMistral-7B-SLERP: A SLERP-merged variant of the BioMistral architecture, optimizing the interpolation of weights for enhanced downstream performance.High-quality models require high-quality data. We open-source our curation efforts to accelerate research.
TATA-NOTATA-FineMistral: A specialized dataset for nucleotide transformer downstream tasks, heavily curated for DNA sequence classification.Our models and pipelines are built using modern, scalable, and sovereign technologies:
We design our models to be easily integrable into your existing workflows. Here is a quick example of how to load one of our text-generation models via the transformers library:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "Kamka-IT/BioTATA-7B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
prompt = "Analyze the following nucleotide sequence: "
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
(Note: For comprehensive pipeline tutorials, check the specific Model Cards!)
Whether you are a researcher looking to fine-tune a model, a developer building an agentic system, or a company seeking to deploy sovereign, self-hosted AI architecture, we would love to connect.