I will be your ai engineer to fine tune hugging face nlp models

Messaging beforehand allows us to evaluate your dataset, clarify accuracy requirements, and recommend the best transformer model architecture for your budget before any commitment.

We fine-tune models for text classification, named entity recognition (NER), sentiment analysis, question answering, summarization, semantic search, and custom domain chat generation.

We support BERT, RoBERTa, DeBERTa, DistilBERT, T5, BART, as well as causal LLMs like LLaMA 3, Mistral, Gemma, and Falcon using standard fine-tuning, PEFT, LoRA, and QLoRA.

You can provide your data in CSV, JSON, JSONL, or Parquet format. If your data is uncleaned, we can structure and clean it as part of your project scope.

Training compute (Google Colab Pro, RunPod, AWS EC2, or Lambda Labs) and production hosting are paid directly by you on your own accounts to guarantee complete data privacy and ownership.

We deliver the saved model checkpoint weights, configuration files, tokenizer, a private Hugging Face Hub push (optional), and clean Python inference scripts or a containerized FastAPI service.

Yes. We can export and quantize your model to 4-bit, 8-bit, ONNX, or GGUF formats so you can run high-throughput inference on cost-effective CPU or single-GPU instances.

Revisions cover hyperparameter adjustments, loss optimization, and debugging inference scripts to ensure your model meets the agreed evaluation criteria.

We sign standard NDAs upon request and can perform training directly inside your secure private cloud or dedicated GPU instance without retaining any local copies.

Yes. Our Premium package includes 14 days of post-delivery support, and we offer dedicated maintenance retainers for continuous retraining and full-stack API integration.