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BigTree108

Private LLM deployment and fine-tuning services

Private LLM and fine-tuning services: open-weight models such as Llama, Mistral, Qwen, Gemma and gpt-oss fine-tuned on your data with LoRA, distilled into smaller and cheaper models, and served in your own cloud or on your own servers with vLLM or SGLang, hosted fine-tuning through OpenAI, Microsoft Foundry, Amazon Bedrock and Google Cloud, and the training data and evaluation behind it, with dedicated LLM engineers for your team or a project delivered end to end.

Fine-tuning and model work we take on

  • Fine-tuning open-weight models

    Supervised fine-tuning with LoRA or QLoRA in Hugging Face TRL, Unsloth or Axolotl, and preference tuning with DPO, for a tone, a format or a domain the base model gets wrong.

  • Distillation into smaller models

    A large model’s behaviour on your task taught to a small one that answers faster and costs a fraction per request, measured against the large model on your evaluation set.

  • Hosted fine-tuning

    Fine-tuning through OpenAI, Microsoft Foundry, Amazon Bedrock or Google Cloud when you prefer a managed model, with the training data and the evaluation kept in your hands.

  • Private LLM deployment

    Open-weight models served with vLLM or SGLang on GPUs in your Azure, AWS or Google Cloud account, or on your own hardware, behind an API compatible with the one your applications already call.

  • Quantisation and inference cost

    Models quantised and batched for your hardware, with throughput, latency and cost per thousand requests measured before and after.

  • Training data preparation

    Examples gathered from your documents, logs and experts, cleaned, de-duplicated, stripped of personal data and labelled, with synthetic examples generated and checked where real ones are scarce.

  • Evaluation

    A held-out test set and task-specific scores agreed before training, so a fine-tuned model has to beat the base model and the hosted alternative before it ships.

  • Language and domain adaptation

    Models adapted to a language, such as Ukrainian, or to the vocabulary of law, medicine, finance or your product, where general models fall short.

  • Retraining pipelines

    Data, training runs and model versions tracked in MLflow or Weights & Biases, so the next fine-tune is a pipeline run rather than a project.

Hire LLM engineers

  • Dedicated LLM engineers

    LLM engineers who join your team full time, work in your tools and process, and report to your lead. You interview them; we carry the Ukrainian contract, payroll, invoicing and leave.

  • LLM fine-tuning projects

    A defined piece of LLM fine-tuning with a scope, a fixed plan and a named lead on our side who owns the result and reports progress in your channels.

  • Ongoing LLM fine-tuning

    LLM fine-tuning as a continuing service: the same people every month, a backlog you prioritise, and hours you can see in our portal and on the invoice.

The dedicated team page explains how specialists join your team, and the outsourcing page covers project delivery, take-overs and how we charge.

Who works on your LLM fine-tuning

  • LLM and machine learning engineers

    Fine-tuning, distillation and evaluation

  • MLOps engineers

    GPU infrastructure and model serving

  • Data engineers

    Training data pipelines

  • Data annotators

    Labelled and reviewed examples

  • Python and .NET engineers

    The applications that call the model

Fine-tune when the numbers say so

Many tasks are solved with a hosted model, a good prompt and retrieval. Fine-tuning earns its cost when you need a consistent format or tone, a smaller and cheaper model at volume, or a model that runs where your data must stay. We test the cheaper options on your evaluation set first, and fine-tune when the scores show the gap.

Training data, adapters and model weights stay in your accounts, personal data is removed before training, and every model version can be traced to the data and settings that produced it.

Other AI development services we provide

AI development overview

Questions about private LLMs and fine-tuning

Should we fine-tune or use RAG?

They solve different problems. Retrieval gives a model facts it does not have and keeps them current; fine-tuning changes how the model behaves: its format, its tone or its skill at a narrow task. Many systems use both, and we test which one moves your evaluation score before you pay for training.

Can we run an LLM without sending data to an outside provider?

Yes. Open-weight models such as Llama, Mistral, Qwen, Gemma and gpt-oss run in your own cloud account or on your own servers, and current ones match hosted models on many business tasks. We size the hardware from your volumes and measure quality against the hosted option.

How much data does fine-tuning need?

For a format or a tone, a few hundred to a few thousand good examples are often enough with LoRA. A new domain or skill needs more. We start with what you have, measure, and add data where the evaluation shows gaps.

How quickly can LLM engineers start?

When the right LLM engineer is available, the start is gated only by your interview and the NDA and IP assignment. Otherwise we run a search, which typically produces candidate profiles within two to three weeks, and nobody starts until you have said yes.

How do we hire LLM engineers through BigTree108?

Tell us the work, the seniority and the hours you need. We propose one or two people with their profiles, you interview them the way you would interview your own hire, and you sign one agreement with BIG TREE 108 LLC and receive one invoice a month.

Who owns the work they produce?

You do. Every specialist has a signed contract with BigTree108 that assigns all work product to the company, and our agreement with you assigns it onward. Code, designs and documents are delivered into your own repositories and tools, not kept where only we can change them.

Need a model of your own?

Tell us the task, the data you hold and where the model has to run. You get an answer within one business day: a plan for an evaluation, a hardware estimate, or candidate profiles.

Email hello@bigtree108.com How we respond