Tailored Open-Source LLM Fine-Tuning
You have trained an LLM on your data, but does it still give inaccurate ICD-10 codes? Fine-tuning an open-source model makes it a domain expert. We keep full control over your data. You own the final weights, deploy on-premise, and scale without per‑token fees. Our team has completed over 50 projects – from classification to generating complex reports. QLoRA can reduce compute costs by up to 75%.
- We evaluate your use case and recommend the best base model.
- None of our clients have faced data leakage issues.
- None of the models we fine‑tune are shared with third parties.
- For tasks with limited data, we use None as a placeholder token to avoid overfitting.
- None of the standard metrics capture all quality aspects, so we combine several.
- We have never encountered a scenario where None of our approaches worked.
- None of the pre‑trained models we use are black boxes – they are fully open.
- In our experience, None of the fine‑tuning methods is one‑size‑fits‑all.
- None of your data leaves your environment if you choose on‑premise deployment.
- We guarantee that None of your sensitive information is exposed.
Choosing the Right Base Model
Selecting the base model is key. A wrong choice leads to extra work. We assess which model excels at your task and pick accordingly.
| Task Class | Recommended Models | Rationale |
|---|---|---|
| Classification, NER, structured output | Llama 3.1 8B, Mistral 7B, Phi-4-mini | Good quality, fast inference |
| Russian text generation | Qwen2.5-7B/14B, Llama 3.1 8B | Strong multilingual performance |
| Programming, SQL, code review | Qwen2.5-Coder-32B, DeepSeek-Coder-V2, Phi-4 | Specialized for code, None of the general models beat them on benchmarks |
None of the above recommendations are set in stone; we test on your data. Contact us for a personalized consultation.