We stopped training LLMs from scratch. We built an AI Foundry instead.

说明与用途

小团队无法承担从零开始训练LLM的成本，但需要可信且高效的行业定制模型。VIDRAFT 的 AI Foundry 通过诊断、重组、蒸馏、量化和验证开源模型，获得了高下载量并在 Fast Gemma 挑战中获胜。

问题

Small teams cannot afford to pretrain competitive LLMs, but they need domain‑specific models that are trustworthy and efficient.

结果

The AI Foundry approach enabled an on‑device model with 1.18M cumulative downloads and a first‑place finish in the Google x Hugging Face Fast Gemma Challenge (510.58 tok/s).

工作流

• 1. Gather domain requirements from the customer.
• 2. Select complementary open models with desired strengths.
• 3. Measure per‑capability deltas using behavioral, representation, and supply‑chain probes.
• 4. Recombine or distill models along axes where each parent excels.
• 5. Perform safety and reliability diagnostics (hallucination, jailbreak resistance, tool‑calling determinism, hidden‑state checks, serialization safety).
• 6. Quantize and optimize for on‑device or on‑prem deployment.
• 7. Validate the final model against benchmarks and real‑world usage metrics.
• 8. Deploy and monitor for drift, re‑heal if necessary.

证据

An on‑device series built this way passed 1.18M cumulative downloads on Hugging Face, and our verified entry took first place in the Google x Hugging Face Fast Gemma Challenge (510.58 tok/s, verified track, 2026-08-02).

来源

The row people skip is the fourth. Anyone can fine‑tune. Very few can tell you whether the resulting model is trustworthy, with numbers.

来源