CatalogBrowse the open models we fine-tune →
Artificial intelligence, trained on your data.
We fine-tune Llama, Qwen, FLUX, and Stable Diffusion on your data, with evaluation, license review, and deployment in AWS, Modal, or your own VPC.
Milestone-based delivery, from dataset review to handoff of the trained weights.
What we train
Language, vision-language, image, video, embeddings, and speech: open-weight models you can host.
How we train
SFT, LoRA, QLoRA, preference tuning, and diffusion adapters, with versioned configs.
What you get
Weights, evaluation reports, and deployment guides for your environment, not lock-in on our infra.
How we work
Dataset → Train → Ship
From raw data to evaluated weights, in three stages.
Dataset
Ingestion, cleaning, captioning, PII handling, and train, validation, and test splits, with quality reports your team can review.
Dataset cards document sources, filters, and splits.
Train
Supervised fine-tuning, LoRA, preference tuning, and diffusion training on NVIDIA GPUs through AWS and Modal, with versioned configs.
Every run can be reproduced from its recorded config.
Ship
Weights, evaluation reports, and deployment guides for your VPC, Modal endpoints, SageMaker, or private Hugging Face repositories.
Delivered with runbooks for your MLOps team.
Model families
Open models we fine-tune
We review model licenses on every engagement. License notes on these cards are summaries, not legal advice.
Llama 3.1 / 3.2 / 3.3
General-purpose instruct models from 1B to 70B parameters
Llama Community License; acceptable-use terms apply
Mistral / Mixtral / Ministral
Dense and mixture-of-experts models for efficient instruction tuning
Apache 2.0 for most releases; some variants use Mistral licenses
Qwen2.5 / Qwen3
Multilingual, coding, and long-context workloads
Apache 2.0 for most sizes; some Qwen2.5 sizes use the Qwen license
Gemma 2 / Gemma 3
Compact models for cost-sensitive and on-device serving
Gemma Terms of Use
Phi-3 / Phi-4
Small models for edge deployment and tight latency budgets
MIT
DeepSeek-R1 distills
Reasoning-oriented models from 1.5B to 70B parameters
MIT; distills also follow their Qwen or Llama base terms
gpt-oss-20b / 120b
Open-weight reasoning models for private deployment
Apache 2.0 with a usage policy
OLMo 2
Open data, code, and training recipes for reproducible work
Apache 2.0
Applications
Use cases we build for
Enterprise knowledge assistant
Grounded answers from wikis, SOPs, and tickets.
SaaSCustomer support copilot
On-brand reply drafts with clear escalation rules.
FinanceContract and compliance review
Clause extraction for attorney review.
HealthcareHealthcare administration
Note summarization on de-identified data.
FinanceFinancial research and reporting
Earnings and portfolio Q&A on licensed data.
SaaSCode assistant for private repositories
Completion that knows your internal APIs.
Process
From discovery to handoff
Typical durations. Actual timelines depend on data readiness and project scope.
- 01
Discover
1–2 weeks
Scope, success metrics, license review, and data assessment.
- 02
Dataset
2–6 weeks
Cleaning, captioning, annotation, privacy review, and data splits.
- 03
Train
1–4 weeks
Fine-tuning runs with tracked configs, checkpoints, and interim evaluations.
- 04
Evaluate
1–2 weeks
Benchmarks, red-team testing, and human review where needed.
- 05
Handoff
1 week
Weights, documentation, and optional inference setup.
Ready to train on your data?
Share your goals, data, and timeline. We will scope a milestone plan before any training begins.