Model fine-tuning for production

R404 fine-tunes LLMs and video generators with LoRA-based pipelines.

We help teams adapt foundation models to their data, brand voice, and domain constraints—without the overhead of full retraining. From instruction tuning and RAG optimization to diffusion/video personalization, R404 delivers measurable gains in quality, latency, and cost.

LoRA / QLoRA LLM alignment Diffusion & video Deployment-ready

Services

End-to-end model adaptation: data → training → evaluation → deployment.

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LLM fine-tuning (SFT + alignment)

Instruction tuning, domain adaptation, and preference optimization to improve helpfulness, accuracy, and tone—tailored to your product and users.

Video generator personalization

LoRA adapter training for diffusion/video models to match character identity, style, brand assets, and creative direction—while preserving temporal consistency.

Inference optimization

Quantization, batching, KV caching strategies, and serving setups that reduce latency and cost without sacrificing output quality.

Evaluation & safety

Task-specific evals, regression tests, and guardrails (policy, PII, jailbreak resistance) to keep releases reliable as you iterate.

Data preparation

Labeling guidance, dataset curation, de-duplication, toxicity filtering, and prompt-response formatting for clean, trainable corpora.

Integrations & MLOps

Reproducible training pipelines, experiment tracking, model registry workflows, and deployment handoff to your cloud stack.

Our approach

A practical playbook designed for shipping—fast experiments, strong evaluation, reliable deployment.

1) Define success

We translate your goal into measurable metrics: accuracy, style adherence, latency, cost, and safety.

  • Target outputs and failure modes captured up front.
  • Baseline established with prompts and evaluation sets.
  • Release criteria agreed before training begins.

2) Build the right adapters

We fine-tune using LoRA/QLoRA to adapt models efficiently, enabling quick iterations and controlled updates.

  • LoRA ranks and target modules chosen per architecture.
  • Data hygiene to prevent overfitting and leakage.
  • Checkpoints validated against eval regressions.

3) Evaluate & harden

We stress test: edge cases, safety prompts, adversarial inputs, and performance constraints.

  • Automated evals + human review on critical slices.
  • Safety filters and refusal behavior tuned as needed.
  • Monitoring plan for post-launch drift and quality.

4) Deploy with confidence

We package adapters, provide reproducible configs, and help integrate into your inference stack.

  • Quantization and serving parameters tuned for cost/latency.
  • Fallbacks and rollbacks defined for safe releases.
  • Documentation for ongoing internal iteration.

Use cases

Common outcomes teams hire R404 for.

Support & ops copilots

Improve resolution accuracy, reduce hallucinations, and match your internal SOPs and tone of voice.

Sales enablement

Product-aware assistants that speak your catalog, pricing rules, and compliance constraints.

Document intelligence

Extraction, classification, and summarization tuned to your templates and domain vocabulary.

Brand-consistent generation

Style-locked text and visual outputs aligned to your brand guidelines and asset library.

Creator workflows

Video generation adapters for characters and scenes, tuned for repeatable creative direction.

Domain expertise

Specialized models for law, finance, medicine, engineering, and other high-precision fields.

Security & privacy

We treat your data as sensitive by default and align on requirements before any transfer.

Data handling

Minimal access, clear retention windows, and secure transfer paths. We can support client-managed storage and isolated training environments when needed.

  • Least privilege access controls for team members.
  • Retention policies documented per engagement.
  • Private runs available on your cloud/VPC.

Responsible outputs

We add guardrails for safety-critical applications and establish evaluation gates to reduce harmful, biased, or non-compliant generations.

  • Safety tests included in eval suites.
  • Red-team prompts for robustness checks.
  • Audit trail for training configs and datasets.

Let’s fine-tune your next model.

Tell us your goal, data readiness, and target platform. We’ll recommend the best approach (LoRA/QLoRA, full fine-tune, or RAG) and outline a clear delivery plan.