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Model fine-tuning & customization

Fine-tune and evaluate models for your tasks

Fine-tune 32B-class open models and customize branded visual models, from instruction data and training to evaluation, quantization and inference integration. Assess feasibility against the model, hardware and workload.

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ASCEND / BUSINESS EXPERIENCEILLUSTRATIVE
From training data to inference
Data & baselines

Instruction data · Held-out evaluation

Train & compare

QLoRA · SFT · DPO

Export & integrate

Quantized artifacts · vLLM / llama.cpp

BUILT AROUND THE WORK

When this service fits

When a general model does not fit your terminology, format or style

Review samples, baseline performance and the operating environment. Identify which problems training can address, then agree evaluation methods and deliverables.

  • General models miss domain terminology
  • Style or output format is inconsistent
  • You need control over deployment and ongoing cost

TECHNICAL CAPABILITIES & SCOPE

What the engineering covers

Choose methods to fit your existing equipment, systems and project requirements.

Language model training & evaluation

Start with a baseline and representative business tasks, then use training where the expected improvement justifies the work.

  • Instruction dataset construction, cleaning, task balance and held-out evaluation
  • SFT supervised fine-tuning and DPO preference alignment
  • QLoRA 4-bit quantization, gradient checkpointing and gradient accumulation
  • Before/after benchmarks and checks for catastrophic forgetting

Model delivery & branded visual adaptation

Deliver a model that fits the intended serving environment, with evaluation records and a path to repeatable updates.

  • GPTQ, AWQ or GGUF export matched to the serving requirements
  • Inference integration with vLLM or llama.cpp
  • Brand visual LoRA using Kohya_ss, ComfyUI and FLUX/SDXL workflows
  • Image annotation, training comparisons and style-consistency review

DELIVERABLES

What you receive

Agree the deliverables and acceptance criteria before work starts.

Dataset and evaluation package

Agreed training/evaluation splits, labeling rules, data permissions and baseline results.

Model and training assets

Model or adapter outputs, versioned training configuration and reproducibility guidance.

Comparative evaluation report

Task-level quality, regressions, resource needs and limitations, not just a single aggregate score.

Inference and update handover

Compatible model exports, inference integration and instructions for later dataset/model updates.

Implementation and handover

Review the working result at each stage before agreeing further work.

  1. Prepare data and baselines

    Review inputs, tasks, hardware and deployment constraints. Prepare training and held-out evaluation materials and agree acceptance scope.

  2. Train and compare

    Train and customize within the agreed scope. Compare before-and-after results on representative tasks, then review differences before the next stage.

  3. Export, verify and transfer

    Record evaluation and inference integration results. Transfer the agreed models, configuration and operating instructions with clear maintenance responsibilities.

DEPLOYMENT & HANDOVER

Deployment, handover and ongoing responsibilities

Private delivery and existing identities

Deliver into the agreed customer environment, with AD / LDAP / OIDC integration where required. Define data migration, staged transition and any external-service dependencies before implementation.

Source-level assets and independent operation

For development work, hand over source, dependencies, deployment configuration, CI/CD definitions, interface/data documentation and operating guides. Usage, redevelopment rights and independent-build acceptance are agreed in the contract.

ACCEPTANCE

Agree what completion means

  • The model is evaluated on held-out representative tasks against the agreed baseline.
  • Domain improvements and general-capability regressions are reported separately.
  • The delivered export runs in the agreed inference environment.
  • Visual-model projects pass the agreed brand and cross-batch consistency review.
Discuss your acceptance criteria

BEFORE WE START

A few practical questions

Should we fine-tune or start with retrieval?

For answers grounded in changing company documents, evaluate retrieval first. For task behavior, tone or output structure, compare fine-tuning against a baseline on representative tasks. Confirm data rights, model licensing and evaluation criteria before training.

How are cost, timing and performance targets agreed?

Review the existing environment and representative tasks first. Then agree milestones, expected scale, acceptance criteria and exclusions. Cost, timing and performance targets depend on that scope and the test conditions.

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