Full Ownership
The fine-tuned weights are yours, exported in your format of choice. No vendor lock-in, no per-token royalty — just a model you control end to end.
/ FINE-TUNING
Customise foundation models on your data
Tscale Fine-Tuning lets you adapt open-source and proprietary foundation models to your domain, your voice, and your workflows — on dedicated GPU clusters with full ownership of the resulting weights.
10X Faster Convergence
LoRA, QLoRA, and DeepSpeed-accelerated training cut convergence time by an order of magnitude compared to vanilla full fine-tuning — without sacrificing quality.
Any Foundation Model
Fine-tune Llama 3, Mistral, Mixtral, Qwen, Phi, Gemma, and domain-specific models — all from a single managed interface.
/ TUNING METHODS
Pick the right method for your model size
From parameter-efficient LoRA adapters to full-rank fine-tuning, Tscale supports the entire spectrum of training techniques. Mix and match to balance cost, speed, and quality — without rewriting your pipeline.
/ DATA PIPELINE
From raw data to training-ready
The quality of a fine-tuned model is bounded by the quality of its data. Tscale’s managed pipeline handles ingestion, cleaning, augmentation, and version control — so your team can focus on prompt design, not preprocessing.
- Multi-format ingest — JSONL, CSV, Parquet, raw HTML, PDF, or HuggingFace Hub imports.
- Built-in PII redaction — automatically strip emails, names, and sensitive tokens before training.
- Synthetic augmentation — generate edge cases and rare-class examples using your favourite foundation model.
- Dataset lineage — every example is traceable to a source commit, so audits are one click away.
/ TUNING STACK
Built on a production-grade stack
Tscale’s fine-tuning infrastructure is the same stack we use to train our own models — battle-tested at scale, open by default, and compatible with the tools your team already uses.
Frameworks
- HuggingFace Transformers
- TRL (DPO, PPO, SFT)
- PEFT (LoRA, QLoRA, IA³)
- DeepSpeed + ZeRO
- Axolotl
- Unsloth
Hardware
- NVIDIA H100 · H200
- NVIDIA A100 80GB
- NVIDIA L40S
- AMD MI300X
- NVLink & InfiniBand fabric
Base Models
- Llama 3 · 8B / 70B
- Mistral · Mixtral 8x7B
- Qwen 2 · 72B
- Phi-3 · Gemma 2
- Domain-specific models
Data Connectors
- S3-compatible Object Storage
- HuggingFace Hub
- PostgreSQL / MySQL
- Notion & Confluence
- Custom Webhooks
Workflow
- Managed Slurm
- Ray Train
- Weights & Biases
- MLflow Tracking
- Git-backed Recipes
Export & Deploy
- Safetensors / GGUF
- One-click Deploy
- Inference Endpoints
- vLLM Production
- HuggingFace Hub Push
Performance
Convergence in hours, not weeks
LoRA + DeepSpeed + NVLink fabric converges 10x faster than vanilla training stacks.
Train 70B models on a single GPU
QLoRA + 4-bit quantisation drops the hardware footprint — and the bill — by 70%.
Measurable quality lift
On average, fine-tuned models score 12 points higher on MMLU than their base counterparts.
Weights are yours, forever
Export in any format, host anywhere. No per-token royalty, no vendor lock-in.
End-to-end customisation
Supervised Fine-Tuning
The classic recipe: instruction-response pairs, custom tokenisers, and full SFT pipelines — managed end to end by Tscale’s orchestration layer.
Learn MoreAlignment & Preference Tuning
DPO, PPO, and RLHF pipelines for aligning models to brand voice, safety policies, and human preferences — with reward models and evaluators built in.
Learn MoreFrom weights to production
Once your model is fine-tuned, the rest of the Tscale stack takes over. Serve it, test it, monitor it — all on the same GPU fabric you trained it on.