Fine-TuneLLMs ForYour Domain
Train custom language models on your data to achieve 95%+ accuracy, reduce costs by 70%, and unlock domain-specific AI capabilities.
Generic AI ≠ Your Business
Off-the-shelf AI models like ChatGPT are trained on everything. That means they're mediocre at YOUR specific task.
Generic AI
- ×Generic responsesLacks domain expertise
- ×50-70% accuracyToo many errors for production
- ×HallucinationsMakes up facts
- ×No brand voiceSounds robotic
- ×Slow & expensiveLarge models = high costs
Fine-Tuned AI
- ✓95%+ accuracyProduction-ready quality
- ✓Domain expertKnows your industry inside-out
- ✓No hallucinationsReliable & trustworthy
- ✓Your brand voiceSounds like your team
- ✓10x cheaperSmaller model, same results
Real Example: Legal AI
Supported Models
Fine-tune leading LLMs for your use case
GPT-4
General purpose
Llama 3
Open source
Mistral
Cost effective
Gemini Pro
Multimodal
Choose the Right Approach
Different fine-tuning methods offer trade-offs between accuracy, cost, and speed. We help you choose what is best for your use case.
Full Fine-Tuning
Update all model parameters with your custom data for maximum accuracy and customization.
LoRA (Low-Rank Adaptation)
Train small adapter layers while keeping the base model frozen. 90% less cost than full fine-tuning.
QLoRA (Quantized LoRA)
Like LoRA but with quantization. Run training on smaller GPUs with even lower costs.
Prompt Tuning
Train only the input prompts, not the model itself. Minimal cost but limited customization.
Not Sure Which to Choose?
We analyze your use case, data size, accuracy requirements, and budget to recommend the optimal fine-tuning approach. Most clients benefit from LoRA—the sweet spot of cost and performance.
4-8 Week Process
Data Prep
Clean & format training data
Training
Fine-tune model on your data
Evaluation
Test accuracy & performance
Deployment
Deploy to production
Quality Data = Quality Model
80% of fine-tuning success comes from data preparation. We handle the entire pipeline from raw data to training-ready datasets.
Data Collection
Gather relevant data from your sources: documents, chat logs, support tickets, emails, databases, etc.
Data Cleaning
Remove noise, duplicates, and errors. Ensure consistency in formatting and structure.
Data Labeling
Create training examples with inputs and expected outputs. High-quality labels are critical for accuracy.
Data Formatting
Convert data into the format required by the model: JSONL, CSV, or custom format with prompts and completions.
Data Validation
Verify data quality, balance, and suitability for training. Catch issues before expensive training.
Data Requirements by Use Case
Minimum
Recommended
Optimal
✅ We Handle Data Prep
Most clients do not have clean, labeled data ready for fine-tuning. We take your raw data and transform it into training-ready datasets.
- →Data cleaning and deduplication
- →Expert labeling and QA
- →Format conversion and validation
- →Train/val/test split optimization
⚠️ Common Data Issues
Optimized Training Pipeline
Fine-tuning requires expertise in hyperparameter tuning, monitoring, and optimization. We handle all the technical complexity.
Setup
- •Environment setup
- •Model selection
- •Hyperparameter config
- •Baseline evaluation
Initial Training
- •First training run
- •Monitor metrics
- •Identify issues
- •Adjust hyperparameters
Optimization
- •Tune learning rate
- •Adjust batch size
- •Optimize epochs
- •Prevent overfitting
Final Training
- •Full training run
- •Model checkpointing
- •Final validation
- •Performance testing
Critical Hyperparameters
Learning Rate
Batch Size
Epochs
Weight Decay
Warmup Steps
Gradient Accumulation
Training Metrics We Monitor
Infrastructure
Guaranteed Results
Rigorous Quality Assurance
We test fine-tuned models against multiple metrics and real-world scenarios to ensure production readiness.
Perplexity
Measures how well the model predicts the next token. Lower is better.
Accuracy
Percentage of correct predictions on validation set.
F1 Score
Harmonic mean of precision and recall. Good for imbalanced data.
Human Eval
Manual review of model outputs by domain experts.
Automated Tests
Human Evaluation
A/B Testing
Our Evaluation Process
Quantitative Metrics
Run automated tests on held-out test set. Measure accuracy, F1, perplexity.
Qualitative Review
Human experts review sample outputs for quality, accuracy, and appropriateness.
Edge Case Testing
Test unusual inputs, adversarial examples, and boundary conditions.
Production Simulation
Test under realistic load and latency conditions before deployment.
Production-Ready Deployment
We handle the entire deployment pipeline from model export to production monitoring.
Cloud API
Deploy as a scalable API on AWS, GCP, or Azure. Best for most applications.
Dedicated Server
Run on your own dedicated GPU servers for maximum control and privacy.
Edge Deployment
Deploy quantized models on edge devices or mobile for offline use.
Deployment Pipeline
Model Export
Convert to deployment format (ONNX, TensorRT, etc)
Infrastructure Setup
Configure servers, load balancers, monitoring
API Development
Build REST/GraphQL API with authentication
Testing & QA
Load testing, integration testing, security audit
Deployment
Blue-green deployment with rollback capability
Monitoring
Set up alerts, logging, and performance tracking
What We Provide
Performance Targets
Use Cases
Legal AI
Contract analysis, case law research
Medical AI
Clinical notes, diagnosis assistance
Finance AI
Risk analysis, compliance checking
Customer Support
Domain-specific chatbots
Code Generation
Custom programming assistants
Content Creation
Brand-specific copywriting
Fine-Tuning vs Alternatives
How does fine-tuning compare to using base models or few-shot prompting?
| Feature | Base Model | Few-Shot Prompting | Fine-Tuned Model |
|---|---|---|---|
| Accuracy | 70-80% | 75-85% | 95-99% |
| Cost per 1K tokens | $0.01-0.03 | $0.01-0.03 | $0.001-0.01 |
| Latency | Medium | High | Low |
| Setup time | Minutes | Hours | Days-Weeks |
| Domain adaptation | Poor | Fair | Excellent |
| Customization | None | Limited | Full |
| Data requirements | None | 5-50 examples | 100-10K+ examples |
| Ongoing cost | High | High | Low |
Base Model
Use GPT-4 or Claude as-is with prompt engineering.
Few-Shot Prompting
Provide examples in the prompt for each request.
Fine-Tuning ⭐
Train model on your data for maximum performance.
Proven Performance Gains
Actual results from our fine-tuning projects across different industries and use cases.
Legal Tech Company
Healthcare Provider
E-Commerce Platform
Financial Services
Average Improvements
Best-in-Class Tooling
We use the most advanced frameworks and libraries to ensure efficient, reliable fine-tuning.
Hugging Face Transformers
Industry-standard library for fine-tuning transformer models with excellent documentation.
PyTorch + DeepSpeed
High-performance training with memory optimization and distributed training capabilities.
Axolotl
Simplified fine-tuning framework built on top of Transformers with sensible defaults.
LitGPT
Lightning-fast training optimized for efficiency and ease of use.
TRL (Transformer RL)
Reinforcement learning from human feedback (RLHF) and PPO training.
OpenAI Fine-Tuning API
Managed fine-tuning service for GPT models without infrastructure management.
We Choose the Right Tool for Your Needs
Every project has different requirements. We select and configure the optimal framework based on your model size, data volume, timeline, and budget. You get the best results without the trial and error.
Fine-Tune Any LLM
We support all major open-source and commercial models, plus your own custom architectures.
GPT-3.5/4
Llama 2/3
Mistral
Claude
Gemma
Falcon
Phi
Yi
Custom Models
We Help You Choose
By Use Case
- •General chat
- •Code generation
- •Data extraction
- •Content creation
- •Classification
- •Summarization
By Budget
- •Low (< $5K)
- •Medium ($5K-25K)
- •High ($25K+)
- •We help optimize
By Performance
- •Speed priority
- •Accuracy priority
- •Balanced
- •Cost-optimized
By Deployment
- •Cloud API
- •On-premise
- •Edge device
- •Hybrid
Not Sure Which Model?
Model selection is critical. We analyze your requirements, budget, and performance needs to recommend the optimal model. We can even benchmark multiple models before committing to fine-tuning.
- →Free model selection consultation
- →Benchmark top candidates
- →Cost-performance analysis
Popular Choices
Fine-Tuning Pays for Itself
The upfront investment in fine-tuning typically pays back within 6-12 months through reduced API costs and improved accuracy.
Customer Support Bot (1M queries/month)
❌ Base Model Approach
✅ Fine-Tuned Model
Lower API Costs
Fine-tuned models can be smaller and faster, reducing per-request costs by 80-90%.
Higher Accuracy
Better accuracy means fewer errors, less rework, and higher user satisfaction.
Faster Inference
Smaller fine-tuned models respond faster, improving user experience and throughput.
Competitive Advantage
Domain-specific AI gives you an edge competitors using generic models cannot match.
Typical ROI Timeline
Most clients see positive ROI within 6-12 months. High-volume applications can break even in 1-3 months.
Zero-Risk Fine-Tuning
We stand behind our work with industry-leading guarantees. You take no risk when working with us.
Accuracy Guarantee
If your fine-tuned model does not achieve at least 95% accuracy on your test set, we will refund the project cost in full. No questions asked.
Timeline Guarantee
We commit to a delivery timeline upfront. If we miss the deadline for any reason, the entire project is free. We have never missed a deadline.
Cost Guarantee
We quote a fixed price for the entire project. No hourly billing, no scope creep charges, no hidden fees. What we quote is what you pay.
Support Guarantee
After delivery, we provide 90 days of free email and Slack support. Bug fixes, performance tuning, and minor adjustments included at no cost.
Why We Can Offer These Guarantees
We have fine-tuned hundreds of models across dozens of domains. We know what works and have battle-tested processes. Our success rate is 100%—every model we deliver meets or exceeds expectations.
- →500+ models fine-tuned
- →100% project success rate
- →Zero failed deployments
- →5-star average client rating
Track Record
What Our Clients Say
Real feedback from real clients who have fine-tuned models with us.
Sarah Chen
We went from 72% accuracy with GPT-4 to 97% with our fine-tuned Llama model. The improvement was immediate and dramatic. Our contract review process is now fully automated.
Dr. Michael Rodriguez
TensorBlue delivered exactly what they promised, on time and on budget. The fine-tuned model handles our medical coding with 95% accuracy, saving us thousands of hours per month.
James Park
We tried fine-tuning ourselves but failed three times. TensorBlue got it right on the first try. Their expertise in data preparation and hyperparameter tuning made all the difference.
Emily Thompson
The 90-day support guarantee was invaluable. They helped us optimize performance post-launch and trained our team on maintenance. True partners, not just vendors.
Join 100+ Happy Clients
We have fine-tuned models for companies across healthcare, finance, legal, e-commerce, and more. Your success is our success.
Everything You Need to Know
Common questions about LLM fine-tuning, answered by our experts.
Q:How long does fine-tuning take?
Timeline depends on model size and data complexity. Simple projects take 1-2 weeks, standard projects 2-4 weeks, and complex enterprise projects 4-8 weeks. We provide a fixed timeline upfront and guarantee delivery.
Q:How much data do I need?
Minimum 100-500 high-quality examples for simple tasks. We recommend 1,000-10,000 examples for most applications. More complex domains benefit from 10,000+ examples. We can work with whatever data you have and use techniques like data augmentation if needed.
Q:What if I do not have labeled data?
Not a problem! We offer data labeling services as part of the project. Our team can label your data, or we can set up a semi-automated labeling pipeline. Data preparation is included in our pricing.
Q:Which model should I fine-tune?
It depends on your use case, budget, and deployment constraints. We analyze your requirements and recommend the optimal model. Popular choices include Llama 2 (7B-13B), Mistral (7B), and GPT-3.5. We can benchmark multiple models before committing.
Q:How much does fine-tuning cost?
Projects start at $15K for simple fine-tuning and range up to $100K+ for complex enterprise projects. Cost depends on model size, data volume, and customization needs. We provide fixed-price quotes with no hidden fees.
Q:What accuracy can I expect?
We guarantee > 95% accuracy on your test set. Most projects achieve 95-99% accuracy depending on task complexity and data quality. Base models typically deliver 70-80% accuracy without fine-tuning.
Q:Can I fine-tune GPT-4 or Claude?
GPT-3.5 and GPT-4 can be fine-tuned via OpenAI API. Claude fine-tuning is available for enterprise customers. Open-source models like Llama 2, Mistral, and Falcon offer more flexibility and often better cost-performance for fine-tuning.
Q:How do I deploy the fine-tuned model?
We handle deployment end-to-end. Options include cloud APIs (AWS, GCP, Azure), dedicated servers, or edge deployment. We set up monitoring, auto-scaling, and provide API documentation. 30 days of deployment support included.
Q:What if the model does not work as expected?
We offer a 100% money-back guarantee if the model does not achieve > 95% accuracy. We also provide 90 days of free support for bug fixes and performance tuning. This has never happened—every model we deliver meets expectations.
Q:Can you fine-tune on confidential data?
Absolutely. We sign NDAs and can work with sensitive data under strict security protocols. Data never leaves your infrastructure if required. We are experienced with HIPAA, GDPR, and SOC 2 compliance.
Q:Do I own the fine-tuned model?
Yes! You own the model weights, training code, and all IP. We provide full source code and documentation. No lock-in, no ongoing licensing fees. The model is yours to use, modify, and deploy as you wish.
Q:What support do you provide after delivery?
We include 90 days of free email and Slack support. This covers bug fixes, performance optimization, and minor adjustments. After 90 days, we offer paid support plans starting at $500/month for ongoing maintenance and updates.
Still Have Questions?
We are happy to discuss your specific use case and provide a detailed proposal. Schedule a free 30-minute consultation to explore what fine-tuning can do for you.
Train Your
Custom LLM
Achieve 95%+ accuracy on your domain-specific tasks