Enterprise LLM Testing & Fine-Tuning
Ensure your language models are accurate, domain-specialized, and immune to jailbreaks. We provide comprehensive benchmark evaluation, adversarial red-teaming, and LoRA/DPO fine-tuning services.
Rigorous Empirical Evaluation for Mission-Critical AI Deployments
Deploying an unvalidated LLM into production exposes your brand to hallucinations, toxic outputs, and costly prompt injection vulnerabilities. BigIntend applies scientific evaluation frameworks, automated regression testing suites, and preference alignment (DPO/RLHF) to guarantee enterprise readiness.
Adversarial Immunity
Comprehensive red-teaming protecting against prompt injections and jailbreaks.
Empirical Benchmarking
Standardized MMLU, GSM8K, and custom domain accuracy scoring.
Targeted LoRA Tuning
Fast domain adaptation with minimal GPU compute and zero catastrophic forgetting.
Our LLM Testing & Fine-Tuning Capabilities
Supervised Fine-Tuning (SFT & LoRA)
Fine-tune foundation models (LLaMA 3, Mistral, Qwen) using LoRA, QLoRA, and FlashAttention on proprietary datasets.
- Parameter-Efficient Adapter Training
- Custom Domain Vocabulary & Format Adherence
Direct Preference Optimization (DPO)
Align model behavior with human preferences and domain standards without the instability of complex RLHF reward models.
- Pairwise Response Preference Optimization
- Tone, Conciseness & Politeness Alignment
Adversarial Red-Teaming & Security Audits
Exhaustive stress-testing attempting prompt injections, system prompt extraction, PII leaks, and toxic bypasses.
- Automated Attack Vector Simulations (PyRIT / Garak)
- Detailed Threat Matrix & Remediation Report
Automated Benchmark & Regression Suites
Continuous evaluation suites measuring precision, recall, hallucination rate, and latency across thousands of test cases.
- Ragas & DeepEval Golden Dataset Benchmarking
- Automated CI/CD Model Evaluation Pipelines
RAG Pipeline Retrieval Evaluation
Quantify retrieval relevance, context precision, and answer faithfulness to isolate retrieval failures from generation errors.
- Context Recall & Faithfulness Scoring
- Embedding Model Comparison Benchmarks
Inference Latency & Quantization Benchmarks
Measure token generation speed (tokens/sec), time-to-first-token (TTFT), and memory footprint across INT4, INT8, and FP16 formats.
- Multi-Concurrency GPU Load Stress Testing
- Optimal VRAM Allocation Profiling
Evaluation Frameworks, Tuning Tools & Models
Our 5-Step LLM Testing & Tuning Lifecycle
Evaluation Dataset & Golden Benchmark Creation
We curate representative question-answer pairs and establish domain-specific scoring metrics.
Baseline Model Profiling & Red-Teaming
We run automated vulnerability scans and benchmark off-the-shelf baseline performance.
Instruction Fine-Tuning & DPO Alignment
We train LoRA adapters and apply Direct Preference Optimization on curated domain datasets.
Regression Testing & Safety Verification
We test the fine-tuned model against golden benchmarks to ensure zero catastrophic forgetting.
Production Quantization & Deployment
We export optimized quantized weights (GGUF / AWQ) and deploy scalable inference endpoints.
Frequently Asked Questions
Test & Fine-Tune Your LLMs
Schedule a session with our Model Evaluation Engineers to audit your LLM safety and accuracy benchmarks.