AI Services
I will fine tune open source LLMs and train custom AI models with LoRA and QLoRA
Trueet Network
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About This Gig
Build Proprietary AI IP With Domain-Specific LLM Fine-Tuning
Prompt engineering alone cannot teach an AI model proprietary industry jargon, niche legal frameworks, or specialized coding standards. At Trueet Network, we fine-tune cutting-edge open-source Large Language Models (Llama 3.1, Mistral, Gemma 2, DeepSeek) using parameter-efficient fine-tuning (PEFT / LoRA / QLoRA).
We convert your raw business data into structured training sets, optimize hyperparameters, evaluate against baseline benchmarks, and deliver a production-ready model adapter or quantized GGUF/vLLM package.
What Our Engineering Pipeline Delivers:
- Dataset Curation & Formatting: Cleaning, deduplication, synthetic data generation, and JSONL instruction-tuning formatting.
- Parameter-Efficient Fine-Tuning: QLoRA 4-bit/8-bit quantization maximizing training efficiency without quality degradation.
- Benchmarking & Evaluation: BLEU, ROUGE, and custom LLM-as-a-judge scoring against base models.
- Quantization & Optimization: GGUF, AWQ, and EXL2 model exports for ultra-fast local or cloud inference.
- Production Inference Serving: vLLM or Ollama endpoint deployment on RunPod, AWS, or local GPU servers.
Why Choose Trueet Network?
- Enterprise-grade PyTorch and Hugging Face expertise.
- Complete ownership of training checkpoints, LoRA adapters, and weights.
- Strict confidentiality for all training datasets.
Frequently Asked Questions
Q: How much training data is required to fine-tune an LLM?
For instruction and style adaptation, 1,000 to 5,000 high-quality question-answer pairs produce remarkable results. For deep domain knowledge, larger datasets or hybrid RAG pipelines are recommended.
Q: Do I own the fine-tuned model weights?
Yes, 100%. All LoRA adapters, merged Hugging Face safetensors, and quantized GGUF weights belong completely to your company.
Q: Can the fine-tuned model be run locally without cloud API fees?
Yes! We quantize the model to GGUF format so it can run locally on an M-series Mac or single consumer GPU using Ollama or LM Studio.
Q: Who covers the GPU cloud compute costs during training?
GPU training compute (e.g. on RunPod or Lambda Labs) is billed directly to your compute account or provided as a transparent pass-through cost based on model size.
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