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I will fine tune your llm lora private deployment
Feichen
About this gig
Custom LLM fine-tuning (LoRA/QLoRA) for classification, sentiment, style transfer, and domain-specific tasks. I built a fact/opinion binary classifier using Qwen3-14B-4bit LoRA, achieving 99.7% accuracy. I run a private 4-node Mac Mini M4 cluster, so I fine-tune and deploy your model entirely on your own infrastructure - your data never leaves your control.
What I offer:
- LoRA/QLoRA fine-tuning on open-source models (Qwen, Llama, Mistral, etc.)
- FastAPI-based private deployment for local/on-premise use
- RAG pipelines for document Q&A over your private knowledge base
- Evaluation reports with accuracy, F1, and error analysis
- Iterative tuning until the model meets your target metrics
Why me:
- 99.7% benchmark result on a real classification project
- Data stays 100% in your environment - no third-party cloud, no leakage
- Fast, clear communication in English & Chinese
Before ordering: message me with your task description, dataset size and format, target language, and deadline, so I can confirm feasibility and delivery time before you place the order.
Get to know Feichen
Feichen
AI ML Engineer, LLM Fine tuning and Private Deployment Specialist
- FromChina
- Member sinceAug 2026
- Avg. response time1 hour
Languages
English, Chinese
AI/ML engineer specializing in LLM fine-tuning (LoRA/QLoRA) and private model deployment. Built a fact/opinion classification model via Qwen3-14B-4bit LoRA, achieving 99.7% accuracy on a 300-sample benchmark. I run a private 4-node Mac Mini M4 cluster - fine-tune and deploy your model locally, keeping your data 100% in your own infrastructure. Services: Custom LLM fine-tuning (LoRA/QLoRA), private/local model deployment & API serving (FastAPI), RAG pipelines, training data preparation & evaluation design. Fast, clear communication, English & Chinese.
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FAQ
What data do you need from me?
Labeled or unlabeled samples in CSV/JSON, or a task description.
Will my data stay private?
Yes, all work runs on my private local cluster, data never leaves your infrastructure.
What do I get after delivery?
Fine-tuned model weights, evaluation report, and deployment guide/API if applicable.
Which models do you fine-tune?
Popular open-source models such as Qwen, Llama, Mistral, and more, using efficient LoRA/QLoRA.
How long will it take?
Typical small to medium tasks are delivered in 3-7 days, depending on data volume and cluster load.
Can you handle non-English text data?
Yes, I can fine-tune multilingual models such as Qwen, BLOOM, or multilingual MiniLM for non-English classification and NLP tasks. Share your language and domain and I will set up the right model for you.
