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I will deploy hugging face models, fine tune llama, mistral, gemma on gcp
Awais Naeem
About this gig
Looking to deploy or fine-tune open-source LLMs like Llama 3, Mistral, or Gemma on your own cloud infrastructure?
I specialize in custom Hugging Face model integration, QLoRA fine-tuning, containerization, and production-ready deployments on GCP, AWS, and inference endpoints.
What I Offer:
- Model Deployment: Deploying Hugging Face models, Llama, Mistral, Gemma, and DeepSeek to GCP Vertex AI, AWS EC2/SageMaker, or Hugging Face Spaces.
- QLoRA Fine-Tuning: Parameter-efficient fine-tuning on custom datasets for specialized domain tasks.
- API Integration: Wrapping models in high-performance FastAPI/Flask REST endpoints with JWT security.
- Docker & Infrastructure: Containerizing AI models for scalable cloud inference.
Tech Stack: Hugging Face Transformers, PEFT/LoRA, PyTorch, Llama 3, Mistral, Gemma, GCP Vertex AI, Docker, FastAPI, Python.
Why Choose Me?
- Clean, optimized inference setups.
- 100% data privacy & secure API keys.
Please message me before ordering to discuss model parameters and cloud hosting setup!
Get to know Awais Naeem
Awais Naeem
AI and Machine Learning Engineer
- FromPakistan
- Member sinceOct 2024
- Avg. response time1 hour
- Last delivery8 months
Languages
Urdu, English
I am an AI & Machine Learning Engineer with 7+ years of experience in real-world automation, from AI agents to backend systems
I help when teams are stuck in manual work, tools don’t integrate or AI feels unclear.
I have built:
• AI agents using LLMs (OpenAI, LLaMA, RAG pipelines)
• Automation Agents using n8n, OpenClaw, make.com
• Workflow automations (APIs, Python, backend systems)
• Chatbots that understand context, not just scripts
• Data + AI systems that turn raw data into decisions
I build systems that save time, cut costs and actually get used. Reach out if you're exploring AI.
My Portfolio
FAQ
1. Which open-source LLMs can you fine-tune or deploy?
I work with all major open-source architectures hosted on Hugging Face, including Llama 3/3.1, Mistral, Mixtral, Gemma 1/2, DeepSeek, Falcon, and Qwen models.
2. Where can you deploy the fine-tuned Hugging Face models?
I can deploy your models to GCP (Vertex AI, Compute Engine), AWS (SageMaker, EC2), Azure, Hugging Face Inference Endpoints, RunPod, or local GPU servers using Docker containers.
3. What fine-tuning techniques do you use to reduce costs?
I specialize in PEFT methods like QLoRA and LoRA using PyTorch and Hugging Face Transformers. This drastically reduces GPU VRAM requirements and training costs while maintaining high model performance.
4. Will I get a usable API endpoint to query my model?
Yes! For Standard and Premium packages, I wrap the model inside a high-performance FastAPI or Flask web service with endpoints ready to be integrated into your web app, mobile app, or backend workflow.
5. How do we handle large dataset transfers and cloud permissions?
You can share your dataset via secure cloud storage (Google Drive, S3 bucket, Hugging Face Datasets). For cloud deployments, you can grant temporary IAM service account access without sharing root account passwords.
