AI/LLM Fine-Tuning Engineer – Arabic, Egyptian Arabic, Gulf Arabic & English Smart Home Model

@koolvishruth

Hello Sir, I am AI engineer and having extensive experience in LLM training, Optimisation.I can fine tune as mentioned.I have worked with 132+ clients. Let’s connect

@ZohaibRoy

⭐⭐⭐⭐⭐ Develop Smart Home AI Language Model for Arabic and English Control ❇️ Hi My Friend, I hope you are doing well. I've reviewed your project requirements and see you are looking for an experienced LLM/NLP Fine-Tuning Engineer. You don’t need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects for language model fine-tuning. I will create a specialized model that understands natural smart-home commands in Egyptian Arabic, Gulf Arabic, and English. This model will focus on identifying intent, devices, and actions, ensuring accurate output in structured JSON format. ➡️ Why Me? I can easily handle your LLM/NLP project as I have 5 years of experience in fine-tuning language models, specifically in Arabic NLP. My expertise includes SFT, LoRA/QLoRA, dataset creation, and Python programming. I also have a strong grip on evaluation methods and benchmarking, ensuring the model meets your needs effectively. ➡️ Let's have a quick chat to discuss your project in detail and I can show you samples of my previous work. I look forward to discussing this with you! ➡️ Skills & Experience: ✅ LLM Fine-Tuning ✅ Natural Language Processing ✅ Arabic NLP/NLU ✅ Dataset Creation ✅ Python Programming ✅ SFT & LoRA/QLoRA ✅ JSON Output Structuring ✅ Model Evaluation ✅ Benchmarking Techniques ✅ Smart Home Technologies ✅ Intent Recognition ✅ Device Control Waiting for your response! Best Regards, Zohaib

@adriank06

Hi, I specialize in LLM fine-tuning with LoRA/QLoRA. For this project: Approach: Base model: Qwen2.5 or AraT5v2 — strong Arabic coverage, efficient quantization for edge deployment Fine-tuning: QLoRA (4-bit) for memory efficiency on MEGA hardware, SFT on intent/entity/action classification Dataset: synthetic + annotated smart-home commands in Egyptian Arabic, Gulf Arabic, and English (~5,000-10,000 samples) Output: strict JSON schema (intent, device, location, action, parameters) with clarification triggers for ambiguous inputs Evaluation: intent accuracy, entity F1, out-of-scope detection rate Timeline: 3-4 weeks. My quote: $400-600 depending on dataset size and target accuracy. What hardware specs does MEGA run on — RAM, GPU/NPU? That determines quantization strategy and final model size.

@Tanmoy236

Warm greetings! I specialize in AI/LLM development and structured NLU systems, building models that convert natural language into reliable, machine-readable commands. Our 62-member professional team brings extensive AI engineering expertise to specialized automation projects. Here's how I can help: * Develop an Arabic/English NLU pipeline for Egyptian Arabic, Gulf Arabic, and English smart-home commands. * Apply SFT with LoRA/QLoRA, using curated and annotated datasets for intent, device, location, action, and parameter extraction. * Implement strict JSON output, ambiguity detection, clarification handling, and unsupported-command recognition for MEGA OS. * Benchmark accuracy, dialect performance, JSON validity, and inference latency, then optimize quantization for local offline deployment. I'd recommend selecting the base model after reviewing MEGA hardware specifications and evaluating Arabic dialect support. Could you share the target hardware, expected command categories, and any existing dataset or JSON schema? This will help define the model, training strategy, and evaluation benchmarks.

@PINESCRIPTERR

Hi, I can develop the MEGA Smart Home NLU model around structured intent extraction rather than general conversation, with strict JSON schemas for intent, device, location, action and parameters, plus explicit handling for ambiguity and unsupported commands. I would use a parameter-efficient SFT/QLoRA approach, build a balanced Egyptian/Gulf Arabic + English dataset with hard negatives and clarification cases, and benchmark exact-field accuracy, JSON validity, intent/entity accuracy and unsupported-command detection before optimizing for local quantized inference. Arabic instruction-tuning research supports task-specific LoRA tuning and cross-dataset evaluation, while recent Arabic tool-calling work also highlights the importance of in-language tool-call data. Questione 1: What are the target MEGA hardware limits for RAM/VRAM, CPU/GPU and maximum acceptable inference latency? Questione 2: Can you provide the current MEGA OS JSON schema, supported devices/actions and representative Egyptian/Gulf command examples for building the initial dataset? Regards, Houssame.

@TeraByte26

Hi I will fine‑tune a LoRA‑based LLM to understand Egyptian Arabic, Gulf Arabic and English smart‑home commands, output intent/device/location/action/parameters as structured JSON, and ask clarifying questions for ambiguous or unsupported requests. I can deliver a quantized 4‑bit model and deployment scripts within 7 days for $200. I can share a short test sample now; shall I begin? Best, Alex Waiting for your response in chat! Best Regards.

@markkien

Hello, I can see the challenge is to develop a specialized language model for MEGA Smart Home AI that accurately understands natural smart-home commands in Egyptian Arabic, Gulf Arabic, and English, and generates a structured JSON output compatible with MEGA OS. I will implement this by fine-tuning a pre-trained base model (e.g. BERT or RoBERTa) with LoRA/QLoRA for efficient adaptation to the specific task, leveraging SFT for intent detection, and utilizing Python libraries (e.g. Hugging Face Transformers) for model development and deployment. I will also design a dataset strategy that includes Egyptian and Gulf Arabic datasets, and implement a clarification mechanism to handle ambiguous requests. The deliverable will be a trained language model that can be deployed on MEGA hardware, along with inference and deployment files. I have extensive experience in LLM fine-tuning, Arabic NLP/NLU, and dataset creation/annotation, with previous projects including Arabic/Egyptian/Gulf LLM development. I can start immediately.

@bohdan38

Hi, The core challenge is building a compact offline NLU model that reliably maps Egyptian Arabic, Gulf Arabic, and English smart-home commands into strict MEGA OS JSON without guessing when requests are ambiguous or unsupported. The first milestone can establish the recommended base model, SFT with LoRA or QLoRA methodology, dataset schema and annotation strategy, quantization approach, and an evaluation benchmark covering intent, device, location, action, parameters, clarification behavior, and unknown commands. Python tooling can also validate JSON outputs automatically. Similar work includes Covertly AI and Cre8tor AI, where AI workflows, structured integrations, and production-oriented model features were developed. The approach will prioritize offline inference efficiency and language-specific coverage. I will deliver a clear technical plan, sample schemas, benchmark design, and deployment recommendations within seven days, with clean documentation for the next implementation stage. Best regards, Bohdan

@FizzaNadeemK

Hi, I can develop and fine tune your smart home NLU model for Egyptian Arabic, Gulf Arabic and English using SFT with LoRA or QLoRA. I’ll focus on reliable intent, device, location, action and parameter extraction with strict JSON output, including clarification handling for ambiguous or unsupported commands. I’ll design the dataset and annotation strategy, select an efficient base model for local deployment, build the training pipeline in Python, and evaluate accuracy with intent and JSON schema metrics plus edge case testing. I’ll provide the trained model, quantized inference files and clear deployment documentation for MEGA hardware. I can start immediately.

@elijahhmonreal

Hi there, I'm an AI/LLM Engineer with 12+ years in NLP and machine learning specialization. Your MEGA Smart Home AI project requires exactly what I've built, multilingual language models for intent recognition and structured JSON output generation. I've successfully completed Arabic/Egyptian dialect NLP projects using LoRA fine-tuning on base models like Llama 2 and Mistral. My approach: curate dialect-specific datasets, implement QLoRA for efficient quantization, benchmark with custom metrics (intent accuracy, entity extraction, clarification request handling), and deliver optimized inference models for offline deployment. For your use case, I recommend a 7-13B parameter model with QLoRA, stratified dataset split across Egyptian/Gulf/English (2,000+ annotated samples minimum), and evaluation against ambiguity scenarios. I'll provide production-ready quantized weights and deployment configs for your hardware. Let me know if my profile looks interesting, and we can set up a time to talk. Best regards, Elijah M

@Yoshida57

Hi, I’ve reviewed your project and understand that you’re looking for a specialized smart-home language model that accurately understands Egyptian Arabic, Gulf Arabic, and English commands, with particular attention to intent extraction, structured JSON output, and safe clarification handling. I can help you with this by fine-tuning a compact LLM for offline NLU, building a robust annotation schema for smart-home intents, and validating outputs against your MEGA OS requirements. I have relevant experience with LLM fine-tuning, Arabic NLP/NLU, dataset creation, and Python-based evaluation, and I’ll make sure the work is accurate and optimized. For your project, my approach would be: • Define intent, device, location, action, and parameter schemas aligned to MEGA OS JSON • Build and annotate a bilingual Arabic-English smart-home dataset, including ambiguity and unsupported-command cases • Fine-tune and evaluate a base model with SFT and LoRA/QLoRA, then prepare offline deployment artifacts I’ll keep communication clear throughout the project and provide regular updates so you always know the progress. I can start immediately and can work within your budget. If you share your current MEGA JSON schema, I can confirm the exact approach and get started. Looking forward to discussing the project with you. Best regards, Kanae

@hakan1022

Hi, there I successfully fine-tuned a multilingual LLM for a smart speaker app, handling Arabic dialects, including Egyptian and Gulf Arabic, with high accuracy. This project involved implementing intent identification, device control, and parameter extraction from user sentences, generating structured JSON output compatible with the app's OS. I utilized a pre-trained base model, fine-tuning it with a combination of SFT and LoRA, and adapted the dataset to include Arabic dialects, resulting in significant improvement in intent recognition and device control. For your MEGA Smart Home AI, I plan to use a pre-trained multilingual base model, fine-tuning it with a dataset of smart home-related commands in Egyptian and Gulf Arabic, using SFT and LoRA. I will utilize a combination of NLP/NLU techniques to identify intent, device, location, action, and parameters, generating a structured JSON output compatible with MEGA OS. To handle ambiguous requests, I will implement a clarification mechanism, asking for additional information instead of guessing, and recognize unknown/unsupported devices or commands. If I use my previous experience, your project will likely be completed successfully. Hope to discuss this in detail. Through detailed discussion, I think I can find the better solution to finish your project successfully. Thank you.

@MuhammadUzair16

Hi, I am a software engineer with over 16 years of experience, including AI/NLP systems, Python-based model pipelines, structured outputs, and efficient local deployment. I can develop MEGA’s multilingual smart-home NLU model for Egyptian Arabic, Gulf Arabic, and English, with reliable intent/entity extraction, strict schema-valid JSON, clarification handling, and rejection of unsupported commands. I would benchmark compact multilingual models first, with Qwen2.5-3B/7B as a strong starting candidate because of its Arabic capability, structured generation, and quantization options. My approach would use a carefully balanced, dialect-aware dataset covering intents, devices, rooms, actions, parameters, paraphrases, ambiguity, and negative examples, followed by SFT with LoRA/QLoRA and constrained JSON validation. I would begin with roughly 15,000–30,000 reviewed examples, depending on your command taxonomy. Evaluation would include intent accuracy, entity-level F1, exact JSON match, schema validity, clarification/unknown-command accuracy, and separate results for each language and dialect. Delivery will include the trained adapters/model, quantized inference files, scripts, configuration, benchmarks, and deployment guidance for MEGA hardware. Relevant client projects cannot be shared publicly, but selected work can be demonstrated privately where permitted. Which hardware, RAM, accelerator, and maximum response latency are targeted? Please contact me to discuss details.

@abhilashr53

You need a compact, accurate NLU engine that reliably maps Egyptian Arabic, Gulf Arabic, and English smart-home commands into structured JSON, runs offline on constrained MEGA hardware, and never silently guesses when it should ask for clarification. That is a precision engineering problem, not a general chatbot project, and the distinction shapes every decision below. Recommended base model: Jais-13B or ALLaM-7B, both Arabic-English pretrained, with QLoRA 4-bit quantization bringing inference to under 6GB VRAM, suitable for edge deployment. I would apply supervised fine-tuning using LoRA adapters targeting intent classification, slot filling (device, location, action, parameters), and a clarification-trigger output class for ambiguous inputs. Dataset strategy: I would build 8,000 to 12,000 annotated utterances split roughly 40% Egyptian, 35% Gulf, 25% English, sourced through templated generation plus native-speaker review, covering edge cases like missing location or conflicting parameters. Evaluation would use intent accuracy, slot F1, clarification precision, and JSON schema validity rate, targeting above 94% across all dialects. Deliverables include the fine-tuned adapter weights, quantized GGUF or ONNX inference files, and a documented inference pipeline ready for MEGA OS integration. Timeline: approximately six weeks from dataset sign-off to final delivery. What JSON schema and device taxonomy does MEGA OS currently define? Starting from your spec avoids rework.

@aakaashberi

Since MEGA OS needs strict JSON, I'd constrain decoding with a JSON grammar at inference. The fine-tuned model then cannot emit an invalid command; fine-tuning only has to get intent, device, location, action and parameters right, including "clarify" and "unsupported" as explicit outputs. Base model: Qwen2.5 1.5B/3B Instruct (strong Arabic, small enough for local hardware). Method: QLoRA SFT on your schema, then merge and quantize to GGUF Q4 for llama.cpp. Dataset: templated generation from your device/room/action catalogue in Egyptian, Gulf and English phrasing, around 8-15k examples, plus ambiguous and unsupported cases; a native speaker on your side reviews a sample for dialect accuracy. Evaluation: JSON validity, exact-match rate, per-slot F1 by dialect, and clarification precision on a held-out set. $75 covers phase 1 in 4 days: the dataset generator, training pipeline and a pilot model on a seed set, with an eval report. Scaling the dataset and final tuning is a follow-up phase. Past work is under NDA, so the pilot is my proof. Can you share the MEGA OS JSON schema and device list, and the target hardware (RAM, GPU/NPU)? Aakaash

@pandaymano8

As an accomplished Full-Stack Developer with a strong background in Machine Learning (ML) and Natural Language Processing (NLP), I am confident that I possess the necessary skills and experience to develop a specialized language model for your MEGA Smart Home AI. In line with your expectation for a freelancer to provide details on how they would execute this task, I would propose using a powerful pre-trained base model like GPT-3. The reliability of GPT-3 cannot be understated and will be an excellent choice for this project due to its ability to understand complex and contextually rich language patterns. Given that you prioritize the understanding of ambiguous requests and distinguished intents, this project isn't just about language comprehension. It requires meticulous data strategy formulation as well as creating a dataset that replicates real-world scenarios. My experience in dataset creation/annotation ensures we produce an extensive dataset specific to your needs. With my Python skills and my evaluation methodology aligned with recognized industry standards, I will deliver not just a final trained model and inference/deployment files but also ensure a smooth transition into cybersecurity maintenance by keeping up with constant evolution in the sector. My commitment is to long-term reliability.

@jaroslavcaprata

Hi, Thanks for sharing the details of your MEGA Smart Home AI project. This is a very interesting use case where a specialized NLU model needs to reliably convert natural language commands into structured smart-home actions. I have experience building AI-powered applications using LLMs, RAG workflows, AI agents, and custom data pipelines. I can help with the full workflow, including selecting an appropriate base model, preparing training datasets, implementing SFT with LoRA/QLoRA, evaluating performance, and optimizing the final model for efficient local deployment. For this type of system, I would focus on intent classification, entity extraction, strict JSON generation, ambiguity handling, and robust evaluation rather than general conversational ability. The model architecture and fine-tuning approach would be selected based on hardware constraints and required inference performance. Could you share the target MEGA hardware specifications (GPU/RAM/storage) and whether you already have an initial dataset of Arabic smart-home commands, or would dataset creation and annotation be part of the project scope? I’d be happy to discuss your requirements and help build a reliable offline smart-home AI model. Best regards, Jaroslav Caprata

@dataspro

Nice to meet you , It is a pleasure to communicate with you. My name is Anthony Muñoz, I am the lead engineer for DSPro IT agency and I would like to offer you my professional services. I have more than 10 years of working as a Backend and Software developer, I have successfully completed numerous jobs similar to yours therefore, and after carefully reading the requirements of your project, I consider this job to be suitable to my area of knowledge and skills. I would love to work together to make this project a reality. I greatly appreciate the time provided and I remain pending for any questions or comments. Feel free to contact me. Greetings

@indreshpnk

Hello, I understand you need a specialized LLM/NLP model for MEGA Smart Home AI that understands Egyptian Arabic, Gulf Arabic, and English and converts commands into strict structured JSON. The goal is an accurate, lightweight model suitable for local/offline deployment. Here’s what I can provide: SFT with LoRA/QLoRA using a suitable multilingual base model optimized for Arabic NLU. Dataset creation and annotation covering intent, device, location, action, parameters, ambiguity, and unsupported commands. Evaluation using intent/field accuracy, JSON validity, clarification accuracy, and inference benchmarks, followed by quantization for local deployment. I bring over 4+ years of experience in Python, machine learning workflows, APIs, and software development, with a strong focus on structured, reliable application outputs. Just to clarify a few things: What MEGA hardware specifications and maximum model size should the final model target? Do you already have an Arabic command dataset, or should I handle dataset creation and annotation from scratch? Please come to the chat box to discuss more about your project. Best regards Indresh Kushwaha