LLM Model - I wan to create from scratch

@ZohaibRoy

⭐⭐⭐⭐⭐ Build a Complete LLM Model from Scratch for Your Needs ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for an experienced developer to create a complete LLM model from scratch. Look no further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in developing language models. I will use proven techniques and efficient methods to build your model, ensuring it meets your needs within the same budget. ➡️ Why Me? I can easily develop your LLM model as I have 5 years of experience in machine learning, natural language processing, and model training. My expertise includes data preprocessing, model evaluation, and optimization. Not only this, but I also have a strong grip on frameworks like TensorFlow and PyTorch, which will enhance the model's capabilities. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. I look forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ Machine Learning ✅ Natural Language Processing ✅ Deep Learning ✅ Model Training ✅ Data Preprocessing ✅ Model Evaluation ✅ TensorFlow ✅ PyTorch ✅ Python Programming ✅ Data Analysis ✅ Algorithm Development ✅ Performance Optimization Waiting for your response! Best Regards, Zohaib

@irsolutions

With over a decade of experience in web and mobile development, including expertise in AI/ML, I understand your need to create an LLM model from scratch. My extensive background in building intelligent, data-driven solutions aligns perfectly with your project requirements. In the realm of FinTech, HealthTech, and eCommerce, I have successfully delivered tailored products that drive results. My experience in AI/ML development will ensure that your LLM model is not only created from scratch but is also optimized for peak performance. I am eager to collaborate with you on this project and bring your vision to life. Please feel free to reach out to discuss how we can move forward with creating the LLM model that meets your needs within the specified budget and timeline.

@arzoof1

Hello Building an LLM model completely from scratch is a serious engineering task, and exactly the kind of challenge I enjoy. I have strong experience in Python, deep learning workflows, model architecture design, data preprocessing, and training pipelines, and I can handle everything from dataset preparation and tokenizer creation to transformer architecture implementation and training optimization. I focus on clean, scalable code and measurable performance improvements. Best Regards, Arzoo Farooq

@mubeenm5026

With over 10 years of invaluable experience in AI development with expertise in Python, I believe my team at Web Crest and I have the exact skill set you need to build your LLM model from scratch. We are not just developers; we are long-term technology partners who have contributed to numerous successful projects. Notably, we have an impressive 98% project completion rate, backed by consistent positive feedback. Our unique value proposition lies in our focus on practical innovation, and this aligns perfectly with your project's demand for intelligence and scalability. Whether it's leveraging AI for automation or building high-performing web or mobile apps, we don't just make them look good; we ensure they perform optimally to save time and increase efficiency. Another vital aspect setting us apart is our commitment to clear communication and a transparent workflow throughout the project. By choosing us, you're guaranteed a partner who understands both the intricacies of technology and the potential business impact that can be brought by a powerful digital product. Let's work together to create an LLM model that is future-ready, smart, and adaptive to your distinct requirements!

@kamran2012

Hi there, I will build your custom LLM covering the data preprocessing pipeline, tokenizer training, transformer architecture setup, model training on GPU infrastructure, and evaluation benchmarking against standard metrics. One critical decision upfront is the model size and training data volume. A 125M-parameter model is trainable on a few GPUs in days and works well for specialized tasks, while anything above 1B parameters requires distributed training across multiple nodes with significantly higher compute costs. I will help you find the right balance for your use case. Questions: 1) What is the target use case for this LLM - general text generation, domain-specific Q&A, code generation, or something else? 2) Do you have a training dataset prepared, or do you need help with data collection and cleaning? 3) What GPU infrastructure do you have access to (cloud budget, on-premise hardware)? Looking forward to your response. Best regards, Kamran

@OutsourceMan

⭐⭐⭐⭐⭐ Dear Valuable Client, At CnELIndia, led by Raman Ladhani, we specialize in creating advanced AI and machine learning solutions, including building large language models (LLMs) from scratch. Our approach would be as follows: Requirement Analysis: Understand the specific needs of your LLM, including use cases, data sources, and performance expectations. Data Collection & Preprocessing: Gather diverse, high-quality datasets to train the model, ensuring it’s optimized for multimodal inputs. Model Architecture Design: Implement state-of-the-art deep learning techniques using Python, focusing on scalability and efficiency. Training & Fine-Tuning: Leverage advanced algorithms for training the LLM, including optimization strategies to achieve desired accuracy. Testing & Deployment: Rigorous model testing followed by seamless deployment with ongoing maintenance. With our team's experience, we are confident in delivering a powerful LLM that meets your goals. Best regards, CnELIndia and Raman Ladhani

@webmasters486

Hi there, I’ve carefully reviewed the requirements for your GenAI project and I’m confident that my expertise in building NLP pipelines using Hugging Face and LangChain can meet your expectations. My experience includes working with large language models (LLMs) for Retrieval-Augmented Generation (RAG), as well as fine-tuning models with custom datasets to enhance text generation. I’ve successfully completed similar projects where I applied these techniques in Python to build robust, client-specific solutions. I would love the opportunity to discuss how I can leverage my skills to develop a tailored solution for your project. Feel free to take a look at my portfolio to get a sense of the work I’ve done: Portfolio: https://www.freelancer.com/u/webmasters486 Looking forward to hearing from you! Best regards, Muhammad Adil

@usamae

Hey, Building an LLM from scratch is a substantial project - scope can range quite a bit depending on what you're going for. Are you looking at full pre-training (collecting a corpus, training a transformer architecture with GPU clusters) or more like fine-tuning/adapting an existing open-source model like Llama or Mistral for a specific use case? I've done Python/ML work including deep learning pipelines, PyTorch, and the Hugging Face ecosystem. Either approach is doable, but the budget and timeline look very different depending on direction. What's the main goal or domain you're tareting with this? - Usama

@techstar12

Building an LLM completely from scratch is possible, but it’s a big engineering and research effort. If your goal is a usable model, the fastest serious path is usually: implement the architecture and training loop yourself, but start from open datasets and proven recipes, then scale up. Here’s how I can help an “experience developer” deliver it end to end: Scope I can implement Data pipeline: collection cleaning dedup tokenization dataset sharding Tokenizer: BPE or Unigram training and inference Model: GPT style Transformer from scratch in PyTorch Training: mixed precision gradient checkpointing DDP FSDP Evaluation: perplexity benchmarks plus safety filters Inference: KV cache batching quantization export to ONNX or Triton Deployment: simple API server with monitoring Reality check on compute A small LLM you can train on 1 to 4 GPUs. A competitive general LLM needs a cluster and budget. If you tell me your GPU setup and target size, I will tailor a plan that fits. To start, please share Your target use case and language Model size goal like 100M 1B 7B parameters Available hardware GPUs type and count Do you need instruction tuning and chat behavior or just base model If you send these, I can reply with a clear build plan, milestones, and what can be realistically achieved with your resources.

@KarthikTechCon

Hi, I’m Karthik, an AI/ML Architect with 15+ years of experience building data platforms and deploying LLM-based systems. I’ve worked with transformer architectures, fine-tuning pipelines, and large-scale training workflows. Building an LLM completely from scratch (architecture → training → evaluation → deployment) is a serious engineering effort, and I can guide or execute this depending on your scope. Before proceeding, we should clarify: • Do you want to train a base model from raw data? • Or build a domain-specific LLM via pretraining + fine-tuning? • What parameter size target (small 100M–1B vs larger)? • What GPU infrastructure budget is available? My Approach 1️⃣ Define model architecture (Transformer-based) 2️⃣ Data collection & cleaning pipeline 3️⃣ Tokenizer training 4️⃣ Distributed training setup (PyTorch + DeepSpeed/FSDP) 5️⃣ Evaluation & benchmarking 6️⃣ API serving layer (FastAPI + inference optimization) If budget constraints exist, I may recommend starting with an open-source base model and fine-tuning instead of full pretraining, which can cost millions in compute. Happy to discuss realistic scope, cost, and timeline before kickoff. – Karthik

@chiragardeshna

Hi there, I am a strong fit because I have worked on training and fine-tuning large language models, including building custom transformer architectures and training pipelines from raw text corpora. I have implemented tokenization pipelines, transformer models in PyTorch, distributed training workflows, evaluation benchmarks, and inference wrappers for production deployment. I need to be direct: training a competitive LLM fully from scratch requires massive datasets, distributed GPU clusters, and significant budget, so in most cases a domain-specific model built via fine-tuning or continued pretraining on an open base model is more practical and cost-efficient. I reduce risk by first defining target use-case, dataset scale, compute budget, and evaluation metrics, then proposing a realistic path such as training a smaller transformer from scratch for research or adapting an existing open-source foundation model. If you share your objective, dataset size, and budget constraints, I can propose a clear technical roadmap and resource estimate. Regards Chirag

@hr157

Hi, I can help you design and build an LLM system from scratch — from architecture planning to deployment — with a strong focus on scalability, performance, and real-world usability. With 10+ years as a full-stack developer, I’ve worked on AI integrations, backend systems, and production-ready platforms, so I understand both the engineering and infrastructure side of LLM projects. My approach: • Define the goal first — training a new base model vs fine-tuning vs RAG-based system • Select the right stack (Llama/Mistral, PyTorch, vector DB, APIs, cloud GPU) • Build data pipeline, training workflow, and evaluation metrics • Create backend APIs + simple UI for testing and future scaling • Optimize for latency, cost, and maintainability I’ll also guide you on dataset preparation, model hosting, and long-term scaling so the system is production-ready — not just experimental. Let’s discuss: ? What type of LLM you want (chatbot, assistant, domain-specific model)? ? Training from zero weights or customizing an existing open model?

@dinhthi

Hello I have build many ai application so i believe i can finish this project very quickly and quality Please contact me to discuss more detail Thanks

@Web1devloper

My decade-long experience in data analytics and related domains makes me an excellent fit for building your LLM model. The combination of my advanced skill set, including data storytelling, dashboarding, predictive analytics, and proficiency in a wide range of tools like Power BI, Looker, SQL, Python, Machine Learning (ML), and more make me the all-rounder you seek for your project. Throughout my career, I have dealt with complex datasets and transformed them into valuable business insights - tirelessly optimizing operations and improving customer understanding. I believe that's crucial as it demonstrates my ability to tackle unique challenges head-on while keeping the end-users in mind. When creating your LLM model 'from scratch', this customer-centric approach will ensure the final product seamlessly caters to your specific needs. With an ample understanding of machine learning techniques such as TensorFlow and PyTorch, I possess the necessary knowhow to approach novel and intricate tasks creatively. Combined with my problem-solving and insights extraction skills, your vision for a completely tailor-made LLM model will not only be realized but exceeded. Trust me to unlock the full potential of your data today!

@TechWagger2026

With over 10 years of experience in the field, I can confidently say that handling your LLM model project from scratch is well within my wheelhouse. My expertise spans a wide range of fields, including AI Development, AI Model Development, and Machine Learning (ML) which are all crucial elements for the successful completion of your task. Drawing from my solid knowledge of Python, I have developed proficiency in working with AI and LLM Models. I have worked with popular libraries like Hugging Face and LangChain to create industry-leading solutions for my clientele. What sets me apart is not just the skills on my resume but a deep understanding of how these skills would fit into your project. While technical prowess is integral, being able to implement it with a thorough understanding of the problem at hand is what separates an average job from an excellent one. I bring a methodology that ensures optimum functionality, quality assurance, and adherence to strict deadlines. Let me help you transform your vision into a living, cutting-edge reality! Reach out to me today so we can discuss further how I can be of value to your project.

@ssquare489

I am Sumit Joshi from Sacesta Technologies. I can help you build an LLM end to end, but “from scratch” can mean two different things: • Train a small language model using open source tooling on your own dataset (realistic) • Build a transformer and train a frontier scale model like GPT (needs massive GPUs and budget) What we can deliver in a practical way for the first option • Define goal and constraints: use case, language, size, latency, budget • Data pipeline: collection, cleaning, dedup, safety filters • Model training: start from a base open model or train a small model from zero, run evaluation • Fine-tuning: SFT, LoRA, preference tuning if needed • Serving: API service, batching, caching, monitoring, guardrails • Deployment: Dockerized inference, autoscaling, cost controls Relevant experience • Built AI-powered SaaS systems with RAG, agent workflows, and production deployments To point you to the right developer quickly, share these 4 details • Use case (chatbot, search, code, domain docs, etc.) • Languages and expected users per day • Do you want training from zero or fine-tune an existing model • Your compute setup (local GPU, cloud, or none yet) Regards, Sumit Joshi

@Microlent

Hello, Building an LLM completely from scratch is a significant undertaking, so I’d first like to clarify what you mean by “from scratch.” There are two very different paths: 1. Pretraining a large transformer model from raw text data – Requires massive datasets (billions of tokens) – Distributed GPU infrastructure – Custom tokenizer, training loop, optimization strategy – High compute budget 2. Building a custom domain model or fine-tuning an existing open-source LLM – More practical and cost-efficient – Uses transfer learning – Can be tailored to your specific use case I have experience working with transformer architectures, LLM fine-tuning workflows, and scalable AI system design. I can help design the full pipeline including data preparation, model architecture, training setup, evaluation, and deployment. To move forward, I’ll need clarity on: • Intended use case • Target model size • Available GPU resources • Budget and timeline • Whether you want full pretraining or domain fine-tuning Once I understand your goal, I can propose a realistic roadmap and architecture. Best, Jenifer

@PrateekP3003

Hello. I have reviewed your requirement, and as per my understanding, you are looking to build a Large Language Model (LLM) completely from scratch—covering model architecture, training pipeline, data handling, and deployment. My approach would focus on: Model Design: Choosing an appropriate transformer architecture (GPT-style or decoder-only) and scaling strategy based on your compute resources. Data Pipeline: Collecting, cleaning, and tokenizing large-scale text corpora, handling preprocessing, filtering, and dataset sharding. Training Infrastructure: Setting up distributed training on GPUs/TPUs, implementing mixed precision, gradient accumulation, and checkpointing for long-running jobs. Fine-Tuning & Evaluation: Supporting domain-specific fine-tuning, validation metrics, and alignment with intended outputs. Deployment: Optimizing the model for inference, API integration, and low-latency serving. I have hands-on experience with PyTorch, Hugging Face Transformers, DeepSpeed, and training LLMs at scale, including both pretraining and instruction fine-tuning workflows. I can also advise on compute requirements, cost estimation, and data management for efficient model development. We can structure the project in milestones: architecture & dataset prep → pretraining → fine-tuning → evaluation & deployment. I can provide a detailed timeline and cost estimate once we align on model size and dataset scope. Best regards, Prateek

@detuli83

Hello, I’m an experienced AI and full-stack developer with hands-on experience working with LLM architectures, model pipelines, and AI system development. I can help design and build an LLM solution from scratch — covering data preparation, model training/fine-tuning, infrastructure setup, and deployment into a scalable application. I am new to this platform, however I bring proven development experience and a strong technical background. I have 20+ years of experience and worked with DELL, TCS, and CAPEGEMINI. Let's connect to discuss Thank you Deepak