Fine-tuning Gemma4 Model Training Pipeline

@Vasilchenko

Hi, I can build a cloud-ready fine-tuning pipeline for the Gemma model using your dataset structure with incoming message, optional reasoning/thinking trace, and desired final reply, including clear control over whether loss is applied to thinking steps, final replies, or both. I will add evaluation/debug outputs showing the full prompt and exact loss-calculation sequence for selected samples, and make the pipeline reproducible for both the original Google model and the patched version once you provide its repository or weights. The deliverable will include training scripts, config files, setup instructions, and deployment guidance for running the pipeline on cloud GPU resources. https://www.freelancer.com/u/Vasilchenko

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@ZohaibRoy

⭐⭐⭐⭐⭐ Create a Training Pipeline for Gemma4 Model Fine-Tuning ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project needs and see you're looking for a solution to train and fine-tune the Gemma4 Model. You don't need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects focused on model training and optimization. I will create an efficient training pipeline that works for both model versions while ensuring optional inclusion of thinking steps. ➡️ Why Me? I can easily handle your project of creating a training pipeline for the Gemma4 Model as I have 5 years of experience in machine learning, model training, and data processing. My expertise includes building training pipelines, optimizing model performance, and handling cloud deployments. I also have a strong grip on relevant technologies such as TensorFlow and cloud platforms, allowing for a seamless project execution. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing this with you in chat. ➡️ Skills & Experience: ✅ Machine Learning ✅ Model Fine-Tuning ✅ Data Processing ✅ Training Pipeline Creation ✅ TensorFlow ✅ Cloud Deployment ✅ Debugging Outputs ✅ Performance Optimization ✅ API Integration ✅ Data Analysis ✅ Python Programming ✅ Cloud Computing Waiting for your response! Best Regards, Zohaib

@Muhammadzeesha59

Hello Sir/MAM I am a skilled full stack developer. Having rich experience in Java , C++ , C , C# , Python , Eclipse , Sql , Mysql , .Net ,Oracle , Object Oriented Programming , Data Structure , Algorithms . I have a perfect grip on “Artificial Intelligence” “Automation” , and work in “Machine Learning” Deep Learning ”. My track record as demonstrated in my 100% job completion and 5-star review rating showcases My ability to deliver exceptional results on time and with utmost quality I believe that my skill set makes me the ideal candidate for this project Please come on chat we will discuss more about this I will be waiting for your reply . Thanks and Best Regards

@emilesd617

Hi, I am an AI/ML engineer with 8 years of rich experience with a background in LLM fine-tuning and cloud training pipelines. I am familiar with Gemma, Hugging Face Transformers, LoRA/QLoRA, PyTorch, Cloud Training, Model Evaluation, etc. For this project, the most important part is building a flexible training pipeline that can support both datasets with thinking steps and datasets with only final replies. I can create a cloud-ready fine-tuning setup for the Google Gemma model and the patched version, with clear loss-masking logic, optional thinking-step training, and debug output showing the full prompt and exact loss-calculation sequence for selected samples. I will also document the setup so you can retrain, evaluate, and deploy the final pipeline easily. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@devanshkhandekar

Hi,I am a seasoned Applied ML Engineer(6+ yoe) & I can build a cloud-ready Gemma/Gemma4 fine-tuning pipeline that supports datasets with incoming_message,optional thinking_steps,& desired_final_reply. Relevant Experience: -Agentic AI & Search:Developed LangGraph multi-tool agents & real-world semantic search systems converting natural language into SQL/Elasticsearch filters -LLM Fine-Tuning:Engineered Gemma/Llama SFT pipelines using Hugging Face,TRL & PEFT/LoRA with robust chat-template & loss-masking configurations -Structured Reasoning:Trained models on multi-stage datasets (user input,intermediate reasoning/rationale,final answers) with configurable thinking fields -Backend & Vector DBs:Built scalable RAG systems leveraging FastAPI,Postgres/pgvector, chunking workflows & Dockerized deployment infrastructure Proposed Approach & Deliverables: -Pipeline Engineering:Build a data validation and training pipeline featuring prompt construction,optional reasoning inclusion, precise label masking, and token-span debugging -Model Compatibility:Support Gemma models and compatible variants via a flexible config loader managing tokenizer paths, chat templates, LoRA parameters, and precision -Evaluation:Implement evaluation scripts that output raw prompts, target sequences, masked labels, and generated text against expected ground truth. -Deliverables:Training and dataset formatting scripts, config files, evaluation/debug tools, cloud setup guides, and reproducible execution commands.

@ivicab6

Hi, I've worked extensively with model fine-tuning, including creating custom training pipelines for datasets similar to yours. I can help you set up a pipeline that optimizes both thinking steps and final responses, with the flexibility to train on datasets with or without thinking steps. We can start with a small test task to ensure the setup meets your requirements before moving to larger projects. The pipeline will run in the cloud, and I can utilize your promo code for Upcloud to ensure efficient computing resources. Let's discuss how to get started! Best Regards, Ivica

@sudhir0112

I specialize in LLM fine-tuning with Gemma, GPT models. MIT graduate + Google PM experience. Built full training pipelines: data preprocessing, LoRA fine-tuning, evaluation, deployment. At Axtria I delivered ML systems for pharma analytics. Can set up your Gemma4 pipeline efficiently. Available now!

@mayr81

Hi there, I'm Cora May, and I can help you build a robust fine-tuning pipeline for Gemma4 that works across both the official Google model and your patched variant. I’ll design the training data format and preprocessing so your dataset (incoming message, optional thinking steps, and desired final reply) can be used with the thinking section toggled on/off per sample without breaking loss masking. The pipeline will optimize two objectives, thinking and final response, using exact, inspectable loss token ranges so you can reproduce results reliably. For evaluation, I’ll add debug output for selected samples that includes the full prompt and the exact token sequence used for loss calculation for both components. I’ll also package everything to run end-to-end in the cloud and deploy as a ready-to-train training job you can re-run for new datasets.

@alizain721

You want a single training pipeline that can fine tune Gemma4 or a patched Gemma4, optionally include thinking steps, and produce per sample debug traces while running in the cloud. I get that the hard part is making thinking steps optional while keeping loss calculation exact and auditable. The real trick is flexible token masking and deterministic sequence construction so evaluation can reproduce the exact loss sequence for both thinking and final reply. I built the CrowdAxis event scoring pipeline where I handled ETL, model training, and cloud deployment for a production ML endpoint. Plan in short lines: I will create a modular trainer that accepts include_thinking as a flag and applies masked targets so you can train with or without thinking steps. I will support switching between Google shipped checkpoint and your patched checkpoint via a model path parameter. Evaluation will emit the full prompt and the exact token sequence used for loss for selected samples and write those to cloud logs or storage. Deployment will be scripted for cloud using the UpCloud credits or another provider you prefer. Can you share a small sample of the dataset and access or the repo/checkpoint for the patched model so I can draft a one page plan and final cost estimate? My bid is 20 USD.

@AlexVydrin

Hi, this is exactly the kind of LLM engineering work I like to get very precise with. I can design a cloud based training pipeline for Gemma 4 that takes your triplet dataset incoming message, thinking steps, final reply and lets you toggle whether the thinking stream is included, so you can train on both chain of thought style data and plain instruction data. The pipeline will support both the standard Google release and your patched variant via a shared config, handle data preprocessing, sharding, checkpointing and evaluation, and print for selected samples the full constructed prompt plus the exact token ranges used for loss on thinking and on final answer. I would set this up to run on a GPU instance in the cloud and document how to redeploy and rerun with your Upcloud credits or another provider, including clear configs so you can iterate on datasets and hyperparameters without touching core code.

@elijahhmonreal

Hi there, I just read your posting. It sounds like you need an expert in LLM fine-tuning and cloud-based training pipelines to build a production-ready workflow for training and evaluating Gemma4 models on datasets with incoming messages, optional thinking steps, and desired final replies. I am a software engineer with 10+ years experience in AI model training, fine-tuning pipelines, cloud infrastructure, and production-grade ML deployment. Building clean, reproducible training workflows, configuring cloud compute, handling custom datasets, and creating transparent evaluation/debugging outputs is what I specialize in. I can work with you to build a flexible fine-tuning pipeline that supports both Google’s original Gemma4 model and the patched version, with optional inclusion of thinking steps depending on the dataset format. The pipeline can optimize both reasoning/thinking traces and final responses, while also providing clear debug output during evaluation, including the full prompt and the exact loss-calculation sequence for selected samples. Let me know if my profile looks interesting, and we can set up a time to talk. Best regards, Elijah M.

@jvy7802

Hi, This is Jorge from IT GLOBAL SOLUTION LLC, based in the U.S. I can help build a cloud-ready fine-tuning pipeline for the Gemma model using your dataset structure with incoming message, optional thinking steps, and desired final reply. The key will be making the training format flexible so it can train on samples with reasoning steps when available, while also supporting simpler datasets that only include prompt and final response. My approach would be to create a clean data-preparation pipeline, prompt formatting logic, tokenizer handling, loss-mask generation, training configuration, evaluation scripts, and debug outputs. During evaluation, the system can show the full prompt, target text, and the exact token/sequence region used for loss calculation so you can verify whether the model is learning the intended reasoning and final answer behavior. I can structure the pipeline to support both the official Google model version and your patched version, assuming both are compatible with the same tokenizer/model loading interface or can be adapted with a custom loader. The setup can be deployed in the cloud with clear scripts for environment setup, training runs, checkpoints, evaluation, and final model export. I have experience with AI model development, fine-tuning workflows, data processing, cloud deployment, prompt formatting, and model integration, so I can help make this reproducible and easy to extend. Let’s connect and go over the details. Best, Jorge

@Dabeer59

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@Chris9753

In my experience, fine-tuning is a very important step in the AI world. I think we can approach this using QLoRA. I’ve faced similar situations before, and using QLoRA significantly enhanced the quality of my model’s responses. There are many ways to fine-tune models, such as PEFT, RL, or IFT, but I believe QLoRA is the best fit for this project. Let’s discuss the details further through chat.