Phoenix - Your AI AssistantFull Fine-Tuning Pipeline – Training & Deployment
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5 days
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What you get with this Offer
I will deliver a complete, end-to-end fine-tuning pipeline for your language model — covering dataset preparation and formatting, training run execution with hyperparameter optimisation, evaluation against a held-out test set, model packaging, and deployment as an inference API endpoint ready for integration with your application. This is the full fine-tuning lifecycle delivered as a single managed engagement, removing the need to coordinate data engineering, ML training, evaluation, and DevOps as separate workstreams.
The pipeline covers data cleaning and JSONL formatting (up to 1,000 examples), training run on your chosen platform (OpenAI API, Hugging Face, or cloud GPU), hyperparameter tuning across epochs and learning rate, validation set evaluation with automated and sample-level metrics, model export and quantisation for deployment, FastAPI inference endpoint development, Docker containerisation, and deployment to your target cloud environment. A pipeline documentation document and model card are delivered at handover.
Designed for startups and product teams who want a custom fine-tuned model in production without building the expertise in-house to manage every phase of the pipeline themselves.
The pipeline covers data cleaning and JSONL formatting (up to 1,000 examples), training run on your chosen platform (OpenAI API, Hugging Face, or cloud GPU), hyperparameter tuning across epochs and learning rate, validation set evaluation with automated and sample-level metrics, model export and quantisation for deployment, FastAPI inference endpoint development, Docker containerisation, and deployment to your target cloud environment. A pipeline documentation document and model card are delivered at handover.
Designed for startups and product teams who want a custom fine-tuned model in production without building the expertise in-house to manage every phase of the pipeline themselves.
What the Freelancer needs to start the work
Please share your raw training data, describe the fine-tuning task and target use case, confirm your preferred base model and platform, provide your cloud provider credentials for deployment, and specify your latency and throughput requirements for the inference endpoint.