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Create and Train Your Own Expert LLM: Generating Synthetic, Fact-Based Datasets with LMStudio/Ollama and then fine-tuning with MLX and Unsloth
Create and Train Your Own Expert LLM: Generating Synthetic, Fact-Based Datasets with LMStudio/Ollama and then fine-tuning with MLX and Unsloth
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Hey everyone!
Hey everyone!
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I know there are tons of videos and tutorials out there already but I've noticed a lot of questions popping up in community posts about using synthetic datasets for creative projects and how to transform personal content into more factual material. In my own work doing enterprise-level SFT and crafting my open-source models, I've enhanced a Python framework originally shared by the creator of the Tess models. This improved stack utilizes local language models and also integrates the Wikipedia dataset to ensure that the content generated is as accurate and reliable as possible.
I know there are tons of videos and tutorials out there already but I've noticed a lot of questions popping up in community posts about using synthetic datasets for creative projects and how to transform personal content into more factual material. In my own work doing enterprise-level SFT and crafting my open-source models, I've enhanced a Python framework originally shared by the creator of the Tess models. This improved stack utilizes local language models and also integrates the Wikipedia dataset to ensure that the content generated is as accurate and reliable as possible.
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I've been thinking of putting together a comprehensive, step-by-step course/guide on creating your own Expert Language Model. From dataset preparation and training to deployment on Hugging Face and even using something like AnythingLLM for user interaction. I'll walk you through each phase, clarifying complex concepts and troubleshooting common pitfalls.
I've been thinking of putting together a comprehensive, step-by-step course/guide on creating your own Expert Language Model. From dataset preparation and training to deployment on Hugging Face and even using something like AnythingLLM for user interaction. I'll walk you through each phase, clarifying complex concepts and troubleshooting common pitfalls.
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Let me know if this interests you!
Let me know if this interests you!
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Most of the datasets and models I've made have been using these scripts and my approach
Most of the datasets and models I've made have been using these scripts and my approach
Create and Train Your Own Expert LLM: Generating Synthetic, Fact-Based Datasets with LMStudio/Ollama and then fine-tuning with MLX and Unsloth

Hey everyone!

I know there are tons of videos and tutorials out there already but I've noticed a lot of questions popping up in community posts about using synthetic datasets for creative projects and how to transform personal content into more factual material. In my own work doing enterprise-level SFT and crafting my open-source models, I've enhanced a Python framework originally shared by the creator of the Tess models. This improved stack utilizes local language models and also integrates the Wikipedia dataset to ensure that the content generated is as accurate and reliable as possible.

I've been thinking of putting together a comprehensive, step-by-step course/guide on creating your own Expert Language Model. From dataset preparation and training to deployment on Hugging Face and even using something like AnythingLLM for user interaction. I'll walk you through each phase, clarifying complex concepts and troubleshooting common pitfalls.

Let me know if this interests you!

Most of the datasets and models I've made have been using these scripts and my approach
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afkfatih algorithm MultiTrickFox raincandy-u victor nivram189 pedi LeroyDyer ahmedzx AtAndDev GaelicThunder
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HeRksTAn algorithm victor nivram189 pedi AtAndDev Norod78
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algorithm victor nivram189 pedi AtAndDev
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algorithm victor nivram189 AtAndDev
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algorithm
algorithm
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Malich Coory
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葉佐俊
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leroy Samuel Dyer
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iweavings
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raincandy_U
raincandy-u
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yy
capti0n
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rshee
pedi
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Ahmed Moawad 
ahmedzx
user
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alkinun
AtAndDev
user