FYI it also supports pre-training, reward model training and RL, not just fine tuning (sft). My team built a managed solution for training that runs on top of llama factory and it's quite excellent and well supported. You will need pretty serious equipment to get good results out of it, think 8xh200. For people at home i would look at doing an sft of gemma3 270m or maybe a 1.6b qwen3, but keep in mind you have to have the dataset in memory as well as the model and kv-cache. cheers
spagettnet · 39s ago
depends ln your goals of course. but worth mentioning there are plenty of narrowish tasks (think text-to-sql, and other less general language tasks) where llama8b or phi-4 (14b) or even up to 30b with quantization can be trained on 8xa100 with great results. plus these smaller models benefit from being able to be served on a single a100 or even L4 with post training quantization, with wicked fast generation thanks to the lighter model.
on a related note, at what point are people going to get tired of waiting 20s for an llm to answer their questions? i wish it were more common for smaller models to be used when sufficient.
metadat · 1h ago
This reminds me conceptually of the Nvidia NIM factory where they attempt to optimize models in bulk / en-masse.
Word on the street is the project has yielded largely unimpressive results compared to its potential, but NV is still investing in an attempt to further raise the GPU saturation waterline.
p.s. This project logo stood out to me at presenting the Llama releasing some "steam" with gusto. I wonder if that was intentional? Sorry for the immature take but stopping the scatological jokes is tough.
sabareesh · 46m ago
This is great,but most work is involved in curating the dataset and the objective functions for RL.
"LLaMA Factory is an easy-to-use and efficient platform for training and fine-tuning large language models. With LLaMA Factory, you can fine-tune hundreds of pre-trained models locally without writing any code."
tensorlibb · 1h ago
This is incredible! What gpu configs, budget to ultra high-end, would you recommend for local fine tuning?
Always curious to see what other ai enthusiasts are running!
jcuenod · 1h ago
Can you compare this to Unsloth?
hall0ween · 2h ago
are there any use cases, aside from code generation and formatting, where fine-tuning consistently useful?
clipclopflop · 1h ago
Creating small, specialized models for specific tasks. Being able to leverage the up front training/data as a generalized base allows you to quickly create a small local model that can generate outputs for that task that can come close to or match the same you would see in a large/hosted model.
on a related note, at what point are people going to get tired of waiting 20s for an llm to answer their questions? i wish it were more common for smaller models to be used when sufficient.
https://www.nvidia.com/en-us/ai/nim-for-manufacturing/
Word on the street is the project has yielded largely unimpressive results compared to its potential, but NV is still investing in an attempt to further raise the GPU saturation waterline.
p.s. This project logo stood out to me at presenting the Llama releasing some "steam" with gusto. I wonder if that was intentional? Sorry for the immature take but stopping the scatological jokes is tough.
I found this link more useful.
"LLaMA Factory is an easy-to-use and efficient platform for training and fine-tuning large language models. With LLaMA Factory, you can fine-tune hundreds of pre-trained models locally without writing any code."
Always curious to see what other ai enthusiasts are running!