Alternatives
Products that do what Rebiha does
Fine-tune LLMs with ready-made datasets, no infrastructure
- 1FT
Aug 2026 · github.com
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LLM reinforcement fine-tuning platform to improve LLM output
2025
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We have a massive GPU cluster and developed our own infrastructure to manage the cluster and train massive models. There's how it works: 1. You upload the dataset with preconfigured format into HuggingFaсe [1]. 2. Choose your LLM (e.g. LLaMa 70B, Mistral 7B) 3. Place your submission into the queue 4. Wait for it to get trained. 5. Then you get your trained model there on HuggingFace. Essentially, why would we want to do it? 1. We already have an experience with training big LLMs. 2. We could achieve near-perfect infrastructure performance for training. 3. Sometimes GPUs have just nothing to…
2023 · higgsfield.xyz
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Aug 2026 · github.com
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Hey HN, If you tried running open-source models like Llama 3.1 70B or 405B, you might have noticed that it gets very expensive. The reason looks obvious enough that you might have stopped even before trying it! - GPUs are very expensive to buy or rent - Running the most performing LLMs need 4, 8 or even 16 top of the line Nvidia GPUs - And that won’t get you anywhere near the level of VRAM needed to batch enough to get a decent throughput and efficiency Some have even questioned if open-source LLM providers are not doing some shenanigans to provide the prices they offer. VC funded…
2024
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Jan 2026 · tinytune.xyz
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Posted before, but wanted to share if you want an open source alternative to OpenAI fine-tuning, give Unsloth a try! Phi 3.5 was just released, and is distilled from GPT4. Unsloth makes finetuning 2x faster, uses 70% less VRAM + has no accuracy degradations. We rewrite all backprop steps and reduce FLOPs and write everything in Triton (JIT low level CUDA). If you want to own the weights after fine-tuning, give Unsloth a spin! I have free Colabs and Kaggle notebooks as well at https://github.com/unslothai/unsloth
2024 · colab.research.google.com
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