Dev tools · alternatives · 2026
24 alternatives to Open-source fine-tuning in a Colab notebook
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
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Open-source fine-tuning in a Colab notebook launched in 2024; newer entries below may have overtaken it.
- 1
- 2
Hi HN! I'm just sharing a project I've been working on during the LLM Efficiency Challenge - you can now finetune Llama with QLoRA 5x faster than Huggingface's original implementation on your own local GPU. Some highlights: 1. Manual autograd engine - hand derived backprop steps. 2. QLoRA / LoRA 80% faster, 50% less memory. 3. All kernels written in OpenAI's Triton language. 4. 0% loss in accuracy - no approximation methods - all exact. 5. No change of hardware necessary. Supports NVIDIA GPUs since 2018+. CUDA 7.5+. 6. Flash Attention support via Xformers. 7. Supports 4bit and 16bit…
2023 · github.com · its alternatives →
- 3
I've been playing around with https://github.com/zphang/minimal-llama/ and https://github.com/tloen/alpaca-lora/blob/main/finetune.py, and wanted to create a simple UI where you can just paste text, tweak the parameters, and finetune the model quickly using a modern GPU. To prepare the data, simply separate your text with two blank lines. There's an inference tab, so you can test how the tuned model behaves. This is my first foray into the world of LLM finetuning, Python, Torch, Transformers, LoRA, PEFT, and Gradio. Enjoy!
2023 · github.com · its alternatives →
- 4
Fine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU. - MakazhanAlpamys/Soup
Aug 2026 · github.com · its alternatives →
- 5WM
Try it out! https://glhf.chat/ Hey HN! We’ve been working for the past few months on a website to let you easily run (almost) any open-source LLM on autoscaling GPU clusters. It’s free for now while we figure out how to price it, but we expect to be cheaper than most GPU offerings since we can run the models multi-tenant. Unlike Together AI, Fireworks, etc, we’ll run any model that the open-source vLLM project supports: we don’t have a hardcoded list. If you want a specific model or finetune, you don’t have to ask us for it: you can just paste the Hugging Face link in and…
2024 · glhf.chat · its alternatives →
- 6
I wanted to share our new speech to text model, and the library to use them effectively. We're a small startup (six people, sub-$100k monthly GPU budget) so I'm proud of the work the team has done to create streaming STT models with lower word-error rates than OpenAI's largest Whisper model. Admittedly Large v3 is a couple of years old, but we're near the top the HF OpenASR leaderboard, even up against Nvidia's Parakeet family. Anyway, I'd love to get feedback on the models and software, and hear about what people might build with it.
Feb 2026 · github.com · its alternatives →
- 7
We recently used DeepSeek V4 Flash as a teacher for finance tasks with GPT-OSS-120B. Distillation works well on this problem. At a constrained 8k token budget, our self-distilled 120B scores 83.61% on FinanceReasoning, above Kimi K3 (81.93%) and Inkling (65.13%). We released the 20B open weights. With V4 as the teacher though, we realized it would be timely to measure if the censorship characteristic of it transferred to the distilled version of the base model. tl;dr it didn't, the teacher answered politically sensitive questions 7 SDs differently than expected, but the distilled model's…
Jul 2026 · ctgt.ai · its alternatives →
- 8
Hey everyone, We have been building SecureAI Tools -- an open-source application layer for ChatGPT and ChatPDF-like AI tools. It works with locally running LLMs as well as with OpenAI-compatible APIs. For local LLMs, it supports Ollama which supports all the gguf/ggml models. Currently, it has two features: Chat-with-LLM, and Chat-with-PDFs. It is optimized for self-hosting use cases and comes with basic user management features. Here are some quick demos: * Chat with documents using OpenAI's GPT3.5 model: https://www.youtube.com/watch?v=Br2D3G9O47s * Chat with documents…
2023 · github.com · its alternatives →
- 9
Hey HN fam, We’ve seen developers spend a lot of time implementing advanced RAG techniques from scratch. While these techniques are essential for improving performance, their implementation requires a lot of effort and testing! To help with this process, our team (Athina AI) has released Open-Source Advanced RAG Cookbooks. This is a collection of ready-to-run Google Colab notebooks featuring the most commonly implemented techniques. Please show us some love by starring the repo if you find this useful!
2024 · github.com · its alternatives →
- 10
Minimal, readable LLM post-training experiments on one 8GB GPU. Measures forgetting, seed variance, and RL emergence. - pochenai/nano-llm-posttraining
Aug 2026 · github.com · its alternatives →
- 11
A pipeline that translates Rust GPU code into formal Coq models, as a foundation for memory model proofs - neelsomani/vericuda
2025 · github.com · its alternatives →
- 12
100% bootstrapped new startup. It lets you fine tune Mistral-7B and SDXL. In particular, for the LLM fine tuning we implemented a dataprep pipeline that turns websites/pdfs/doc files into question-answer pairs for training the small LLM using an big LLM. It includes a GPU scheduler that can do finegrained GPU memory scheduling (Kubernetes can only do whole-GPU, we do it per-GB of GPU memory to pack both inference and fine tuning jobs into the same fleet) to fit model instances into GPU memory to optimally trade off user facing latency with GPU memory utilization It's a pretty…
2023 · docs.helix.ml · its alternatives →
- 13
Hey HN! I made a completely open sourced alternative to Weights and Biases with (insert cringe) blazingly fast performance (yes we use rust and clickhouse) Weights and Biases is super unperformant, their logger blocks user code... logging should not be blocking, yet they got away with it. We do the right thing by being non blocking. Would love any thoughts / feedbacks / roasts etc
2025 · github.com · its alternatives →
- 14
FinetuneDB▲52AI fine-tuning platform to create custom LLMs
2024 · finetunedb.com · its alternatives →
- 15
- 16
runNburn is an Apache-2.0 Rust inference engine for quantized GGUF models that are too big for your fast memory. The core idea: weights stay file-backed (mmap), host residency stays under an explicit byte budget (--ram-budget), and GPU caches are sized from detected free/total VRAM — never from device-name presets. There is no conversion step, no sidecar cache files, no silent requantization. The GGUF on disk is the single source of truth. The result that made me want to post this: Tencent's Hy3 (295B total / 21B active sparse MoE, a single 97.8 GiB Q2_K GGUF) runs on my desktop…
Jul 2026 · github.com · its alternatives →
- 17
Hey HN! After using a combination of Unsloth and Axolotl a lot, and finding it generally painful to figure out the right performance tuning for things like batch sizing and multi-GPU sharding, I wrote a small Python lib that sets up known-good LoRA training configurations for Llama 3.1 8B and 70B Instruct, and includes helpers for distilling from larger models or training on serverless finetuning platforms, and includes a walkthrough for distilling DeepSeek-R1 into a Llama 3.1 8B LoRA... But you can use it for pretty much any finetuning task, not just distilling large models!
2025 · github.com · its alternatives →
- 18L2
Hi all, today we're excited to launch LoraLand: 25 fine-tuned mistral-7b models that outperform #gpt4 on task-specific applications ranging from sentiment detection to question answering. All 25 fine-tuned models… - Outperform GPT-4, GPT-3.5-turbo, and mistral-7b-instruct for specific tasks - Are cost-effectively served from a single GPU through LoRAX - Were trained for less than $8 each on average You can prompt all of the fine-tuned models today and compare their results to mistral-7b-instruct in real time! We'd love to hear comments and feedback from the community
2024 · predibase.com · its alternatives →
- 19TF
I’d originally launched my app: Private LLM[1][2] on HN around 10 months ago, with a single RedPajama Chat 3B model. The app has come a long way since then. About a month ago, I added support for 4-bit OmniQuant quantized Mixtral 8x7B Instruct model, and it seems to outperform Q4 models at inference speed and Q8 models at text generation quality, while consuming only about 24GB of RAM[3] at 8k context length. The trick is: a) to use a better quantization algorithm and b) to use unquantized embeddings and the MoE gates (the overhead is quite small). Other notable features include many more…
2024 · its alternatives →
- 20
I wanted to know how fast a 26B mixture-of-experts model could run on a desktop CPU with no GPU. Got ~40 tok/s single-stream (lossless) and ~124 batched. The surprising part was the byte budget: for this model you compress the output head (32% of per-token bytes), not the experts (16%). The writeup has the bandwidth roofline and the dead-ends; the repo has the reproducible recipe. Happy to answer questions. Repo: https://github.com/arun-prasath2005/gemma4-cpu-moe
Jun 2026 · apeg.dev · its alternatives →
- 21IB
After fine-tuning GPT for a personal project, I realized how tedious it is to write plain text in a massive JSON file. That's why I built this app for my own use, and I want to see if others could benefit from a tool like this as well ;)
2024 · finetuna-ui.com · its alternatives →
- 22
I made this after seeing someone posit the idea online yesterday over lunch then spent some time refining it. So far it's pretty impressive IMO! Right now I am running Qwen3-30B-A3B on my 24gb unified memory m4 MacBook Pro at 50 tok/sec and this should definitely not be working for such a large model on my middling hardware. Things are detailed in the README to get up and running and DESIGN.md has details on all the choices and such made along the way.
Aug 2026 · github.com · its alternatives →
- 23
Local, gradient-free neuro-symbolic memory engine combining Hyperdimensional Computing (HDC/VSA), Hebbian plasticity, and graph triples for offline AI. - roandejager/Hillock
Aug 2026 · github.com · its alternatives →
- 24
Abstract page for arXiv paper 2607.23806: A Frozen 12B Beats Frontier Models on Verified Work: 100% Accuracy, 0 Tokens, Bit-Exact, Forever
Jul 2026 · arxiv.org · its alternatives →
Also compare
- Unsloth alternatives
- 80% faster, 50% less memory, 0% loss of accuracy Llama finetuning alternatives
- Finetune LLaMA-7B on commodity GPUs using your own text alternatives
- Fine-tune an 8B model on a 4 GB laptop GPU alternatives
- We made glhf.chat – run almost any open-source LLM, including 405B alternatives
- Moonshine Open-Weights STT models – higher accuracy than WhisperLargev3 alternatives
Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →