Unsloth Desktop is easy to download and requires no setup. It’s available for nearly all modern CPU and GPU setups and most operating systems.

Read Unsloth Desktop Blog

Here is an overview of everything Unsloth supports:

• Supports text, MLX, diffusion image/video, audio, GGUF and more models

• Self-healing tool calls are 50% more accurate, with sandboxed Python and Bash execution

• Image and video generation + training, with faster diffusion inference

• Day-zero support for Qwen3.8, Muse Glimmer 30B, MiniMax H3, Kimi K3, DeepSeek-V4 Flash and Gemma 4

• Unlimited private web search, Deep Research, RAG and MCP

• Parallel chats, each with its own tools, sandbox and prompt queue

• An OpenAI-compatible API, plus connections to cloud OpenAI and Anthropic models

• Secure remote access from any device via Cloudflare HTTPS

• Runs on NVIDIA, AMD, Intel, Apple Silicon, CPU and multi-GPU systems

Image + video generation 🎬

You can now generate and train images and videos locally, right inside Desktop. Diffusion is inference faster, LoRA workflows are supported, and video generation works on Apple Silicon through Metal and via CPU setups.

MiniMax H3 is supported and it generates video with synchronized audio, and takes images, videos or audio as references. On an NVIDIA B200, a 960×544, 124-frame, 8-step generation dropped from 70+ seconds to around 13 seconds.

Better tool calling 🔧

Tool calling is now 50% more accurate with self-healing tool calls. Your local models can run Python and Bash in a sandbox to calculate, analyze data, test code and generate files, and malformed tool calls get repaired and retried automatically. You can download any files or artifacts your chats produce.

You can also connect your local model to most agents. Run:

unsloth start claude

to connect your local model with Claude Code. You can switch between Claude Code, Codex, and other agents interchangeably.

Deep Research + Local RAG 🔎

Desktop includes unlimited private web search, deep research, RAG and MCP. Deep Research builds a plan, reads sources, tracks citations and produces a full report, and you can combine it with your own knowledge bases.

You can also link a local folder directly to RAG and it stays synced. Add, rename or update files and Unsloth handles it.

You can also start a new conversation while another model is still generating and do parallel chats.

Meta Muse Glimmer 30B

Today’s also comes with support for Meta’s new dense 30B coding model ‘Muse Glimmer’ which runs locally on around 18GB RAM/VRAM setups, and you can fine-tune the model with Unsloth.

Meta Muse Glimmer

Run Local Models Anywhere 💻

Unsloth Desktop works on NVIDIA, AMD, Intel/Vulkan, Apple Silicon, CPU and multi-GPU systems, with GPU layer controls, memory offloading and tensor parallelism for GGUF models. You can serve anything through the OpenAI-compatible API, expose it securely over Cloudflare HTTPS, and essentially access the model remotely via a phone or another device.

Unsloth Desktop GitHub

Unsloth Desktop is available now for Windows, macOS and Linux.

You can also download Unsloth Desktop from our GitHub releases page. We’d love for you to try it out and are more than happy for any feedback!

Star us on GitHub

A huge thanks to NVIDIA and Hugging Face for collaborating with us on this release! And most importantly, we’re grateful for our early alpha testers and couldn’t have done it without them.

We hope to share more exciting news soon, especially around Qwen3.8.

💚 Unsloth x AMD

We collaborated with AMD to enable you to train and run LLMs locally on AMD GPUs across 500+ models. Works on AMD Radeon, Instinct, Ryzen and data center GPUs across Windows, WSL, and Linux. You can train models 2× faster with 70% less VRAM and no accuracy loss on just 3GB VRAM. We also have optimized ROCm builds for GGUF and Safetensors inference. GitHub repo

AMD Blog

🧭 New Model Hub

We’re introducing a new Model Hub which makes it easier to discover, download and run models, with a trending feed, search, README previews and a resumable download manager. We also rebuilt model context auto-fitting, delivering up to ~3× longer context in several scenarios.

⚡ Dynamic NVFP4 quants + Better export

We’ve released new Dynamic NVFP4 quants for NVIDIA Blackwell GPUs, covering Qwen3.6 and Gemma 4. They enable faster and more accurate 4-bit inference. Unsloth can now also create merged FP8 and NVFP4 exports after training, plus imatrix-assisted GGUF quantization.

Dynamic NVFP4 Blog

💎 Gemma 4 improvements

The full Gemma 4, QAT family now runs and trains in Unsloth, including the new 12B model alongside E2B, E4B, 26B-A4B and 31B. We also uploaded new Dynamic NVFP4 quants. Recently, Google also added better tool calling and MTP for faster and improved local inference.

Gemma 4 Blog

🎨 Make Unsloth yours

Unsloth Studio’s appearance is now fully customizable, palettes, custom accent/background colors, fonts and accessibility settings, all synced across devices. We also now support 12 languages including Japanese, Chinese, Portuguese, Hindu, Arabic and others. Local transcription is also coming this week!

🐳 Better model and GPU control

We now have much more customization for model loading. Loading now exposes Flash Attention and tensor-parallel options. A new GPU Memory panel lets you choose which GPUs to use, set GPU layers and control MoE expert offload, or leave everything to Unsloth’s automatic fit mode.

🔮 New models

• DeepSeek-V4-Flash, GLM-5.2, Qwen3.6 - run the new agentic models

• Inkling, DiffusionGemma, Qwen-AgentWorld, Ornith, Kimi K2.7 Code and MiniMax-M3 - are all now available and supported in Unsloth.

Hope you have a lovely rest of July! We’ve got even more hardware, Unsloth and model updates on the way. 🦥

Thank you Google for providing Unsloth with day zero access. For more details on Gemma 4 and usage, read our blog / guide:

Gemma 4 Blog

✨ Unsloth Updates

Since we released Unsloth Studio 2 weeks ago, we’ve made many improvements. Most notably:

• 50-80% more accurate Tool calling: better parsing, better accuracy, faster execution, no raw tool markup in chat, plus a new Tool Outputs panel and timers.

• Pre-compiled llama.cpp binaries for ~1min installs and -50% less size

• Auto-detection of existing models / GGUFs

• 20–30% faster inference, now matches llama-server / llama.cpp speeds.

• New Desktop app shortcuts

• Data Recipes now supports macOS, CPU and multi-file uploads.

Unsloth Studio

Unsloth Studio now also installs in one command:

macOS, Linux, WSL:

curl -fsSL https://unsloth.ai/install.sh | sh

Windows:

irm https://unsloth.ai/install.ps1 | iex

A new way to use Unsloth is also coming soon…

• Run GGUF and safetensor models locally on Mac, Windows, Linux.

• Train 500+ models 2x faster with 70% less VRAM (no accuracy loss)

• Run and train text, vision, TTS audio, embedding models

Read Unsloth Studio Blog

• MacOS and CPU work for Chat GGUF inference. MLX training coming soon.

• ​Export your model to GGUF, 16-bit safetensor etc.

• ​Auto inference parameter tuning and edit chat templates.

Unsloth Studio allows ​Self-healing tool calling / web search + code execution​. LLMs can run code and programs in a sandbox so it can calculate, analyze data, test code, generate files, or verify an answer with actual computation. This makes answers from models more reliable and accurate.

No dataset needed. Transform PDFs, CSV, DOCX, TXT or any file into a structured synthetic datasets via Unsloth Data Recipes. Build and edit your datasets visually via a graph-node workflow and use them for fine-tuning.

Unsloth Studio provides the complete workflow, from data preparation and training to observability, inference, export, and deployment. You can now use Unsloth Studio locally via GitHub:

⭐ Star us on GitHub

Thank you so much for the support as always. Thank you to NVIDIA and Hugging Face for being great partners. We hope to release multiGPU and Apple Silicon MLX training very soon!

We’ve added support for many new models that you can now run and fine-tune locally, including Qwen3-Coder-Next, DeepSeek-OCR 2, GLM-4.7-Flash, Kimi-2.5, and more.

⭐ We’d also like to thank all of you for 50K stars on GitHub: https://github.com/unslothai/unsloth

💎 12× faster MoE training

You can now train MoE models 12× faster with >35% less VRAM and 6x longer context via our new Triton and math kernels (no accuracy loss). gpt-oss-20b works on 12.8GB VRAM. Qwen3-30B-A3B (16-bit LoRA) uses 63GB.

Unsloth supports fast training for gpt-oss, Qwen3 (30B, 235B, VL, Coder), DeepSeek R1/V3 arch and GLM (4.7, Flash) models. The larger the model and more context you use, the more pronounced the memory savings from our Unsloth kernels will be.

Faster MoE Blog

🔎 Embedding models now train 2× faster

Fine-tuning embedding models can largely improve retrieval and RAG performance on specific tasks. We collaborated with Hugging Face to enable 1.8-3.3x faster embedding, BERT and classifier model training with 20% less VRAM, 2x longer context & no accuracy loss vs. FA2 setups.

Embedding model Blog

💡 Ultra Long Context RL is here

Reinforcement learning’s (RL) biggest challenge is supporting long reasoning traces. Our new batching algorithms enable ~7x longer context (can be more than 12x) RL with no accuracy or speed degradation vs. other optimized setups that use FA3, kernels & chunked losses. Unsloth trains gpt-oss QLoRA with 380K context on a single 192GB NVIDIA B200 GPU

Long Context RL Blog

🔮 New models

• 🌠 Qwen3-Coder-Next: Run the newly released coding model.

• 🐳 DeepSeek-OCR 2 - Run and fine-tune the new OCR model.

• 🥝 Kimi 2.5 - Run the SOTA model locally with Unsloth GGUFs.

• ⚡ GLM-4.7-Flash - Run and fine-tune the best-in-class 30B LLM.

📖 New Guides

• </> How To Use Claude Code + Codex with local LLMs: Guide

• 👾 Train & deploy to LM Studio for local inference: Guide

• 🎨 Run Diffusion image models with Unsloth GGUFs: Guide

February is shaping up to be an amazing month for LLM releases, and we hope you’re just as excited as we are. 😊

✨ New Google model

Google releases FunctionGemma, a new 270M parameter model built for tool/function-calling. You can fine-tune it with Unsloth & later run/deploy on your phone. Learn how with our guide.

Our notebook turns FunctionGemma into a reasoning model by making it ‘think’ before tool-calling.

FunctionGemma Guide

🦥 3× Faster Training

You can now train 3× faster and use ~30% less VRAM (with no accuracy loss) via new RoPE + MLP Triton kernels, padding-free training, and uncontaminated packing. Train Qwen3-4B 3× faster on just 3.9GB VRAM.

3x Faster Blog

We also added a preliminary multi-GPU support: DDP Guide
This is an early preview (the official release is expected early next year).

🤗Transformers v5 is now supported, too!

📱 Deploy LLMs on your phone

We collaborated with PyTorch to enable you to fine-tune LLMs, and then directly deploy/run them on your phone. Read guide

🧠 500K context training

You can now do 500K context-length fine-tuning with Unsloth! Train gpt-oss-20B to extend its context window to 530K on 80GB VRAM and 750K+ on 192GB, with no accuracy loss.

Unsloth’s new algorithms + our collaboration with Snowflake enable ~72% less VRAM and up to 6× longer context lengths. Read the blog

💚 NVIDIA Nemotron 3

NVIDIA releases Nemotron 3, a new 30B hybrid reasoning model. You can now fine-tune and run the model locally with Unsloth. Nemotron 3 Guide

Also thanks to NVIDIA for collabing on a blogpost and video on how to do reinforcement learning (RL) with Unsloth: NVIDIA blog • Video Tutorial

🐱 New Mistral models

Mistral releases Devstral 2 and Mistral 3, new multimodal models that excel at coding + chat workflows: Ministral 3 • Devstral 2

You can fine-tune them all with Unsloth and we made a new reinforcement learning notebook for autonomously solving sudoku.

And that’s it, hope you have a wonderful Christmas and happy holidays! 🎁

⚡FP8 Reinforcement Learning

DeepSeek-R1 demonstrated how powerful FP8 can be, and faster RL inference is essential as it’s the most compute-intensive part of the workload. We’re introducing FP8-precision training for RL, making FP8 GRPO now possible on consumer GPUs (NVIDIA RTX 40, 50 etc).

Qwen3-1.7B FP8 GRPO now works on just 5GB of VRAM. We collabed with PyTorch for ~1.4× faster FP8 RL inference vs FP16 (even more at longer context). FP8 GRPO benefits from Unsloth’s features like weight sharing, Flex Attention, making it use 60% less VRAM and have 10x longer context than other implementations.

FP8 RL Blog

We also dug into the issue RL reward mismatch when using FP16 vs BF16 and found Unsloth does not have this issue.

Even better Unsloth!

You may notice Unsloth now uses much less VRAM than before, enabling even longer context. We’re also implementing faster training very soon and we’ll share all the details in an upcoming blog.

🚀 OpenAI DevDay collab

We collabed with OpenAI on a gpt-oss RL notebook that autonomously beats the 2048 game using RL. Training was done locally with Unsloth on NVIDIA DGX Spark. Our notebook example was showcased at OpenAI DevDay 2025.

💚 NVIDIA support

NVIDIA DGX Spark: Unsloth enables local fine-tuning of LLMs with up to 200B parameters on the NVIDIA DGX™ Spark. With 128 GB of unified memory, you can train massive models such as gpt-oss-120b, and run or deploy inference directly on DGX Spark.

Blackwell / RTX 50 series: Unsloth now supports NVIDIA’s Blackwell architecture GPUs, including RTX 50-series GPUs (5060–5090), RTX PRO 6000, and GPUs such as B200, GB100 and more! You can read our collab on the official NVIDIA blogpost.

🐋 DeepSeek-OCR

DeepSeek-OCR is a 3B-parameter vision model for OCR and document understanding. It uses context optical compression to convert 2D layouts into vision tokens, enabling efficient long-context processing.

Fine-tune DeepSeek-OCR to enhance its vision or language performance. In our Unsloth free fine-tuning notebook, we demonstrated a 88.26% improvement for language understanding.

🎯 Quantization-Aware Training (QAT)

We worked with TorchAO to enable trainable quantization that recovers as much accuracy as possible. This results in significantly better model quality compared to standard 4-bit naive quantization.

QAT can recover up to 70% of the lost accuracy and achieve a 1-3% model performance improvement on benchmarks such as GPQA and MMLU Pro.

🐳 Run LLMs locally via Docker × Unsloth

You can run any model, including Unsloth Dynamic GGUFs, on Mac, Windows or Linux with a single line of code or no code at all. We collabed with Docker to simplify model deployment, and Unsloth now powers most of Docker’s GGUF models.

🔮 Qwen3-VL

Qwen3-VL is Qwen’s new vision models with instruct and thinking versions. The 2B, 4B, 8B and 32B models are dense, while 30B and 235B are MoE. Unsloth supports Qwen3-VL fine-tuning and RL and Qwen3-VL (8B) can be trained for free with our notebooks.

Hope you have a lovely Thanksgiving (if you celebrate it) otherwise, a lovely rest of the week! =)

Aider is one of the most comprehensive benchmarks to test how well LLMs can write, code, follow instructions, and apply changes without human intervention.

DeepSeek GGUF Results

To make training easier, Unsloth now has a Docker image: Just run the container on Windows or Linux and start training without dependency issues. Guide

Here are rest of the Unsloth news:

🦥 Unsloth Updates

• Inference is crucial in RL training. To achieve the fastest inference speed for gpt-oss without vLLM, we rewrote Transformers to enable at least 3× faster inference for gpt-oss. You can train gpt-oss with GRPO/GSPO in our Colab notebook, where we showcase how you can automatically create faster GPU kernels.

gpt-oss RL Blog

• Vision RL is here with Gemma 3, Qwen2.5-VL and other vision models. Due to Unsloth’s unique weight sharing and custom kernels, Unsloth makes VLM RL 1.5–2× faster, use 90% less VRAM, and enables 10× longer context lengths than FA2 setups, with no accuracy loss.

Vision RL Blog

• IBM releases Granite-4.0 today, and Unsloth has Day Zero support. You can run with our Dynamic GGUFs or fine-tuning notebook which has a support agent example. Guide

• RL is now even faster & more memory efficient! Our new kernels & algos allows faster RL with 50% less VRAM & 10× more context. Blog

• Introducing Unsloth Flex Attention for gpt-oss that enables >8× longer context, >50% less VRAM and >1.5× faster. Blog

• We’re hosting a Developer event with Mistral AI & NVIDIA at Y Combinator’s Office in San Francisco on Oct 21. Come say hello!

We’ve got lots of new collabs and features over the next few weeks including a new product launch so stay tuned!

r/unsloth Reddit

We also investigated gpt-oss and did some chat template fixes so make sure to use Unsloth for training & our quants.

All other training libraries/methods will require a minimum of 65GB VRAM to train gpt-oss-20b, while Unsloth only requires 14GB VRAM. The 120b gpt-oss model fits in 65GB VRAM.

• Fine-tune gpt-oss-20b for free with our Google Colab notebook

• View all our gpt-oss model uploads with fixes analysis and Guide:

gpt-oss Blog

🦥 Unsloth Updates

• Our newest updated speeds up every single model while also reducing VRAM usage by >20%.

• Qwen upgraded Qwen3 and launched their SOTA coding models! Fine-tune and run Qwen3-Coder and Qwen-2507 with Unsloth.

  Qwen3 Guide

Happy July! 🦥 We’re excited to introduce lots of new updates for Unsloth:

✨ Gemma 3n

Run and fine-tune Google’s new Gemma 3n models. Gemma 3n supports text, image, video, and audio with 32K context. We’ve uploaded Dynamic quants, GGUFs, and released fine-tuning notebooks.

Gemma 3n Blog

🦥 Unsloth Updates

• We’ve released a new GitHub repo featuring 100+ Unsloth notebooks

• Qwen3 Advanced GRPO notebook with our new proximity scoring reward function and priming stage to skip GRPO format learning

• Unsloth now supports the latest vLLM, TRL, and Transformers.

• New Reinforcement Learning (RL) and LoRA hyperparameter guide

🔊 Text-to-Speech (TTS)

Unsloth supports TTS and Speech-to-text (STT) models including Sesame-CSM, Orpheus, OpenAI’s Whisper. We also made extensive notebooks for TTS and a Guide.

TTS Guide

🐳 DeepSeek-R1-0528

DeepSeek’s R1 update is here. Fine-tune DeepSeek-R1-0528-Qwen3 with GRPO. Our notebook features a new reward function which increases multilingual response rates by 40%.

Our Dynamic 1-bit GGUFs shrink the full 715GB model to 162GB (-80% size).

R1-0528 Blog

The new Qwen3 models deliver advancements in reasoning, instruction-following and multilingual support.

You can fine-tune Qwen3 up to 8x longer context lengths with Unsloth compared to all setups with FA2 on a 24GB GPU. Qwen3-30B-A3B comfortably fits on 17.5GB VRAM. We released a Colab notebook for Qwen3 (14B) here.

We uploaded Qwen3 GGUFs using our Unsloth Dynamic 2.0 method and also uploaded Qwen3 with native 128K context windows (up from 40K).

Read Qwen3 Blog

Other Highlights:

• Meta released Llama 4 in Scout and Maverick variants. You can run them locally with our Dynamic 2.0 GGUFs.

• We collaborated with Meta on a Synthetic Datasets notebook.

• Microsoft released their new reasoning models for Phi-4 which you can now run or fine-tune with Unsloth.

Thank you! If this email landed in your "Promotions" tab, drag it to your "Primary" inbox and ⭐️Star it to ensure you don’t miss an update from us. We appreciate your support!

Unsloth also now supports: full finetuning, reasoning (GRPO), 8-bit, pretraining, nearly all models (Mixtral, MOE, Cohere) etc. MultiGPU support is coming very soon.

We’d like to thank the Gemma team for their support and for featuring Unsloth in their release blogpost. Gemma 3 Training free notebooks and guide:

Gemma 3 Blog

Reasoning (GRPO) Support

Unsloth enables ten times more context lengths and uses 90% less VRAM for GRPO which was the algorithm DeepSeek used to train R1. Unsloth allows you to train your own reasoning model with just 5GB of VRAM using Qwen2.5 (1.5B).

With 24GB VRAM, you’ll be able to transform any model up to 24B in parameters, like Gemma 3 (12B), Llama 3.1 (8B) and Phi-4 (14B) into reasoning models.

R1 Reasoning Blog

Thank you! If this email landed in your "Promotions" tab, drag it to your "Primary" inbox and ⭐️Star it to ensure you don’t miss an update from us. We appreciate your support!

Hope you all had an amazing New Year! Here's our latest updates:

✨ Microsoft Phi-4

• Phi-4, Microsoft's new 14B model that performs on par with OpenAI's GPT-4o-mini is now in Unsloth!

• We fixed 4 bugs in Phi-4, greatly increasing the model’s accuracy.

• Unsloth Phi-4 finetuning enables >128K context lengths

Read Phi-4 Blog

🦙 Llama 3.3 + Ultra long context

• Llama 3.3 is Meta’s new 70B model that performs on par with Llama 405B.

• We worked with Apple to integrate CCE, making Unsloth now support 13x longer context lengths.

Read Llama 3.3 Blog

🦥 Unsloth Dynamic 4-bit

• We’re also excited to introduce Unsloth Dynamic 4-bit Quantization!

• Unlike naive quantization, which reduces accuracy, our method selectively avoids quantizing certain parameters, achieving significantly higher accuracy, while only using <10% more VRAM than BitsandBytes 4-bit.

View our Docs

To train your own custom Llama 3.2 Vision model, we uploaded Google Colab notebooks to fine-tune for free:

• Llama 3.2 Vision (11B)

Other multimodal models like Llava, Pixtral and Qwen VL are also supported. We uploaded models in GGUF, 16-bit and 4-bit formats. You can also now follow us on Hugging Face.

Visit our Documentation

Gradient Accumulation Bug Fixes

A while ago, we fixed a universal issue in Gradient Accumulation that negatively impacted everyone's training, pre-training & finetuning runs for sequence models like LLMs. Unsloth's Gradient Accumulation fix ensures training runs and loss calculations are performed accurately and correctly.

The goal of gradient accumulation is to mimic full batch training with reduced VRAM usage. Gradient accumulation is also used in DDP and multi GPU setups, so this issue affected large scale training runs as well.

Qwen 2.5 + Coder

Qwen 2.5 Coder (32B) released last week, matched GPT4o's performance on coding. Unsloth makes Qwen 2.5 finetuning 2x faster and use 60% less memory than Flash Attention 2 (FA2) + Hugging Face (HF).

Qwen Google Colab notebooks to finetune on a free Tesla T4:

• Qwen 2.5 Coder (14B)

• Qwen 2.5 (7B)

Thank you!

Thank you for reading and your support. If you have any questions, please reach out to us on Twitter (X) or via support@unsloth.ai.

• Pip install Unsloth now works!

• Multi GPU is now in beta with around 20 community members testing it!

• You can now fine-tune with CSV / Excel files with multiple columns

• Microsoft's new Phi 3.5 mini is now supported and we Llamafied it

• DPO is now fixed. All chat templates now work and we fixed chat templates for Llama + Phi models

• Please update your Unsloth Colab and Kaggle notebooks - all got updated with Torch 2.4

🦥 Our Mission + Studio Update

At Unsloth, our mission is to empower users to build the highest-quality models, the right way. We're deeply committed to advancing open-source development through collaborations with incredible teams like Hugging Face and Google, ensuring proper implementation of open models. We will continue to contribute to the development of open-source models, having previously addressed key bugs in models like Google’s Gemma and Meta’s Llama.

Unsloth Studio, our first release coming soon, will encompass many highly requested features and will be able to run in Google Colab for free - or you can install it locally and use it 100% offline. Thanks to our community, we understand developer's need for integrating with existing workflows, so we're making it easier for users to incorporate their favorite open-source packages like Jan, Ollama, or VLLM, or even just exporting for inference.

And as demand for models like stable diffusion, multimodal models, and voice models grows across industries, we’re also focused on revolutionizing how these models are trained and being deployed today—just as we’ve already transformed the landscape for language model training.

Join our mission by subscribing to our newsletteror following us on social media to stay updated. P.S. We’re also hiring so if you’re eager to unsloth it, reach out to us at support@unsloth.ai—we’d love to hear from you!

💎 The Platform

Using math algorithms, low-level GPU programming (Triton, CUDA) we aim to innovate the LLM ecosystem through software, not hardware. With innovations like our Unsloth Gradient Checkpointing algorithm, which reduces training memory usage by more than 50%, we're constantly pushing the boundaries of AI. We're working on our new methodologies and algorithms for our platform which results in a 10%-40% lift in accuracy for models trained with the same controlled environments.

Our platform will have our exclusive 30x faster fine-tuning and faster inference offering along with easy peasy deployment setups plus much more. With our innovations, we aim to become the place for building custom models from scratch, offering the most accurate and accessible solutions for anyone—whether you’re a seasoned expert or just starting out.

We also understand the frustrations users experience when training & deploying models and our aim is to for you to focus on creating the models you've always dreamed of, without the hassle. Say bye bye to dataset preparation, data & chat template issues, and more! We also recognize the importance of deep customization, therefore, we're also introducing numerous components to enhance customizability without compromising accuracy or speed.

The beta version of the platform will be out by the end of this year and we'd love any feedback on the platform when we release it. In the meantime you can join our waitlist.

Thank you for reading! 😊