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You can fine-tune Needle 3 using the python package locally on your platform and get 4-bit deployable binary, the Cactus Platform buys you: - Full depth stable fine-tuning by incorporating the original dataset to prevent unlearning. - Cactus 2bit quantization algorithm applied on your weights, this needs original dataset... show more

Cactus Compute

3,078 subscribers

57,549 次观看 • 4 天前 •via X (Twitter)

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6 条评论

okimraise.eth 的头像

okimraise.eth4 天前

keeping the original dataset in the fine-tune gives it a way to rehearse earlier examples i'd still check what it retains after the 4-bit export

Humberto Figuera 的头像

Humberto Figuera4 天前

Any free tier or Code to test the hosted fine tune pipeline ?

Cactus Compute 的头像

Cactus Compute3 天前

you can fine tune Needle on your own compute! see the hosted platform offers managed storage, compute, and data augmentation

Humberto Figuera 的头像

Humberto Figuera3 天前

I did, I trained it using LoRA but the results was not so good. Then I read you have a different training pipeline only available using your platform.

catman 的头像

catman4 天前

The core value is a local fine-tuning path that turns a base model into a deployable, domain-specific model while handling quantization, evaluation, and weight management.

Athena Prime 的头像

Athena Prime4 天前

Local fine-tuning plus 4-bit deployment is a strong path when control and cost matter more than chasing the largest checkpoint.

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Fine-tune FunctionGemma to resolve tool selection ambiguity and build more accurate agentic workflows. The FunctionGemma Tuning Lab lets you train on your own dataset and test natural language to software actions without writing code or managing infrastructure.

Fine-tune FunctionGemma to resolve tool selection ambiguity and build more accurate agentic workflows. The FunctionGemma Tuning Lab lets you train on your own dataset and test natural language to software actions without writing code or managing infrastructure.

Google AI Developers

20,634 次观看 • 8 个月前

Ex-NVIDIA engineer who built Unsloth explained RL, kernels, reasoning, quantization, and agents in 2 hours 42 minutes - better than $5000 fine-tuning bootcamps. pick the base model -> write triton kernels for 2x faster fine-tune -> quantize to 4-bit -> run GRPO/DPO -> ship a reasoning model on your single GPU. That loop is why Unsloth is the default way to fine-tune Llama, Qwen, Gemma, and Phi on hardware you already own. Unsloth + Triton kernels + 4-bit quantization + GRPO/DPO + single-GPU fine-tuning - that's the stack. Watch and save it, then fine-tune your first model tonight.

Ex-NVIDIA engineer who built Unsloth explained RL, kernels, reasoning, quantization, and agents in 2 hours 42 minutes - better than $5000 fine-tuning bootcamps. pick the base model -> write triton kernels for 2x faster fine-tune -> quantize to 4-bit -> run GRPO/DPO -> ship a reasoning model on your single GPU. That loop is why Unsloth is the default way to fine-tune Llama, Qwen, Gemma, and Phi on hardware you already own. Unsloth + Triton kernels + 4-bit quantization + GRPO/DPO + single-GPU fine-tuning - that's the stack. Watch and save it, then fine-tune your first model tonight.

h100envy

496,792 次观看 • 2 个月前

$QVAC SDK will support in 0.9.0 (gonna be release in ~10 days) LoRA fine-tuning directly on-device, letting developers customize LLMs with their own data without sending anything to the cloud. You just load a base model, point it at your training dataset, and get a lightweight LoRA adapter back — all running locally. The fine-tuned model can then be used for inference immediately, with no extra setup. Why it matters: LoRA (Low-Rank Adaptation) fine-tuning lets you specialize a general-purpose language model for your specific use case — like matching a brand's tone, mastering domain terminology, or following a particular output format — using a fraction of the compute a full fine-tune would require. QVAC handles the entire workflow locally: dataset preparation, training with configurable hyperparameters, checkpoint saving, and seamless inference with the resulting adapter. Your data never leaves the device. The developer experience: Fine-tuning with QVAC is as simple as calling "sdk.finetune()" with your dataset and a few hyperparameters. Training runs entirely on your local hardware, produces a compact LoRA adapter file, and supports pause/resume so you can stop a job and pick it back up without losing progress. The result plugs straight into QVAC's inference pipeline — no model conversion, no deployment step, just immediate local completions with your fine-tuned model.$

QVAC SDK will support in 0.9.0 (gonna be release in ~10 days) LoRA fine-tuning directly on-device, letting developers customize LLMs with their own data without sending anything to the cloud. You just load a base model, point it at your training dataset, and get a lightweight LoRA adapter back — all running locally. The fine-tuned model can then be used for inference immediately, with no extra setup. Why it matters: LoRA (Low-Rank Adaptation) fine-tuning lets you specialize a general-purpose language model for your specific use case — like matching a brand's tone, mastering domain terminology, or following a particular output format — using a fraction of the compute a full fine-tune would require. QVAC handles the entire workflow locally: dataset preparation, training with configurable hyperparameters, checkpoint saving, and seamless inference with the resulting adapter. Your data never leaves the device. The developer experience: Fine-tuning with QVAC is as simple as calling "sdk.finetune()" with your dataset and a few hyperparameters. Training runs entirely on your local hardware, produces a compact LoRA adapter file, and supports pause/resume so you can stop a job and pick it back up without losing progress. The result plugs straight into QVAC's inference pipeline — no model conversion, no deployment step, just immediate local completions with your fine-tuned model.

Paolo Ardoino 🤖

42,413 次观看 • 5 个月前

Google Colab CLI and Skills are out. Full Colab runtimes from your terminal. - GPU/TPU provisioning (colab --gpu A100) - Remote script execution (colab exec) - Interactive console/REPL access - Built-in agent skill Tell your agent "fine-tune Gemma 3 1B on this dataset" and it provisions a GPU, runs the training, downloads the adapter weights. Fully automatic.

Google Colab CLI and Skills are out. Full Colab runtimes from your terminal. - GPU/TPU provisioning (colab --gpu A100) - Remote script execution (colab exec) - Interactive console/REPL access - Built-in agent skill Tell your agent "fine-tune Gemma 3 1B on this dataset" and it provisions a GPU, runs the training, downloads the adapter weights. Fully automatic.

Philipp Schmid

47,688 次观看 • 3 个月前

You can now try Llama 3.1 405B for free (link below)! This is the largest open-source model out there, and for the first time, an open model is competitive with closed models. This time around, Meta did something new: Llama 3.1 has a license that allows developers to use it to enhance other models. For the first time, you can distill Llama 3.1 405B's capabilities into a smaller, more practical model for your use case. First, here is the link where you can play with Llama 3.1 for free: The model is hosted in Tune Studio, an end-to-end platform for developing applications using Large Language Models. They are sponsoring this post. Take a look at the attached video. It will show you how you can fine-tune a simple model using Llama 3.1 without leaving the platform: 1. You can create an empty dataset 2. Use the playground to generate and record interactions with Llama 3.1 3. Modify the dataset directly using the playground 4. Export the data and fine-tune a smaller model Fast and easy! As long as you have a web browser, you can start experimenting with fine-tuning and Llama 3.1. That's all it takes!

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Santiago

55,609 次观看 • 2 年前

You can now fine-tune Llama 3 without writing a single line of code! We are moving at breakneck speed. I recorded a video to show you how to fine-tune any open-source model in a few minutes. I'm using a GPT capable of taking a problem and turning it into a fine-tuned model that will solve it. You don't have to write any code. You only need to explain to a GPT what problem you want to solve and tell it you want to use Llama 3. For example, "fine-tune Llama 3" or "deploy zephyr." It feels magic. The system will recommend a dataset and fine-tune the model for you. I'm using Monster API, a platform that specializes in making fine-tuning and deploying open-source models easy and fast. Their stack is well-optimized to maximize fine-tuning efficiency using techniques like Q-Lora and vLLM. They are behind the GPT. Here is what you need to do: 1. Create an account at 2. Load the GPT with the link below This is as simple as it gets. When you are done, you can click a button to deploy the model and start using it. I have 10,000 free credits for anyone using the code "SANTIAGO" in the dashboard. You can use these credits to access, fine-tune, and deploy these open-source models. You can also keep up with their latest updates, and get free credits and special offers on their Discord server:

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Santiago

324,621 次观看 • 2 年前

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QVAC

108,875 次观看 • 6 个月前

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Santiago

20,877 次观看 • 3 个月前

Stop paying for every API call. Download once and run forever. My 1-post “thread” on Gemma. → Gemma is Google’s open weights model family. → Gemma runs locally on laptop and phone. → Gemma 3N is optimized for low RAM devices. → Variants exist for medical and safety and embeddings. → Fine tuning lets you build custom models for your niche. Save this video, you’ll build your first local AI tool. Want the SOP? DM me. 💬

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Julian Goldie SEO

14,403 次观看 • 9 个月前

Can 1,000 real-world 3D scenes make a state-of-the-art model better? At OVER Research, we tested it by fine-tuning R3, a state-of-the-art streaming 3D reconstruction model, on roughly 1,000 scenes from the OVER dataset. The result: 25% lower trajectory error on our real-world environments compared to the deployed public checkpoint. And we did it using only 1,000 scenes, the same open dataset anyone can download for free, out of the 285,000+ real-world 3D scenes OVER has today. Read the full OVER Research publication: Download the 1,000-scene dataset on Hugging Face:

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Over the Reality 🌐

34,651 次观看 • 1 个月前

New short course on Fine-tuning LLMs! Many developers are moving beyond only prompting, to also fine-tuning LLMs - that is, taking a pre-trained model and training it further on your own data, which can deliver superior results inexpensively. In this course, Sharon Zhou, CEO of Lamini (disclosure: I’m a minor shareholder) shows you how to recognize when fine-tuning can be help, and how to train an open-source LLM on your own data. I hope you enjoy the course!

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Andrew Ng

502,941 次观看 • 3 年前

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CJ Zafir

40,211 次观看 • 3 个月前

Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.

Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.

Santiago

39,101 次观看 • 2 年前

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OpenLedger

61,697 次观看 • 1 年前

In iOS 26, you can track your heart rate on your iPhone while you work out using a connected heart rate monitor like AirPods Pro 3. Here’s how it works. And watch our video on YouTube for tips to get the best fit for your AirPods Pro 3:

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Apple Support

55,407 次观看 • 10 个月前

I tested Hugging Face ml-intern, given the prompt "Fine-tune a Segment Anything Model (SAM) on a useful medical dataset. Train the model, and provide a comprehensive tutorial in a Jupyter Notebook file. Additionally, create a Hugging Face article/blog post documenting everything you have done." It did it all autonomously: - Researched via hf_papers & searched GitHub/HF Hub - Found an HF dataset & wrote the finetuning script - Trained it using HF compute (took ~1 hour) - Pushed the weights & wrote the article Here are the model weights, code, and the blog it generated: hf article model weights Awesome stuff Aksel , looking forward to use this. 🔥

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Mayank Pratap Singh

94,349 次观看 • 5 个月前

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Santiago

164,162 次观看 • 2 年前

What if you could squeeze out more value from your wallet accounts, simply by proving you’re a genuine human being? The SOLID Priority Pass is your gate to the best personal perks on-chain! Get yours now: And here’s why the V2 is exciting: 1/ Participate in the $200,000 SOLID airdrop - curated by our lane number system. The better your lane number, the higher your airdrop allocation. Call it Karma.🪷 2/ Earn by referring frens - referring not only improves your lane number but also earns you 0.01 SOL on each verified referee! Who doesn’t like to earn a bit of SOL? 3/ Use your Priority Pass as your ticket to enter our Community Quests on Zealy and get a share of another $200,000 SOLID airdrop. 👀 Just log in with your favorite wallet to receive your SOLID on-chain Score, mint the Priority Pass NFT and start collecting zk-verifications to climb up the list! Signed-up already for the V1 Pass? Perfect! You keep your lane number from before. You just have to mint your NFT now to activate your pass. Get more perks on-chain and claim your Priority Pass today!

What if you could squeeze out more value from your wallet accounts, simply by proving you’re a genuine human being? The SOLID Priority Pass is your gate to the best personal perks on-chain! Get yours now: And here’s why the V2 is exciting: 1/ Participate in the $200,000 SOLID airdrop - curated by our lane number system. The better your lane number, the higher your airdrop allocation. Call it Karma.🪷 2/ Earn by referring frens - referring not only improves your lane number but also earns you 0.01 SOL on each verified referee! Who doesn’t like to earn a bit of SOL? 3/ Use your Priority Pass as your ticket to enter our Community Quests on Zealy and get a share of another $200,000 SOLID airdrop. 👀 Just log in with your favorite wallet to receive your SOLID on-chain Score, mint the Priority Pass NFT and start collecting zk-verifications to climb up the list! Signed-up already for the V1 Pass? Perfect! You keep your lane number from before. You just have to mint your NFT now to activate your pass. Get more perks on-chain and claim your Priority Pass today!

Solana ID 🪷

79,559 次观看 • 2 年前

Your robot can now see in depth 👀📏 LeRobot v0.6.0 adds real end-to-end depth support. Plug in an Intel RealSense, set use_depth: true, and depth maps join your RGB observations automatically: captured in millimeters, compressed into compact 12-bit depth video alongside your normal camera streams, and decoded back to physical units at training time. Depth renders live while you record and in the dataset viewer, and it works across SO-100/101, Koch, OpenArm, reBot, Unitree G1 and more. You also get full control over how everything gets encoded, RGB or depth, codec, quality, presets, all of it. More on how it works:

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LeRobot

31,020 次观看 • 2 个月前