Social Post Explorers

community
Activity Feed

AI & ML interests

None defined yet.

Recent Activity

social-post-explorers's activity

sayakpaul 
posted an update 1 day ago
ehristoforu 
posted an update 3 days ago
view post
Post
2670
✒️ Ultraset - all-in-one dataset for SFT training in Alpaca format.
fluently-sets/ultraset

❓ Ultraset is a comprehensive dataset for training Large Language Models (LLMs) using the SFT (instruction-based Fine-Tuning) method. This dataset consists of over 785 thousand entries in eight languages, including English, Russian, French, Italian, Spanish, German, Chinese, and Korean.

🤯 Ultraset solves the problem faced by users when selecting an appropriate dataset for LLM training. It combines various types of data required to enhance the model's skills in areas such as text writing and editing, mathematics, coding, biology, medicine, finance, and multilingualism.

🤗 For effective use of the dataset, it is recommended to utilize only the "instruction," "input," and "output" columns and train the model for 1-3 epochs. The dataset does not include DPO or Instruct data, making it suitable for training various types of LLM models.

❇️ Ultraset is an excellent tool to improve your language model's skills in diverse knowledge areas.
InferenceIllusionist 
posted an update 5 days ago
view post
Post
1849
MilkDropLM-32b-v0.3: Unlocking Next-Gen Visuals ✨

Stoked to release the latest iteration of our MilkDropLM project! This new release is based on the powerful Qwen2.5-Coder-32B-Instruct model using the same great dataset that powered our 7b model.

What's new?

- Genome Unlocked: Deeper understanding of preset relationships for more accurate and creative generations.

- Preset Revival: Breathe new life into old presets with our upgraded model!

- Loop-B-Gone: Say goodbye to pesky loops and hello to smooth generation.

- Natural Chats: Engage in more natural sounding conversations with our LLM than ever before.

Released under Apache 2.0, because sharing is caring!

Try it out: InferenceIllusionist/MilkDropLM-32b-v0.3

Shoutout to @superwatermelon for his invaluable insights and collab, and to all those courageous members in the community that have tested and provided feedback before!
neph1 
posted an update 7 days ago
view post
Post
1042
For those interested in game development I've released an experimental finetune of Qwen2.5-Coder for Unity.

neph1/Qwen2.5-Coder-7B-Instruct-Unity

It's using a mix of open source datasets + one specifically made for this (also OS) with multiple responses.

Also thinking about making a code completion model, or one to have more architectural discussions with.
  • 1 reply
·
sayakpaul 
posted an update 7 days ago
view post
Post
1547
In the past seven days, the Diffusers team has shipped:

1. Two new video models
2. One new image model
3. Two new quantization backends
4. Three new fine-tuning scripts
5. Multiple fixes and library QoL improvements

Coffee on me if someone can guess 1 - 4 correctly.
  • 1 reply
·
sayakpaul 
posted an update 16 days ago
view post
Post
2040
Introducing a high-quality open-preference dataset to further this line of research for image generation.

Despite being such an inseparable component for modern image generation, open preference datasets are a rarity!

So, we decided to work on one with the community!

Check it out here:
https://huggingface.co./blog/image-preferences
·
sayakpaul 
posted an update 16 days ago
view post
Post
2096
The Control family of Flux from @black-forest-labs should be discussed more!

It enables structural controls like ControlNets while being significantly less expensive to run!

So, we're working on a Control LoRA training script 🤗

It's still WIP, so go easy:
https://github.com/huggingface/diffusers/pull/10130
InferenceIllusionist 
posted an update 19 days ago
view post
Post
1799
Howdy folks, your friendly neighborhood fine-tuner I^2 here coming out of stealth with a thrilling model release announcement for you all.

🎵 Introducing MilkDropLM-v0.3: An LLM for Generating MilkDrop Presets
InferenceIllusionist/MilkDropLM-7b-v0.3



A weekend project that combines two of my favorite things - LLMs and music visualization. MilkDropLM is an experimental model trained to generate MilkDrop preset scripts, aiming to expand the creative possibilities in the visualization space. If you're familiar with WinAmp, MilkDrop was one of the many spiritual successors that spun out of its visualization features.

-Model Details-

Early alpha release (v0.3)
- Trained to generate MilkDrop preset syntax
- Based on qwen-2.5-coder-7b due to its math and programming ability
- Fine-tuned on 10k+ cream of the crop presets, hand-curated by a MilkDrop vet (shout out @superwatermelon !)
- Apache 2.0 licensed

The training process was fascinating - a lot of broader ML questions about this space that you can read all about on the model card.

This is still an early alpha so any feedback is appreciated but I'm excited to share the results with you all.

Feel free to experiment, contribute, or just grab some presets for your next visualization session. PRs and feedback always welcome!
Demo notebook and training details in the repo.

PS: A HUGE thank you to my followers old and new - your support does not go unnoticed. It's my absolute honor to share these experiments with you all!
lunarflu 
posted an update 20 days ago
ZennyKenny 
posted an update 23 days ago
sayakpaul 
posted an update 26 days ago
ZennyKenny 
posted an update about 1 month ago
view post
Post
1206
I've joined the Bluesky community. Interested to see what decentralized social media looks like in action: https://bsky.app/profile/kghamilton.bsky.social

Looking forward to following other AI builders, tech enthusiasts, goth doomscrollers, and ironic meme creators.
ZennyKenny 
posted an update about 1 month ago
view post
Post
345
Using AI to teach English as a Foreign Language? EFL teachers often have busy schedules, variable class sizes, and unexpected cancellations. Introducting VocabSova: ZennyKenny/VocabSova

VocabSova is a simple chatbot interface that helps teachers create topical vocabulary lists, custom worksheets using that vocabulary, and group activities on a defined theme for a specific English-speaking level (according to CEFR international standards).

There is a great use case for AI in nearly every field, and language learning is a particularly apt domain in my opinion. VocabSova is in active development during its Alpha release, all feedback welcome.
BrigitteTousi 
posted an update about 1 month ago
sayakpaul 
posted an update about 1 month ago
view post
Post
2599
It's been a while we shipped native quantization support in diffusers 🧨

We currently support bistandbytes as the official backend but using others like torchao is already very simple.

This post is just a reminder of what's possible:

1. Loading a model with a quantization config
2. Saving a model with quantization config
3. Loading a pre-quantized model
4. enable_model_cpu_offload()
5. Training and loading LoRAs into quantized checkpoints

Docs:
https://huggingface.co./docs/diffusers/main/en/quantization/bitsandbytes
  • 1 reply
·
not-lain 
posted an update about 1 month ago
view post
Post
1832
ever wondered how you can make an API call to a visual-question-answering model without sending an image url 👀

you can do that by converting your local image to base64 and sending it to the API.

recently I made some changes to my library "loadimg" that allows you to make converting images to base64 a breeze.
🔗 https://github.com/not-lain/loadimg

API request example 🛠️:
from loadimg import load_img
from huggingface_hub import InferenceClient

# or load a local image
my_b64_img = load_img(imgPath_url_pillow_or_numpy ,output_type="base64" ) 

client = InferenceClient(api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx")

messages = [
	{
		"role": "user",
		"content": [
			{
				"type": "text",
				"text": "Describe this image in one sentence."
			},
			{
				"type": "image_url",
				"image_url": {
					"url": my_b64_img # base64 allows using images without uploading them to the web
				}
			}
		]
	}
]

stream = client.chat.completions.create(
    model="meta-llama/Llama-3.2-11B-Vision-Instruct", 
	messages=messages, 
	max_tokens=500,
	stream=True
)

for chunk in stream:
    print(chunk.choices[0].delta.content, end="")
tomaarsen 
posted an update about 1 month ago
view post
Post
5296
I just released Sentence Transformers v3.3.0 & it's huge! 4.5x speedup for CPU with OpenVINO int8 static quantization, training with prompts for a free perf. boost, PEFT integration, evaluation on NanoBEIR, and more! Details:

1. We integrate Post-Training Static Quantization using OpenVINO, a very efficient solution for CPUs that processes 4.78x as many texts per second on average, while only hurting performance by 0.36% on average. There's a new export_static_quantized_openvino_model method to quantize a model.

2. We add the option to train with prompts, e.g. strings like "query: ", "search_document: " or "Represent this sentence for searching relevant passages: ". It's as simple as using the prompts argument in SentenceTransformerTrainingArguments. Our experiments show that you can easily reach 0.66% to 0.90% relative performance improvement on NDCG@10 at no extra cost by adding "query: " before each training query and "document: " before each training answer.

3. Sentence Transformers now supports training PEFT adapters via 7 new methods for adding new adapters or loading pre-trained ones. You can also directly load a trained adapter with SentenceTransformer as if it's a normal model. Very useful for e.g. 1) training multiple adapters on 1 base model, 2) training bigger models than otherwise possible, or 3) cheaply hosting multiple models by switching multiple adapters on 1 base model.

4. We added easy evaluation on NanoBEIR, a subset of BEIR a.k.a. the MTEB Retrieval benchmark. It contains 13 datasets with 50 queries and up to 10k documents each. Evaluation is fast, and can easily be done during training to track your model's performance on general-purpose information retrieval tasks.

Additionally, we also deprecate Python 3.8, add better compatibility with Transformers v4.46.0, and more. Read the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.3.0