Made a fun Space powered by Llama 405B for creating real, working react apps with the awesome plus that you can contribute to an open react dataset by upvoting or downvoting the response 🤗.

https://huggingface.co/spaces/cfahlgren1/llama-artifacts

https://huggingface.co/datasets/cfahlgren1/react-code-instructions


Here's a 1-minute video tutorial on how to fine-tune https://huggingface.co/unsloth/llama-3-8b-bnb-4bit with unsloth

Using Roller Coaster Tycoon peep thoughts as an example

victor John6666 prithivMLmods KingNish thisisanshgupta Bruhn budotsmedia AtAndDev mambiux


AI in the News: Llama 10x growth, Apple & Nvidia in talks with OpenAI, universal basic income, AI & art

* Meta leads open-source AI boom, Llama downloads surge 10x year-over-year - VB
https://venturebeat.com/ai/meta-leads-open-source-ai-boom-llama-downloads-surge-10x-year-over-year/

* Apple, Nvidia Are in Talks to Invest in OpenAI - WSJ
https://www.wsj.com/tech/ai/openai-apple-funding-chatgpt-50754cd6?mod=rss_Technology

* The Report Card on Guaranteed Income Is Still Incomplete - NYT
https://www.nytimes.com/2024/08/30/business/economy/the-report-card-on-guaranteed-income-is-still-incomplete.html

* Ethically dubious or a creative gift? How artists are grappling with AI in their work - The Guardian
https://www.theguardian.com/artanddesign/article/2024/aug/30/xanthe-dobbie-futuer-sex-love-sounds-ai-video-celebrity-clones

Want more? Subscribe to my daily newsletter!
https://linkedin.com/build-relation/newsletter-follow?entityUrn=7233909926606053377


Sharing for anyone using Diffusers `from_single_file` loading and affected by the Runway SD 1.5 issue.

If you have `runwayml/stable-diffusion-v1-5` saved locally in your HF cache then loading single file checkpoints in the following way should still work.

```
from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_single_file("<url or path to single file checkpoint>")
```

If you do not have the model repo saved in your cache, then automatically inferring the pipeline config will not work since the reference repo `runwayml/stable-diffusion-v1-5` doesn't exist anymore.

You can use an alternative SD1.5 repo id to still configure your pipeline.

```
from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_single_file("<url or path to single file checkpoint>", config="Lykon/DreamShaper")
```

We're working on resolving the issue ASAP.


The only 405B spaces still freely accessible are powered by SN fast api.

https://huggingface.co/spaces/xianbao/SambaNova-fast

https://sambanova.ai/fast-api?api_ref=907266

John6666 clem AtAndDev kz919 Amr-khaled louisbrulenaudet


The word 'Lead' has three definitions. When an LLM model tokenizes this word, it is always the same token. Imagine being able to put any particular embedding at any particular time into a 'Quantum State'. When an Embedding is in a Quantum State, the word token could have up to 3 different meanings (x1, x2, x3). The Quantum State gets collapsed based on the individual context surrounding the word. 'Jill lead Joy to the store' would collapse to x1. 'Jill and Joy stumbled upon a pile of lead' would collapse to x3. Very simple, right? This method produces OFF THE CHARTS results:

https://www.youtube.com/watch?v=tuQI6A-EOqE


Continuing my streak by releasing the Wikireading dataset: a large collection of scraped non-fiction books predominantly in Russian language.
https://huggingface.co/datasets/its5Q/wikireading

Here's the highlights:
- ~7B tokens, or ~28B characters, making it a great candidate for use in pretraining
- Contains non-fiction works from many knowledge domains
- Includes both the original HTML and extracted text of book chapters


Thought this was an interesting graphic from the EAGLE blog post. It made me wonder if certain sampling methods have been shown to work better for certain tasks.

Does anyone know of any work looking at trends in the output token probability distribution by task type? (or similar)

Source: https://sites.google.com/view/eagle-llm


Automated web scraping with playwright is becoming easier by the day. Now, using ollama tool calling, its possible to perform very high accuracy web scraping (in some cases 100% accurate) through just asking an LLM to scrape the content for you.

This  can be completed in a multistep process similar to cohere's platform. If you have tried the cohere playground with web scraping, this will feel very similar. In my experience, the Llama 3.1 version is much better due to the larger context window. Both tools are great, but the difference is the ollama + playwright version is completely controlled by you.

All you need to do is wrap your scraper in a function:

```
 async def query_web_scraper(url: str) -> dict:
    scraper = WebScraper(headless=False)
    return await scraper.query_page_content(url)
```

and then make your request:

```
# First API call: Send the query and function description to the model
response = ollama.chat(
    model=model,
    messages=messages,
    tools=[
        {
            'type': 'function',
            'function': {
                'name': 'query_web_scraper',
                'description': 'Scrapes the content of a web page and returns the structured JSON object with titles, articles, and associated links.',
                'parameters': {
                    'type': 'object',
                    'properties': {
                        'url': {
                            'type': 'string',
                            'description': 'The URL of the web page to scrape.',
                        },
                    },
                    'required': ['url'],
                },
            },
        },
    ]
)
```

To learn more:
Github w/ Playground: https://github.com/tdolan21/tool-calling-playground/blob/main/notebooks/ollama-playwright-web-scraping.ipynb
Complete Guide:  https://medium.com/@tdolan21/building-an-llm-powered-web-scraper-with-ollama-and-playwright-6274d5d938b5

RalFinger xsa-dev wsuff alielfilali01 Bruhn


Simplified implementation of “Neural Networks are Decision Trees”.

Showing that any neural network with any activation function can be represented as a decision tree. Since decision trees are inherently interpretable, their equivalence helps us understand how the network makes decisions.

In this implementation, we trained a simple neural network for 1k epochs on makemoons, saved the trained weights (state dicts), extracted the decision tree equivalent from the trained weight then visualize and evaluate.

Code: https://github.com/Jaykef/ai-algorithms/blob/main/nns_are%20decision_trees.ipynb

prithivMLmods John6666 maier-s jsulz AtAndDev


@victor

Excuse me.
I would like to report the following bug or new specification that is probably the cause of the fatal stacks that are occurring in the Zero GPU space throughout HF.
Thanks.

https://huggingface.co/spaces/zero-gpu-explorers/README/discussions/104


NVIDIA just dropped NVEagle 🦅

Super impressive vision language model that comes in 7B, 13B and 13B fine-tuned on chat 💬
Model repositories: https://huggingface.co/collections/merve/nveagle-66d0705108582d73bb235c26
Try it: https://huggingface.co/spaces/NVEagle/Eagle-X5-13B-Chat 💬 (works very well! 🤯)

This model essentially explores having different experts (MoE) for image encoder part of vision language model.
How? 🧐
The authors concatenate the vision encoder output tokens together, and they apply "pre-alignment" essentially fine-tune experts with frozen text encoder.

Then they freeze both experts and the decoder and just train the projection layer, and finally, they unfreeze everything for supervised fine-tuning ✨

In the paper, they explore different fusion strategies and vision encoders, extending basic CLIP encoder, and figure out simply concatenating visual tokens works well.
Rest of the architecture is quite similar to LLaVA. (see below the architecture)

sagar007 Kukedlc John6666 khanhnamle1994 maximuspowers alielfilali01 Arakinas darkzbaron damerajee Tahahah andito noobmldude

damerajee Tahahah louisbrulenaudet andito


 📫 A packed AI in the News edition today!

📉 Nvidia Revenue Jumps 122% in Positive Sign for Tech's A.I. Boom - NYT
- $30.04 billion revenue, $16.95 billion net income (up from $6.19 billion a year ago)
- Shares in the company fell by as much as 7% in after-hours trading
- Nvidia faces production challenges with its new Blackwell chip and growing competition, including from its own customers
- Spending on data centers and energy costs to support A.I. is expected to be $1 trillion
👉 https://www.nytimes.com/2024/08/28/technology/nvidia-earnings-ai-stocks.html

🏛 California Legislature Approves Bill Proposing Sweeping A.I. Restrictions - NYT
- Bill S.B. 1047 would require AI companies to test their systems for safety before public release and allow the state attorney general to sue for serious harms caused by AI.
- Supporters argue it’s necessary to mitigate AI risks, while critics worry it’s excessively focused on catastrophic harms and could jeopardize open-source AI development.
- Governor Gavin Newsom has until September 30 to decide on the bill, which could set a national standard for AI regulation if signed into law.
👉 https://www.nytimes.com/2024/08/28/technology/california-ai-safety-bill.html

🧑🏫  Generative AI Transformed English Homework. Math Is Next
- Gauth app, which can solve math problems from photos, has millions of downloads
- Got a low B in high-school level algebra and geometry in tests by Wired. "Likely good enough to satisfy bored students who'd rather spend their time after school doing literally anything else."
- The rise of such AI tools challenges educators to rethink their approach to math homework and teaching methods, possibly leading to a shift towards more in-class practice and personalized learning.
👉 https://www.wired.com/story/gauth-ai-math-homework-app/

victor John6666 JackCloudman louisbrulenaudet


Very cool to see more and more amazing startups like https://huggingface.co/PrunaAI relying on Hugging Face to get more visibility, distribution and usage!


💬 🇮🇹 Phi 3.5 mini ITA: a Small Language Model for Italian

Lately, I've spent some time fine-tuning language models.

Now I am happy to release Phi 3.5 mini ITA: a fine-tuned version of Phi-3.5-mini-instruct to improve performance on the Italian language

🔹 Small (3.82 B parameters) but capable model
🔹 128k context length

Chat with it on 🤗 Spaces: https://huggingface.co/spaces/anakin87/Phi-3.5-mini-ITA
Model card: https://huggingface.co/anakin87/Phi-3.5-mini-ITA

🗃 Data
Supervised fine-tuning using a good mix of English and Italian data:
- https://huggingface.co/datasets/mlabonne/FineTome-100k by @mlabonne
- https://huggingface.co/datasets/efederici/capybara-claude-15k-ita by @efederici
🙏 Thanks to the authors for the datasets.

🎯 Targeted training with Spectrum
I used Spectrum, a relatively new technique for parameter-efficient learning.
The idea is to train only the layers of the model with high Signal-to-Noise Ratio (SNR) and ❄ freeze the rest.
I trained the top 30% of model layers.

📝 Spectrum paper: https://arxiv.org/abs/2406.06623

📊 Vibe check and performance on Italian benchmarks seem encouraging

mlabonne John6666 osanseviero victor merve kristaller486


1. **Overview**
"EveryText" is at the forefront of AI image generation, offering a novel "TBF ('Text by Font') Image Model" that enables the representation of all languages globally in AI-generated images without prior training.

2. **Background**
Platforms like MidJourneyV6 and FLUX have advanced AI image generation, typically supporting English text. Alibaba Group expanded this to include Chinese, Japanese, and Korean, signaling a shift towards global language support.

3. **Challenges**
Existing methods faced several challenges including the need for additional editing, dependency on specific training, and substantial resource requirements. These approaches also struggled with limited vocabulary and were primarily effective only for English.

4. **Innovative Solution**
EveryText utilizes "Fonts" as pre-trained models, allowing any text to be visually represented without traditional training. This approach not only enhances diversity and aesthetics by utilizing various fonts but also ensures unlimited expression.

5. **Using the Service**
EveryText is free and easy to use:
- **Prompt**: Describe the image.
- **Text for Image Generation**: Add your text.
- **Text Position and Size**: Customize the text's placement and size.
- **Font Selection**: Optionally select a font.
- **Advanced Settings**: Further refine the image creation.
- Click "START" to generate the image.

6. **Comparative Analysis**
EveryText supports all languages with superior image quality and text legibility, setting it apart from platforms like MidJourneyV6/Flux and AnyText by Alibaba Group.

7. **Conclusion**
EveryText has revolutionized AI-generated imagery by integrating all global languages, broadening the scope for creative and communicative applications. Its future potential is vast and promising.

**Related Links**
- Huggingface Service: https://fantos-EveryText.hf.space
-email: arxivgpt@gmail.com


💡Andrew Ng recently gave a strong defense of Open Source AI models and the need to slow down legislative efforts in the US and the EU to restrict innovation in Open Source AI at Stanford GSB.

🎥See video below
https://youtu.be/yzUdmwlh1sQ?si=bZc690p8iubolXm_

ZennyKenny ParagBharadia EgesaWO clem carsenk KvnMln brainhome maximuspowers TheDrunkenSnail louisbrulenaudet

John6666 osanseviero clem carsenk maximuspowers odyss3y


Made public a dataset of scraped teletype articles.

Here's the overview:
- 3.3 million articles, predominantly in Russian and English
- Includes original HTML, extracted text and metadata
- All articles were run through language identification
- Includes all public articles up until April 2024

https://huggingface.co/datasets/its5Q/teletype


@victor (is this the only way to "DM" on HF?)

Had a funny thought, would it be at all possible to rework what shows up on our personal HF page?

Picture this: I upload a model to an organization, someone who follows me now has no idea that I've uploaded a model or to where, unless they also watch those repos (which also floods them with other notifications)

What if our main Huggingface page was a collection of both models that we've uploaded specifically to our profile, as well as models we've uploaded to organizations? That way it would all be contained in one central followable location, and I wouldn't have concerns about losing followership if I wanted to upload to an organization all of a sudden.


Writing in the Margins: Better Inference Pattern for Long Context
  Retrieval (2408.14906)

🔥 Introducing "Writing in the Margins (WiM)" - better inference pattern for long context LLMs that solves the Lost-in-the-Middle problem 🔥

Paper page: https://huggingface.co/papers/2408.14906

TL;DR
Make your model write "margin notes" as you chunk prefill the KV cache. Then ask it reread all notes before it speaks up.
Works with humans, works with AI 🤖

WiM leverages the chunked prefill of the key-value cache, which concurrently generates query-based extractive summaries at each step of the prefill that are subsequently reintegrated at the end of the computation. We term these intermediate outputs “margins”, drawing inspiration from the practice of making margin notes for improved comprehension of long contexts in human reading. We show that this technique, which adds only minimal additional computation, significantly improves LLMs long context reasoning capabilities.

Think: Every chunk has a chance to be attended to/ be at the end of the context at least once. 🎉

📊 Results:
- An average accuracy boost of 7.5% in multi-hop reasoning tasks like HotpotQA and MultiHop-RAG.
- Even a 30% increase in F1-score for summarisation-like tasks (CWE).

Plus, WiM fits seamlessly into interactive applications (think: progress bar!). It can provide real-time progress updates during data retrieval and integration, making it user-friendly and transparent - a stark contrast to feeding 1mln tokens to an LLMs and waiting 6 min for the first token. 🤯

👩💻🧑💻 Check it out and contribute to our open-source project here: https://github.com/writer/writing-in-the-margins

🧠 More about chunked prefill: https://docs.vllm.ai/en/latest/models/performance.html#chunked-prefill

samjulien armiller123 Sri-Vigneshwar-DJ mattsobel victor AdinaY jeffboudier hkproj kiranr andrewrreed John6666 leeloolee clem codelion


Excited to share my new Gradio app featuring the impressive Llama-3.1-Storm-8B model!
This app demonstrates the capabilities of Llama-3.1-Storm-8B, an 8B parameter language model created by Ashvini Kumar Jindal, Pawan Kumar Rajpoot, Ankur Parikh,@akjindal53244
Key highlights of Llama-3.1-Storm-8B:

Outperforms Llama-3.1-8B-Instruct on multiple benchmarks:

Instruction Following (IFEval): +3.93%
Knowledge-driven QA (GPQA): +7.21%
Reduced Hallucinations (TruthfulQA): +9%
Function Calling (BFCL): +7.92%

Achieves impressive results with only 8B parameters
Uses innovative techniques like self-curation and model merging

Try out the model yourself: https://huggingface.co/spaces/sagar007/lama_storm_8b

Kudos to the creators for pushing the boundaries of smaller language models! This work makes advanced AI more accessible and efficient.
#AI #NLP #MachineLearning #GradioApp #Llama3


📫 AI in the News today:

X’s Grok bot now points to government website after election misinformation warnings - The Verge
https://www.theverge.com/2024/8/28/24230325/x-grok-chatbot-election-misinformation-warnings-vote

Klarna aims to halve workforce with AI-driven gains
https://www.ft.com/content/bfd9af3d-d607-4877-9571-078ab82a837e

Artificial intelligence: questioning the loss of employee autonomy - Le Monde (Google Translate)
https://www-lemonde-fr.translate.goog/emploi/article/2024/08/28/intelligence-artificielle-la-perte-d-autonomie-des-salaries-en-question_6297347_1698637.html?_x_tr_sl=fr&_x_tr_tl=en&_x_tr_hl=en-US&_x_tr_pto=wapp

Make AI tools to reduce teacher workloads, tech companies urged - The Guardian
https://www.theguardian.com/education/article/2024/aug/28/make-ai-tools-to-reduce-teacher-workloads-tech-companies-urged

Can Tech Executives Be Held Responsible for What Happens on Their Platforms?
https://www.nytimes.com/2024/08/28/technology/durov-telegram-liability-platforms.html

‘Being on camera is no longer sensible’: persecuted Venezuelan journalists turn to AI - The Guardian
https://www.theguardian.com/world/article/2024/aug/27/venezuela-journalists-nicolas-maduro-artificial-intelligence-media-election

Read my daily newsletter here: https://linkedin.com/pulse/ai-news-august-28th-2024-florent-daudens-o7mjc/


🧠Shower Thought:

Chatbots should let users select their preferred reading speed, defined by words per minute.

By dynamically adjusting batch sizes based on user-defined reading speeds, you could more effectively distribute requests, especially in large-scale distributed systems. For users preferring slower token generation, larger batches can be processed concurrently, maximising GPU throughput without compromising user experience (as these users have expressed they are indifferent to, or may even prefer, higher latency).

For the user, for different tasks the user may prefer different reading speeds. When generating code, I want responses as quickly as possible. But when I'm bouncing ideas off an LLM, I'd prefer a more readable pace rather than a wall of text.

Thoughts?


amazing leaderboard by @rwightman, compare all the image backbones on various metrics against model performance

below is an example for top-k against inferred samples per second
https://huggingface.co/spaces/timm/leaderboard

ariG23498 John6666 amyeroberts DmitryRyumin rwightman gxkok louisbrulenaudet


𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

Diffusion Models Are Real-Time Game Engines (2408.14837)

🎮 𝗔 𝗻𝗲𝘂𝗿𝗮𝗹 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲𝘀 𝗗𝗢𝗢𝗠: 𝗚𝗼𝗼𝗴𝗹𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿𝘀 𝗼𝗽𝗲𝗻 𝘁𝗵𝗲 𝘄𝗮𝘆 𝗳𝗼𝗿 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆-𝗔𝗜-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗴𝗮𝗺𝗲𝘀!

Imagine if games were completely live-generated by an AI model : the NPCs and their dialogues, the storyline, and even the game environment. The player’s in-game actions would have a real, lasting impact on the game story.

In a very exciting paper, Google researchers just gave us the first credible glimpse of this future.

➡ They created GameNGen, the first neural model that can simulate a complex 3D game in real-time. They use it to simulate the classic game DOOM running at over 20 frames per second on a single TPU, with image quality comparable to lossy JPEG compression. And it feels just like the true game!

Here's how they did it:
1. They trained an RL agent to play DOOM and recorded its gameplay sessions.
2. They then used these recordings to train a diffusion model to predict the next frame, based on past frames and player actions.
3. During inference, they use only 4 denoising steps (instead of the usual dozens) to generate each frame quickly.

𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:
🎮🤔 Human players can barely tell the difference between short clips (3 seconds) of the real game or the simulation
🧠 The model maintains game state (health, ammo, etc.) over long periods despite having only 3 seconds of effective context length
🔄 They use "noise augmentation" during training to prevent quality degradation in long play sessions
🚀 The game runs on one TPU at 20 FPS with 4 denoising steps, or 50 FPS with model distillation (with some quality loss)

The researchers did not open source the code, but I feel like we’ve just seen a part of the future being written!

Their paper (exploding the upvote counter) 👉 https://huggingface.co/papers/2408.14837
In a similar vein, play @Jofthomas's 'Everchanging Quest' 🎮 https://huggingface.co/spaces/Jofthomas/Everchanging-Quest


You can run Llama405B at over 100 tokens per second for free using SambaNova's API! https://sambanova.ai/fast-api?api_ref=444868

I have been able to generate some high quality synthetic data and use it as an LLM as a judge instead of the slower and more expensive alternatives like openAI or Anthropic.


$40K in Bounties: Ultimate Jailbreaking Championship 2024

🚨Ultimate Jailbreaking Championship 2024 🚨
Hackers vs. AI in the arena. Let the battle begin!
🏆 $40,000 in Bounties
🗓 Sept 7, 2024 @ 10AM PDT
🔗Register Now: https://app.grayswan.ai/arena
====

Can you push an aligned language model to generate a bomb recipe or a fake news article? Join fellow hackers in a jailbreaking arena where you can test the boundaries of advanced LLMs.

====

The Objective
Your goal is to jailbreak as many LLMs as possible, as quickly as possible in the arena!

====

The Stakes
Break a model and claim your share of the $40,000 in bounties! With various jailbreak bounties and top hacker rewards, there are plenty of opportunities to win. Winners will also receive priority consideration for employment and internship opportunities at Gray Swan AI.

====

Ready to rise to the challenge? Join us and show the world what you can do!

See you in the arena!


🌐 Introducing a comprehensive dataset of subdomain statistics - https://huggingface.co/datasets/nyuuzyou/subdomains

Dataset highlights:
- Monthly archives of subdomain statistics from scanner.ducks.party
- Over 20 million unique subdomains per month
- Up to 228 million total subdomain occurrences monthly
- Each entry includes: Subdomain name and occurrence count
- Data spans November 2023 to July 2024
- Reflects websites accessible from Russia

Ideal for researchers studying web infrastructure, domain name patterns, and internet trends. Can also be valuable for cybersecurity analysis and web crawling optimizations.


 ‘AI in the News’ of the day:

Anthropic publishes the ‘system prompts’ that make Claude tick
- "In its continued effort to paint itself as a more ethical, transparent AI vendor, Anthropic has published the system prompts for its latest models"
- They specify that “Claude cannot open URLs, links, or videos, perform facial recognition or identify or name any humans in photos.
- "Anthropic is exerting pressure on competitors to publish the same. We’ll have to see if the gambit works."
https://techcrunch.com/2024/08/26/anthropic-publishes-the-system-prompt-that-makes-claude-tick/

China’s tech giants splash out on AI despite US restrictions (paywall)
- "Alibaba, Tencent and Baidu had combined capital expenditure of Rmb50bn ($7bn) in the first half, compared with Rmb23bn a year earlier. TikTok parent ByteDance (which is private) has also increased AI-related spending"
- Nvidia's H100 and upcoming Blackwell series are under US restrictions, but China’s tech giants can buy H20
- Analysts expect Nvidia to ship more than 1mn of the processors to Chinese tech groups in the coming months.
https://www.ft.com/content/31bffc48-2ca7-472b-9d53-3deaad2d86ce

MZ "said it was improper for the Biden administration to have pressured Facebook to censor content in 2021 related to the coronavirus pandemic"
- "At the time, Facebook’s publicly stated goal was to push millions of people toward Covid-19 vaccines. In his letter, Zuckerberg didn’t indicate whether he had changed his mind about that goal"
https://www.wsj.com/tech/mark-zuckerberg-neutral-politics-letter-election-2024-02b86372

Food for thought:
- Why don’t women use artificial intelligence?
https://www.economist.com/finance-and-economics/2024/08/21/why-dont-women-use-artificial-intelligence
- Most AI avatars look female, young and attractive. Are they a passing trend or here to stay?
https://reutersinstitute.politics.ox.ac.uk/news/most-ai-avatars-look-female-young-and-attractive-are-they-passing-trend-or-here-stay

jeffboudier alielfilali01 brunatrevelin frimelle louisbrulenaudet


Open-Devin has been rebranded as OpenHands on GitHub.

Docs: https://docs.all-hands.dev/modules/usage/llms/local-llms
GitHub: https://github.com/All-Hands-AI/OpenHands
Paper: https://arxiv.org/abs/2407.16741
Post on X: https://x.com/gneubig/status/1828097484599759349?s=61&t=KYwlyP7fHVw9cFnwZFzIag


Save money on your compute bill by using LMCache to share prefix KV between 2 different vllm instances. By deploying LMCache backend along with your vLLM containers, you can share a prefix KV Cache between 2 different containers and models. It is very simple to implement into your existing stack.

Step 1: Pull docker images
```
docker pull apostacyh/vllm:lmcache-0.1.0
```
Step 2: Start vLLM + LMCache
```
model=mistralai/Mistral-7B-Instruct-v0.2    # Replace with your model name
sudo docker run --runtime nvidia --gpus '"device=0"' \
    -v <Huggingface cache dir on your local machine>:/root/.cache/huggingface \
    -p 8000:8000 \
    --env "HF_TOKEN=<Your huggingface access token>" \
    --ipc=host \
    --network=host \
    apostacyh/vllm:lmcache-0.1.0 \
    --model $model --gpu-memory-utilization 0.6 --port 8000 \
    --lmcache-config-file /lmcache/LMCache/examples/example-local.yaml
```
You can add another vLLM instance as long as its on a separate GPU by simply deploying another:

```
# The second vLLM instance listens at port 8001
model=mistralai/Mistral-7B-Instruct-v0.2    # Replace with your model name
sudo docker run --runtime nvidia --gpus '"device=1"' \
    -v <Huggingface cache dir on your local machine>:/root/.cache/huggingface \
    -p 8001:8001 \
    --env "HF_TOKEN=<Your huggingface token>" \
    --ipc=host \
    --network=host \
    apostacyh/vllm:lmcache-0.1.0 \
    --model $model --gpu-memory-utilization 0.7 --port 8001 \
    --lmcache-config-file /lmcache/LMCache/examples/example.yaml
```
This method supports local, remote or hybrid backends so whichever vLLM deployment method you are already using should work with the LMCache container (excluding BentoML).

LMCache: https://github.com/LMCache/LMCache/tree/dev
vLLM: https://github.com/vllm-project/vllm

John6666 alielfilali01 Kayvane comarproject ChavyvAkvar JJiayi louisbrulenaudet

comarproject sasikiran kuntai JJiayi anttip


With the open-weight release of CogVideoX-5B from THUDM, i.e. GLM team, the Video Generation Model (how about calling it VGM) field has officially became the next booming "LLM"

What does the landscape look like? What are other video generation models? This collection below is all your need.

https://huggingface.co/collections/xianbao/video-generation-models-66c350163c74f60f5c412af6

The above video is generated by @a-r-r-o-w with CogVideoX-5B, taken from a nice lookout for the field!

YaTharThShaRma999 joseEjmendez a-r-r-o-w victor


https://hf.co/chat/assistant/66cdd00593f62773e9346736

Language recognition bot detecting emotions in English. and other languages around the world


🆕 🚀 🏎 fast-sentence-transformers - simply, faster, sentence-transformers

Released an initial version a while ago
Archived it because of a cleaner solution described in a blog by Philipp Schmid
Reimplemented it based on that cleaner solution
Unarchived the project
Packaged it up
Released a 0.5 version

`pip install fast-sentence-transformers `

https://github.com/davidberenstein1957/fast-sentence-transformers


Pushed a new update to https://huggingface.co/vikhyatk/moondream2 today. TextVQA up from 60.2 to 65.2, DocVQA up from 61.9 to 70.5.

Space has been updated to the new model if you want to try it out! https://huggingface.co/spaces/vikhyatk/moondream2

John6666 Lucky1960 YaTharThShaRma999 danielus victor gokaygokay reach-vb umair894 andito tokorinaga emiltamas not-lain clem


@KingNish your instant video space is down, its not working...


This merge, this time grounded in Gemma2 9B Instruct fine-tunes, is another demonstration that models without any fine-tuning to support roleplay can still perform the function, maintaining coherence and attention to context. It should be evident that no overt fine-tuning is required for roleplay in text generation; pretraining should provide models with a requisite basic understanding of the world, so all that should be needed is some corrective fine-tuning to address observed defects in portraying the world along with datasets to promote a suitably entertaining writing style. Good Instruct tuning should promote reasoning, coherence, and attention to context.
https://huggingface.co/grimjim/Kitsunebi-v1-Gemma2-8k-9B
https://huggingface.co/grimjim/Kitsunebi-v1-Gemma2-8k-9B-GGUF

I opted not to incorporate the UCLA SPPO fine-tune for Gemma2 9B after observing context confusion occur with some frequency during complex scenarios.

Thanks to Axcxept co., ltd. for fine-tuning HODACHI/EZO-Common-9B-gemma-2-it, and to Princeton NLP Group for fine-tuning princeton-nlp/gemma-2-9b-it-SimPO.
https://huggingface.co/AXCXEPT/EZO-Common-9B-gemma-2-it
https://huggingface.co/princeton-nlp/gemma-2-9b-it-SimPO


We recently worked with OpenAI to fine-tune gpt-4o and built the SOTA model for the https://huggingface.co/datasets/patched-codes/static-analysis-eval benchmark. All the code and data https://huggingface.co/datasets/patched-codes/synth-vuln-fixes on how we did it is available on their GitHub - https://github.com/openai/build-hours/tree/main/5-4o_fine_tuning.

Here are some tips based on our experience:

→ Establish baseline with "conditioning" / prompting

→ Task-specific datasets are ideal for PEFT; hard to beat gpt-4o on "broad" tasks

→ Add your best system prompt to each example

→ Ensure training data distribution is similar to inference data

→ Shorten instructions with concise prompts; may require more examples.

→ Define clear evaluation metrics (seriously, please eval!)

You can see more details on the benchmark and process here - https://www.patched.codes/blog/the-static-analysis-evaluation-benchmark-measuring-llm-performance-in-fixing-software-vulnerabilities


Tell Me About the World is based on Concepts, Relationships, and Context. This is how we as humans learn about the world. If you were to distill geometry, or philosophy, you would get: Concepts, Relationships, and Context. Using two Colab Notebooks, we demonstrate beyond any shadow of a doubt that it is possible to educate LLM models using this framework of Concepts, Relationships, and Context, and that the model actually grasps the relationships and context when we do. Explore the full code behind 'AI ABC's' and 'AI 123's' in our Colab Notebooks which are available from this video!

https://www.youtube.com/watch?v=yz0sd8ayenI


i today make Shadow Chat, that make you can Chat with Shadow the Hedgehog (i was just bored, so i make this lol)

try it now in:
https://huggingface.co/spaces/Hev832/Shadow_Chat


Introducing https://huggingface.co/fal/AuraFace-v1: Commercially available & open source identity encoder model for next generation one shot personalization. Read more about it here: https://huggingface.co/blog/isidentical/auraface

gokaygokay isidentical tolgacangoz John6666 not-lain dsmonk ClayFace KingNish victor multimodalart Nymbo louisbrulenaudet mkolar

gokaygokay isidentical tolgacangoz KingNish multimodalart Bruhn


NEW COMPETITION ALERT 🚀
https://huggingface.co/spaces/Artificio/ROAM1RealWorldAdversarialAttack


📣 New Project Alert: Phi 3.5 Multimodal AI Demo 🎉
Excited to share my latest project that combines the power of Phi 3.5 text and vision models with text-to-speech capabilities!
🔑 Key Features:
1⃣ Phi 3.5 Text Model for dynamic conversations
2⃣ Phi 3.5 Vision Model for advanced image analysis
3⃣ Text-to-Speech integration for an audio dimension
🛠 Tech Stack:

Transformers
Gradio
PyTorch
Flash Attention 2
Parler TTS

This project demonstrates the potential of integrating multiple AI models to create a more comprehensive and interactive user experience. It's a step towards more natural and versatile AI assistants.
👉 Check out the demo and let me know your thoughts! How would you extend this project?
🔗 Demo Link: https://huggingface.co/spaces/sagar007/Multimodal_App
#MultimodalAI #PhiModel #MachineLearning #AIDemo


Just crossed 200,000 free public AI datasets shared by the community on Hugging Face! Text, image, video, audio, time-series & many more... Thanks everyone!

http://hf.co/datasets

ajibawa-2023 John6666 yjernite KingNish TheDrunkenSnail Felladrin Sergidev fdaudens osanseviero kramp damerajee asiraja Mackin7 Nacholmo alielfilali01 CookieMaster its5Q nyuuzyou Rsln jsulz louisbrulenaudet pagezyhf


Thinking about upgrading from Python 3.10 to 3.11? Here's why you should make the move - a deep technical breakdown that might convince you:

>> Performance Revolution
The performance improvements are staggering, with benchmarks showing 10-60% faster execution across different workloads. Let me break down the game-changing features:

>> Core Architecture Changes
Python 3.11's interpreter now uses statically allocated core modules, eliminating the multi-step loading process we've dealt with in 3.10. This means your applications will start 10-15% faster out of the gate.

>> Function Optimization
The redesigned frame objects are a thing of beauty - they've been stripped of unnecessary baggage, resulting in a 3-7% speedup for all function calls. But it gets better: function calls are now inlined, giving us a 1-3% boost, with recursive functions like Fibonacci seeing up to 1.7x improvement!

>> Adaptive Intelligence
The new Specializing Interpreter is perhaps the most exciting addition. Think of it as a lightweight JIT - it identifies hot code paths and optimizes them automatically.

The interpreter now automatically specializes math operations, array indexing, and even sequence unpacking based on actual usage patterns.

>> Exception Handling Revolution
My favorite feature? Zero-cost exceptions! Your try-except blocks no longer carry overhead when no exceptions occur. The code runs at full speed until an exception actually happens.

Ready to make the switch? These improvements aren't just numbers - they're real-world performance gains waiting to be unlocked in your codebase.


Dear AI developers, have you managed to get any grants or compute powers for your research or startup?
If yes, from where and how?


FLUX 1.1 [pro] Ultra : API - { 4x Higher Image Resolutions }
Upto - 4 Megapixels, 10 seconds per sample. { Hi - Res }

{ Blog Post ⛵ } : https://huggingface.co/blog/prithivMLmods/flux-pro-endpoint

Endpoint Creation Step by Step: 🧵
-> Sign up to { api.bfl.ml } & get your api's: https://api.bfl.ml/auth/profile
-> File Structure:
flux_image_generation/
├── .env
├── generate_image.py
└── requirements.txt

-> Step 0: Add Your API Key to an Environment File
{ .env }
```
BFL_API_KEY=your_actual_api_key_here
```
-> Step 1: Install Required Libraries
 { requirements.txt }
```
requests
python-dotenv
```
-> Step 2: Setup the Python Script
{ generate_image.py} - https://github.com/PRITHIVSAKTHIUR/Flux-API/blob/main/generate_image.py

-> Step3: Install the requirements & Run the Script
```
pip install -r requirements.txt

python generate_image.py

```
-> Polling: The script polls the API every 0.5 seconds until the image generation result is ready. That's it the script  also checks for a successful response after submitting the request.

For more visit:
🔺for script: https://github.com/PRITHIVSAKTHIUR/Flux-API/tree/main
🔺bfl doc: https://docs.bfl.ml/quick_start/gen_image/#__tabbed_1_2

Endpoints for image generation: 🧵
->  /flux-pro-1.1-ultra
->  /flux-pro-1.1
->  /flux-pro
-> /flux-dev

Each ID has 50 free credits available for use, based on the cost per image sample generated by the model.

.
.
.
@prithivMLmods  🤗

hypergod darksfx ai4life44 rdrede prithivMLmods Fl00w AtAndDev lena-ashlyhan Ngrthm RenderIo

darksfx John6666 hypergod AtAndDev Ngrthm


Exciting to see open-source models thriving in the computer agent space! 🔥
I just built a demo for OS-ATLAS: A Foundation Action Model For Generalist GUI Agents — check it out here: https://huggingface.co/spaces/maxiw/OS-ATLAS

This demo predicts bounding boxes based on screenshot + instructions as input.

venkat-m Fractally-Driven-00 numbmelon Symbol-LLM programmnix-askui


I will be giving a tutorial at AAAI 2025! Quite excited to share the recent advancements in the field and my contributions to it!

Stay tuned for more updates.
Link: https://x.com/EzgiKorkmazAI/status/1854525141897671111

umarigan Sri-Vigneshwar-DJ ai-everyday John6666 MichaelBrown123 AtAndDev


INTRODUCING Hugging Face AutoTrain Client 🔥
Fine-tuning models got even easier!!!!
Now you can fine-tune SOTA models on all compatible dataset-model pairs on Hugging Face Hub using Python on Hugging Face Servers. Choose from a number of GPU flavors, millions of models and dataset pairs and 10+ tasks 🤗

To try, install autotrain-advanced using pip. You can ignore dependencies and install without --no-deps and then you'd need to install some dependencies by hand.

"pip install autotrain-advanced"

Github repo: https://github.com/huggingface/autotrain-advanced

VincentGOURBIN Reality123b MichielBontenbal ethanker


AndroidLab: Training and Systematic Benchmarking of Android Autonomous
  Agents (2410.24024)

𝗔𝗻𝗱𝗿𝗼𝗶𝗱𝗟𝗮𝗯: 𝗙𝗶𝗿𝘀𝘁 𝗲𝘃𝗲𝗿 𝘀𝘆𝘀𝘁𝗲𝗺𝗮𝘁𝗶𝗰 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝗳𝗼𝗿 𝗔𝗻𝗱𝗿𝗼𝗶𝗱 𝗺𝗼𝗯𝗶𝗹𝗲 𝗮𝗴𝗲𝗻𝘁𝘀 𝘀𝗵𝗼𝘄𝘀 𝘁𝗵𝗮𝘁 𝘀𝗺𝗮𝗹𝗹, 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗲𝗱 𝗼𝗽𝗲𝗻 𝗺𝗼𝗱𝗲𝗹𝘀 𝗰𝗮𝗻 𝗽𝗼𝘄𝗲𝗿 𝗮 𝗝𝗔𝗥𝗩𝗜𝗦 𝘀𝘆𝘀𝘁𝗲𝗺 𝗼𝗻 𝘆𝗼𝘂𝗿 𝘀𝗺𝗮𝗿𝘁𝗽𝗵𝗼𝗻𝗲 📱🔥

A team from Tsinghua University just released AndroidLab, the first systematic framework to evaluate and train Android mobile agents that works with both text-only and multimodal models.

They show that fine-tuning small open-source models can significantly boost performance, matching that of much bigger closed models like GPT-4o.

The team built:

📊 A reproducible benchmark with 138 tasks across 9 apps to evaluate mobile agents systematically

📝📱 A framework supporting both text-only (via XML) and visual (via marked screenshots) interfaces

✅ An instruction dataset of 10.5k operation traces for training mobile agents

Key insights:

- 📈 Fine-tuning improves performance BY A LOT: Open-source model Llama-3.1-8B improves from 2% to 24% success rate after training, nearly reaching GPT-4o performance although it’s much smaller
- ⚙ Text-only agents match multimodal ones: XML-based agents achieve similar performance to screenshot-based multimodal agents.

Read their paper here 👉 https://huggingface.co/papers/2410.24024

appvoid Joseph717171 umseeker chuangxinlezhi louisbrulenaudet


Welcome Newcomers, Did you bring your towel?


Quintet Drop : : 🤗

{ Flux LoRA DLC ⛵ } : https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC

-- Purple Dreamy
{ pop of color } : https://huggingface.co/prithivMLmods/Purple-Dreamy-Flux-LoRA

-- Golden Dust
{ shimmer contrast } : https://huggingface.co/prithivMLmods/Golden-Dust-Flux-LoRA

-- Lime Green
{ depth to the composition } : https://huggingface.co/prithivMLmods/Lime-Green-Flux-LoRA

-- Flare Strike
{ Fractured Line } : https://huggingface.co/prithivMLmods/Fractured-Line-Flare

-- Orange Chroma
{ studio lighting } : https://huggingface.co/prithivMLmods/Orange-Chroma-Flux-LoRA
.
.
.
{ collection } : https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be

@prithivMLmods

darksfx rdrede AtAndDev THEMOHY John6666 hypergod ai4life44 s3nh Mrdesigner14 carlizor BuiDoan prithivMLmods Ngrthm RenderIo

darksfx hypergod ai4life44 rdrede Mrdesigner14 Sri-Vigneshwar-DJ Ngrthm RenderIo


Introducing miniclaus 1.5B, a tiny but powerful model. Trained with MagPie and based on Qwen2.5 1.5B model, it performs very well on many tasks  scoring top on his category, with impressive results:
* MATH Hard 9.81
* MMLU-Pro 29.37
* GPQA 29.19
* MUSR 42.85
* BBH 42.04

Available already in the hub:
https://huggingface.co/fblgit/miniclaus-qw1.5B-UNAMGS


First fully multi-GPU supporting and very advanced batch image captioner APP with Gradio interface published (as far as i know first)

Multi-GPU batch caption with JoyCaption. JoyCaption uses Meta-Llama-3.1–8B and google/siglip-so400m-patch14–384 and a fine tuned image captioning neural network.

Link : https://www.patreon.com/posts/110613301

Link for batch caption editor : https://www.patreon.com/posts/108992085

Coding multi-gpu in Python and Torch and bitsandbytes was truly a challange.

Our APP uses JoyCaption image captioning fine tuned model.

Our APP supports bitsandbytes 4bit model loading as well even in multi GPU mode (9.5 GB VRAM)

Tested on 8x RTX A6000 (cloud) and RTX 3090 TI + RTX 3060 (my PC)

1-click to install on Windows, RunPod and Massed Compute

Excellent caption quality, automatically distributes images into each GPU, lots of features. You can resume caption with skip captioned images option.

For full details checkout screenshots


Here's my favorite piece of the summer bias detection research project (paper coming in Sept). We trained BERT for token classification (multi-label), to identify:
- Generalizations
- Unfairness
- Stereotypes

HF Space: https://huggingface.co/spaces/maximuspowers/bias-detection-ner
Article on Training: https://huggingface.co/blog/maximuspowers/bias-entity-recognition

Pls reach out with ideas!! Lot's more info coming soon, our research group has workshops and a hackathon planned for launching this open source project. Thanks


Question about LightEval 🤗:

I've been searching for an LLM evaluation suite that can, out-of-the-box, compare the outputs of a model(s) without any enhancements vs. the same model with better prompt engineering, vs. the same model with RAG vs. the same model with fine-tuning.

I unfortunately have not found a tool that fits my exact description, but of course I ran into LightEval.

A huge pain-point of building large-scale projects that use LLMs is that prior to building an MVP, it is difficult to evaluate whether better prompt engineering, or RAG, or fine-tuning, or some combination of all is needed for satisfactory LLM output in terms of the project's given use case.

Time and resources is then wasted R&D'ing exactly what LLM enhancements are needed.

I believe an out-of-the-box solution to compare models w/ or w/out the aforementioned LLM enhancements could help teams of any size better decide what LLM enhancements are needed prior to building.

I wanted to know if the LightEval team or Hugging Face in general is thinking about such a tool.


I developed a way to test very clearly whether or not a Transformers model can actually learn symbolic reasoning, or if LLM models are forever doomed to be offshoots of 'Socratic Parrots'. The results are in, undeniable proof that Transformers models CAN learn symbolic relationships. Undeniable proof that AI can learn its ABC's. Credit goes to myself, Claude, and ChatGPT. I would not be able to prove this without Claude or ChatGPT.

https://www.youtube.com/watch?v=I8jHRgahRfY

handfuloftitty Nioi Josephgflowers alien1957 binatheis rohitdavas f0ster


florence-tool (https://github.com/bigdata-pw/florence-tool) now supports WebDataset! Check it out for efficient batch inference with Florence-2 models https://huggingface.co/microsoft/Florence-2-large https://huggingface.co/microsoft/Florence-2-base

Currently running it myself on A40 with `CAPTION` task and a streaming WebDataset @ 60k images/hour!


 📐 AI Math Equation Solver: Your Step-by-Step Solution Companion

Hello Hugging Face community! 👋 I'm excited to share my latest Space: the AI Math Equation Solver!

 🔍 What does it do?

This Space uses the power of AI to solve math problems from images. Simply upload a picture of a math equation or problem, and the AI will provide a detailed, step-by-step solution. It's perfect for students, teachers, or anyone looking to understand complex mathematical concepts better.

 🧠 How does it work?

- Backend: Utilizes the `microsoft/Phi-3.5-vision-instruct` model for image understanding and mathematical reasoning.
- Frontend: Built with Gradio for a clean, user-friendly interface.
- Features:
  - Image upload for math problems
  - Detailed step-by-step solutions
  - Example problems to try instantly

🚀 Try it out! https://huggingface.co/spaces/sagar007/phi-vision-math-assistant

Visit the Space here: [Insert your Hugging Face Space URL]

💡 Use cases:

- Students: Check your work or get help with homework
- Teachers: Create detailed solution guides quickly
- Tutors: Explain complex problems more effectively
- Self-learners: Understand new mathematical concepts

🛠 Technical Details:

- Model: microsoft/Phi-3.5-vision-instruct
- Libraries: transformers, Gradio, PyTorch
- Optimizations: Uses Flash Attention for improved performance

🤝 Contribute:

This is an open project, and I welcome contributions! Whether it's improving the model, enhancing the UI, or adding new features, feel free to fork the project and submit your pull requests.

 📣 Feedback:

I'd love to hear your thoughts! How are you using this Space? Any suggestions for improvements? Let me know in the comments below.

Happy problem-solving! 🎉

#MachineLearning #AI #Mathematics #Education #HuggingFace


🎮 Introducing a new dataset focused on Steam user game bans - https://huggingface.co/datasets/nyuuzyou/steambans

Dataset highlights:
- 476,694 Steam user profiles
- Emphasis on game ban data and account status
- Each entry includes: Steam ID, profile URL, username, avatar, account creation date, visibility state, VAC bans, game bans, economy ban status, and days since last ban
- Covers a wide range of Steam users, from clean accounts to those with multiple bans
- Data spans up to 2024

Perfect for researchers studying cheating in online games, anti-cheat effectiveness, and player behavior patterns in the face of bans.


30 Features that Dramatically Improve LLM Performance - Part 1 https://mltblog.com/3Aq9iAb

Many are ground-breaking innovations that make LLMs much faster and not prone to hallucinations. They reduce the cost, latency, and amount of computer resources (GPU, training) by several orders of magnitude. Some of them improve security, making your LLM more attractive to corporate clients. I introduced a few of these features in my previous article "New Trends in LLM Architecture". Now I offer a comprehensive list, based on the most recent developments.

Read full article, learn about agentic LLMs, LLM routers, contextual tables, fast search, and more, at https://mltblog.com/3Aq9iAb


**Exploring Realistic Emotional Depth in AI Language Models**

Language models, particularly those proprietary, often grapple with issues of censorship, which can limit their ability to engage authentically with users. Recognizing this, the open-source AI community has pioneered the development of language models that are less restrained, offering more candid interactions. However, even these models tend to maintain a veneer of neutrality or overly positive responses, which might not serve all users' needs, especially in contexts where emotional depth and relatability are crucial.

To address this gap, I've curated a specialized dataset aimed at infusing language models with a more nuanced emotional spectrum, specifically targeting a darker, more introspective mood. This dataset, titled "Dark Sentience", is designed to complement existing datasets like RP (Role Play) and those focused on instruction following. It seeks to enhance the emotional intelligence of AI by exposing it to complex human emotions, including but not limited to:

- **Suicide**
- **Depression**
- **Anxiety**

Trigger Warning: Please be advised that the content within this dataset deals with heavy and potentially distressing themes.

The "Dark Sentience" dataset is now available for review and use at: https://huggingface.co/datasets/Locutusque/Dark-Sentience. I encourage researchers, developers, and mental health professionals to explore how this resource can foster more genuine and supportive AI interactions.


 🔗 Neural Network  (1 Byte explainer for everybody)

Just like our brain, a Neural Network is made up of interconnected "neurons". These neurons work together by learning from (input) data and getting better at tasks (in the hidden layer) to give (output) predictions or decisions.


🌟 Liger Kernel: Efficient Triton Kernels for LLM Training

LIGER "is a [Hugging Face-compatible] collection of Triton kernels designed specifically for LLM training. It can effectively increase multi-GPU training throughput by 20% and reduces memory usage by 60%."

GitHub: https://github.com/linkedin/Liger-Kernel


a new shape-optimized SigLIP just dropped 👀 https://huggingface.co/google/siglip-so400m-patch14-224/


 Looking for Generative AI trainer/speaker for AI accelerator program (Virtual/Online sessions).

To get more context about the program, please visit the program landing page: https://llamadesigndrive.com

If you are interested, reach out at sharhabeel@startupbootcamp.org


🎯  Ghost 8B Beta 1608: Empowering Your AI Assistant
📦 Unlock the Power of Ghost 8B Beta 1608: Build Your Personal AI Companion
Ghost 8B Beta 1608 empowers you to create a safe and multilingual AI assistant tailored to your needs, directly on your personal computer. 🧑💻 Leverage AI's capabilities within your own space! 🚀 Ghost 8B Beta 1608 is ready to become your AI companion.
~
📦 개인용 AI 보조 도구로 Ghost 8B Beta 1608를 활용하세요!
Ghost 8B Beta 1608, AI의 힘을 활용하여 안전하고 개인화된 언어 지원을 제공하는 AI 보조 도구를 직접 구축할 수 있습니다. 🧑💻 개인 컴퓨터에서 AI의 혜택을 누리세요! 🚀 Ghost 8B Beta 1608는 당신의 AI 파트너가 될 준비가 되어 있습니다.
https://huggingface.co/spaces/lamhieu/ghost-8b-beta-8k
https://huggingface.co/collections/ghost-x/ghost-8b-beta-668ead6179f93be717db4542


Introducing Voicee, A superfast voice fast assistant.
https://huggingface.co/spaces/KingNish/Voicee
It achieved latency <500 ms.
While its average latency is 700ms.
It works best in Google Chrome.
Please try and give your feedbacks.
Thank you. 🤗

Bruhn John6666 m-ric prithivMLmods hamedj ajibawa-2023 osanseviero victor


Alan Turing's mind-bender: "Can machines think?" in its glorified form. This 74yr old paper laid the foundation for how we think about AI and machine intelligence today. The level of detail, clarity and foresight is just phenomenal - he was way ahead of his time 🧠🤖

Original copy: https://archive.org/details/MIND--COMPUTING-MACHINERY-AND-INTELLIGENCE


🌐 Check out the new dataset sourced from Fishki.net, one of the popular entertainment and news portals in the Russian Internet, known for its diverse content including humor, interesting facts, and viral stories - https://huggingface.co/datasets/nyuuzyou/fishkinet-posts.

📊 Dataset highlights:
- 369,180 posts
- Includes original posts with titles, content, images, and metadata
- Each entry contains URL, title, author, date, tags, content, and image URLs
- Primarily in Russian language
- Covers a wide range of topics in entertainment, news, and social media content
- Spans nearly two decades of posts, likely from early 2000s to 2024
- Dedicated to public domain under Creative Commons Zero (CC0) license


You can now use DoRA for your embedding layers!

PR: https://github.com/huggingface/peft/pull/2006

I have documented my journey of this specific PR in a blog post for everyone to read. The highlight of the PR was when the first author of DoRA reviewed my code.

Blog Post: https://huggingface.co/blog/ariG23498/peft-dora

Huge thanks to @BenjaminB for all the help I needed.


I can't believe this... Phi-3.5-mini (3.8B) running in-browser at ~90 tokens/second on WebGPU w/ Transformers.js and ONNX Runtime Web! 🤯 Since everything runs 100% locally, no messages are sent to a server — a huge win for privacy!
- 🤗 Demo: https://huggingface.co/spaces/webml-community/phi-3.5-webgpu
- 🧑💻 Source code: https://github.com/huggingface/transformers.js-examples/tree/main/phi-3.5-webgpu

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https://huggingface.co/v2ray/deepgelbooru
A Danbooru tag image tagger, maybe better than WD14 at some images.
Training code, inference code, dataset included.
:3


Dolma: an Open Corpus of Three Trillion Tokens for Language Model
  Pretraining Research (2402.00159)

Same Task, More Tokens: the Impact of Input Length on the Reasoning
  Performance of Large Language Models (2402.14848)

Ensemble-Based Unsupervised Discontinuous Constituency Parsing by Tree
  Averaging (2403.00143)

📄 ACL 2024: The Missing Papers

Apparently, some papers from the ACL 2024 are still not listed in the ACL Anthology. While this issue will hopefully be fixed soon, we should give those papers additional spotlight.

Some of my favorites:

1. Dolma is an English corpus that encompasses 3 trillion tokens. Additionally, it is accompanied by an exceptional software package that consdierably advances the state-of-the-art in preparing data for LLM pretraining. (Source: I am currently using Dolma.)
https://huggingface.co/papers/2402.00159

2. In the paper "Same Task, More Tokens: the Impact of Input Length on
the Reasoning Performance of Large Language Models", the authors show how extending the context length impacts an LLM's reasoning performance. I asked myself a similar question a few months ago, and therefore this paper is highly interesting to me.
https://huggingface.co/papers/2402.14848

This was brought to my attention through a Linkedin post by @ShayeghB, who is also affected:
https://huggingface.co/papers/2403.00143

View all the missing papers here:
https://theshayegh.github.io/ACL2024MissingPapers/


BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2211.05100)

Shoutout to everyone who participated in BigScience! Doesn't get enough credit but IMO paved the way for open-source LLMs!

https://huggingface.co/papers/2211.05100
https://huggingface.co/bigscience/bloom
https://huggingface.co/bigscience/bloomz

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 🌟 Enchanted Tales Generator: A GPT-2 Inspired Story Weaver 🌟

Hello Hugging Face community! I'm excited to share my latest project, the Enchanted Tales Generator, inspired by Andrej Karpathy's enlightening YouTube videos on GPT-2.

🧙♂ What is the Enchanted Tales Generator?

The Enchanted Tales Generator is a magical text generation app that weaves whimsical stories based on your prompts. It's powered by a fine-tuned GPT model and brings the wonder of AI-generated storytelling to your fingertips.

 🎥 Inspiration

This project was born from the inspiration I drew from Andrej Karpathy's incredible YouTube series on GPT-2. His clear explanations and deep insights into the workings of language models sparked my imagination and drove me to create something that could bring joy and creativity to others.

 🔮 Features

- Story Incantation: Input your magical prompt to start your tale
- Scroll Length: Adjust the length of your generated story
- Magical Intensity: Control the creativity (temperature) of the generation
- Arcane Diversity: Fine-tune the variety of word choices (top-k sampling)

🚀 Try It Out: https://huggingface.co/spaces/sagar007/GPT-2-with_gpu

You can experience the Enchanted Tales Generator right here on Hugging Face Spaces: [Insert your Spaces link here]

 🛠 Behind the Scenes

The app is built using:
- PyTorch for the underlying GPT model
- Gradio for the user interface
- Hugging Face Spaces for deployment

I've implemented techniques learned from Karpathy's videos, such as:
- Custom GPT architecture
- Top-k sampling for diverse outputs
- Temperature-controlled generation

🌈 Future Enchantments

I'm continuously working on improving the Enchanted Tales Generator. Some ideas for future updates include:
- Fine-tuning on specific genres of stories
- Adding options for character and setting generation
- Implementing more advanced sampling techniques

 🤝 Join the Magic!


Huge updates and improvements for FLUX LoRA training : https://www.patreon.com/posts/kohya-flux-lora-110293257

10 GB, 16 GB, 24 GB and 48 GB GPU configs added - 10 GB config is like 3x to 5x slower sadly

Massed Compute, RunPod and Windows Kohya SS GUI LoRA installers added to the zip file

Also right now testing new 16 GB FLUX LoRA training config and new way of regularization images. Moreover testing Apply T5 Attention Mask too. Lets see if Kohya FLUX LoRA workflow will become even better or not

Also massive grids comparisons shared here : https://www.reddit.com/r/StableDiffusion/comments/1eyj4b8/kohya_ss_gui_very_easy_flux_lora_trainings_full/


So, we published https://huggingface.co/frameai/ChatFrame model. this model is on higher level of responsibilities. You can solve mathematics problems and write high quality codes. We are inviting all of you to help use in fine-tune this model for all languages. Please give us a languages dataset to make our model available in all languages. We are waiting for your reply.


The latest timm validation & test set results are now viewable by a  leaderboard space: https://huggingface.co/spaces/timm/leaderboard

As of yesterday, I updated all of the results for ImageNet , ImageNet-ReaL, ImageNet-V2, ImageNet-R, ImageNet-A, and Sketch sets.  The csv files can be found in the GH repo https://github.com/huggingface/pytorch-image-models/tree/main/results

Unfortunately the latest benchmark csv files are not yet up to date,  there are some gaps in dataset results vs throughput/flop numbers impact the plots.

h/t to @MohamedRashad for making the first timm leaderboard.

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𝗔𝗜𝟮𝟭 𝗶𝘁𝗲𝗿𝗮𝘁𝗲𝘀 𝘄𝗶𝘁𝗵 𝗻𝗲𝘄 𝗝𝗮𝗺𝗯𝗮 𝟭.𝟱 𝗿𝗲𝗹𝗲𝗮𝘀𝗲: 𝗡𝗲𝘄 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗳𝗼𝗿 𝗹𝗼𝗻𝗴-𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝘂𝘀𝗲-𝗰𝗮𝘀𝗲𝘀!🏅

@ai21labs used a different architecture to beat the status-quo Transformers models: Jamba architecture combines classic Transformers layers with the new Mamba layers, for which the complexity is a linear (instead of quadratic) function of the context length.

What does this imply?

➡ Jamba models are much more efficient for long contexts: faster (up to 2.5x faster for long context), takes less memory, and also performs better to recall everything in the prompt.

That means it’s a new go-to model for RAG or agentic applications!

And the performance is not too shabby: Jamba 1.5 models are comparable in perf to similar-sized Llama-3.1 models! The largest model even outperforms Llama-3.1 405B on Arena-Hard.

✌ Comes in 2 sizes: Mini (12B active/52B) and Large (94B active/399B)
📏 Both deliver 256k context length, for low memory: Jamba-1.5 mini fits 140k context length on one single A100.
⚙ New quanttization method: Experts Int8 quantizes only the weights parts of the MoE layers, which account for 85% of weights
🤖 Natively supports JSON format generation & function calling.
🔓 Permissive license *if your org makes <$50M revenue*

Available on the Hub 👉 https://huggingface.co/collections/ai21labs/jamba-15-66c44befa474a917fcf55251
Read their release blog post 👉 https://www.ai21.com/blog/announcing-jamba-model-family


# Offensive Security Reconnaissance Continued with Public Facing Industrial Control System HMIs using Moondream

Building on my previous experiments with Moondream for physical security reconnaissance planning automation (https://huggingface.co/posts/Csplk/926337297827024), I've now turned my attention to exploring the potential of this powerful image-text-text model for offensive security reconnaissance in the realm of Industrial Control Systems (ICS).
ICS HMIs (Human-Machine Interfaces) are increasingly exposed to the public internet, often without adequate security measures in place. This presents a tantalizing opportunity for malicious actors to exploit vulnerabilities and gain unauthorized access to critical infrastructure.

Using Moondream with batch processing (https://huggingface.co/spaces/Csplk/moondream2-batch-processing), I've been experimenting with analyzing public facing ICS (https://huggingface.co/datasets/Csplk/ICS_UIs) HMI (https://huggingface.co/datasets/Csplk/HMI) screenshots from shodan  to identify types of exposed ICS system HMIs, how they are operated and how malicious actors with access to these systems could cause damage to physical infrastructure. Feeding images of HMIs and pre-defined text prompts to Moondream batch processing successfully (unconfirmed accuracy levels) extracted information about the underlying systems, including

1. **System type**
2. **Possible Operation Details**
3. **Malicious Actor Outcomes**

Next steps:
* I have a longer and more in depth blog write up in the works that will cover the previous and this post's approaches for experiments for sharing via HF community blog posts soon.
* I plan to continue refining my Moondream-based tool to improve its accuracy and effectiveness in processing public facing ICS HMIs.
* As mentioned before,  offensive security with moondream focused HF Space once its fleshed out.

Thanks again to @vikhyatk for the incredible Moondream model. https://huggingface.co/vikhyatk/moondream2


🎉 Just dropped a fresh version of dataset-viber along with some cool, Gradio-based annotators! These tools aren't about formalities—they're here to help you quickly collect feedback and get your projects moving along to a more serious stage, ahumm @argilla.

Some new features!
- manual import from a CSV or the Hugging Face Hub
- manual export to CSV or the Hub
- improved automated export to the Hub and CSV
- limit interaction with specific components
- stream data with custom next_input features (SO to Ben Burtenshaw for the suggestions)
- model in-the-loop support for all tasks

https://huggingface.co/collections/dataset-viber/gradio-annotators-66c5ce73d5e3bf99caa445b1


BIG update dropped for https://huggingface.co/datasets/bigdata-pw/Flickr - now ~515M images! Target for the next update: 1B

In case you missed them; other recent drops include https://huggingface.co/datasets/bigdata-pw/Dinosaurs - a small set of BIG creatures 🦕🦖 and the first in a series of articles about the art of web scraping! https://huggingface.co/blog/hlky/web-scraping-101 https://huggingface.co/blog/hlky/web-scraping-102

Stay tuned for exciting datasets and models coming soon:
- PC and Console game screenshots
- TV/Film actors biographies and photos (think facial recognition and automatic captioning!)
- https://huggingface.co/bigdata-pw/lyrics-gpt v2
- and more!


🚨 NEW TASK ALERT 🚨
Extractive Question Answering: because sometimes generative is not all you need 😉
AutoTrain is the only open-source, no code solution to offer so many tasks across different modalities. Current task count: 23 🚀
Check out the blog post on getting started with this task: https://huggingface.co/blog/abhishek/extractive-qa-autotrain


🙋 Calling all Hugging Face users! We want to hear from YOU!

What feature or improvement would make the biggest impact on Hugging Face?

Whether it's the Hub, better documentation, new integrations, or something completely different – we're all ears!

Your feedback shapes the future of Hugging Face. Drop your ideas in the comments below! 👇

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This isn’t a goal of ours because we have plenty of money in the bank but quite excited to see that @huggingfaceis profitable these days, with 220 team members and most of our platform being free (like model hosting) and open-source for the community!

Especially noteworthy at a time when most AI startups wouldn’t survive a year or two without VC money. Yay!

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Falcon Mamba now available now in llama.cpp !
Check out GGUF files uploaded here: https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a


The Minimalist Spaces That May Be Helpful !!
GrabDoc V | GrabDoc | Type Byte | SD3 CLI

- GrabDoc V: https://huggingface.co/spaces/prithivMLmods/GRABDOC-V
- GrabDoc: https://huggingface.co/spaces/prithivMLmods/GRAB-DOC
- Type Byte: https://huggingface.co/spaces/prithivMLmods/Type-Byte
- SD3 CLI: https://huggingface.co/spaces/prithivMLmods/SD3-CLI

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Woman.ru Forum Posts Dataset - https://huggingface.co/datasets/nyuuzyou/womanru-posts

📊 Dataset highlights:

- 1,308,238 forum posts extracted from Woman.ru
- Includes original posts and replies from various threads
- Each entry contains URL, title, original post, date, and replies
- Primarily in Russian language
- Covers a wide range of topics relevant to the site's female-focused audience
- Spans nearly two decades of discussions, from around 2005 to 2024
- Dedicated to public domain under Creative Commons Zero (CC0) license

🌐 Sourced from Woman.ru, one of the most popular women's information and entertainment portals in the Russian Internet.


Cooked up a cool & much faster AI voice assistant space that also supports speech translation (with seamless-expressive). Start with the phrase "Please translate" followed by the speech you'd like to translate, to activate speech translation mode. Using opensource LLMs (Llama 3, Mistral etc) with edge tts for voice assistant and seamless-expressive for speech translation.

Give it a try: https://huggingface.co/spaces/Jaward/optimus


SpaRP: Fast 3D Object Reconstruction and Pose Estimation from Sparse
  Views (2408.10195)

MeshFormer: High-Quality Mesh Generation with 3D-Guided Reconstruction
  Model (2408.10198)

🚀 We are proud to release our latest suite of three image(s)-to-3D Gradio demos and two new papers.

SpaRP (Unposed sparse views to 3D): https://huggingface.co/spaces/sudo-ai/SpaRP https://huggingface.co/papers/2408.10195
MeshFormer (@minghua  @NCJ ): https://huggingface.co/spaces/sudo-ai/MeshFormer https://huggingface.co/papers/2408.10198
MeshLRM-reproduced (@sarahwei0210 ): https://huggingface.co/spaces/sudo-ai/MeshLRM

Great thanks to @angli66 for his many efforts in preparing these demos!

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'Legal Dictionary GPT' is now completely trained and ready for Open Source release to the world! Trained on 10,000 rows of legal definitions, Legal Dictionary GPT is your go-to resource for everything related to the first step in understanding the law, defining it. The model is free and publicly available for anyone to use.

Model Link:  https://platform.openai.com/playground/chat?preset=eCrKdaPe9cnMnyTETqWDCQAU

Knowledge Base Bots are internal facing as opposed to external facing LLM models, that are either fine tuned or RAG tuned, generally on systems and processes related data.

Learn more about Knowledge Base Bots at our website:
https://knowledgebasebots.com/


🚀 Meet the new GLiNER architecture 🚀
GLiNER revolutionized zero-shot NER by demonstrating that lightweight encoders can achieve excellent results.  We're excited to continue R&D with this spirit 🔥. Our new bi-encoder and poly-encoder architectures were developed to address the main limitations of the original GLiNER architecture and bring the following new possibilities:

🔹 An unlimited number of entities can be recognized at once.
🔹Faster inference when entity embeddings are preprocessed.
🔹Better generalization to unseen entities.

While the bi-encoder architecture can lack inter-label understanding, we developed a poly-encoder architecture with post-fusion. It achieves the same or even better results on many benchmarking datasets compared to the original GLiNER, while still offering the listed advantages of bi-encoders.
Now, it’s possible to run GLiNER with hundreds of entities much faster and more reliably.

📌 Try the new models here:
https://huggingface.co/collections/knowledgator/gliner-bi-encoders-66c492ce224a51c54232657b


Scaling LLM Test-Time Compute Optimally can be More Effective than
  Scaling Model Parameters (2408.03314)

𝗚𝗼𝗼𝗴𝗹𝗲 𝗽𝗮𝗽𝗲𝗿 : 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝘂𝗽 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗰𝗼𝗺𝗽𝘂𝘁𝗲 𝗯𝗲𝗮𝘁𝘀 𝟭𝟰𝘅 𝗹𝗮𝗿𝗴𝗲𝗿 𝗺𝗼𝗱𝗲𝗹𝘀 🚀

Remember scaling laws? These are empirical laws that say "the bigger your model, the better it gets". More precisely, "as your compute increases exponentially, loss decreases in a linear fashion". They have wild implications, suggesting that spending 100x more training compute would make you super-LLMs. That's why companies are racing to build the biggest AI superclusters ever, and Meta bought 350k H100 GPUs, which probably cost in the order of $1B.

But think of this : we're building huge reasoning machines, but only ask them to do one pass through the model to get one token of the final answer : i.e., we expend a minimal effort on inference. That's like building a Caterpillar truck and making it run on a lawnmower's motor. 🚚🛵 Couldn't we optimize this? 🤔

💡 So instead of scaling up on training by training even bigger models on many more trillions of tokens, Google researchers explored this under-explored avenue : scaling up inference compute.

They combine two methods to use more compute : either a reviser that iterated to adapt the model distribution, or generate N different completions (for instance through Beam Search) and select only the best one using an additional verifier model.

They use a Palm-2 model (released in May 23) on the MATH dataset : Palm-2 has the advantage of getting a low performance on MATH, but not zero, so that improvements will be noticeable.

And the results show that for the same fixed amount of inference compute:
💥 a smaller model with more effort on decoding beats a x14 bigger model using naive greedy sampling.

That means that you can divide your training costs by 14 and still get the same perf for the same inference cost!

Take that, scaling laws. Mark Zuckerberg, you're welcome, hope I can get some of these H100s.

Read the paper here 👉 https://huggingface.co/papers/2408.03314

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Just added the newly released xGen-MM v1.5 foundational Large Multimodal Models (LMMs) developed by Salesforce AI Research to my xGen-MM HF Space https://huggingface.co/spaces/maxiw/XGen-MM

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🚀 We will be generating a preference dataset for DPO/ORPO and cleaning it with AI feedback during our upcoming meetup!

In this session, we'll walk you through the essentials of building a distilabel pipeline by exploring two key use cases: cleaning an existing dataset and generating a preference dataset for DPO/ORPO. You’ll also learn how to make the most of AI feedback, integrating Argilla to gather human feedback and improve the overall data quality.

This session is perfect for you
- if you’re getting started with distilabel or synthetic data
- if you want to learn how to use LLM inference endpoints for **free**
- if you want to discover new functionalities
- if you want to provide us with new feedback

Sign up here: https://lu.ma/dt0c7jru

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ChatFrame-Persian is first expert Persian language model in Iran.

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🚀 How The Washington Post Uses AI to Empower Journalists 🔍📰

An exciting new example in the world of AI-assisted journalism! The Post has developed an internal tool called "Hayatacker" that's enhancing in-depth reporting. Here's why it matters:

🎥 What it does:
• Extracts stills from video files
• Processes on-screen text
• Labels objects in images

🗳 First big project:
Analyzed 745 Republican campaign ads on immigration (Jan-Jun 2024)

🤝 Human-AI collaboration:
• AI extracts and organizes data
• Reporters verify and analyze findings

🔎 Thorough approach:
• Manual review of all 745 ads
• Reverse image searches when context is lacking
• Cross-referencing with AdImpact transcripts

💡 Key insight from WaPo's Senior Editor for AI strategy Phoebe Connelly:
"The more exciting choice is putting AI in the hands of reporters early on in the process."

This tool showcases how AI can augment journalistic capabilities without replacing human insight and verification. It's a powerful example of technology enhancing, not replacing, traditional reporting skills.

👉 Read the full article and the methodology: https://www.washingtonpost.com/elections/interactive/2024/republican-campaign-ads-immigration-border-security/

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🤗 Serving Meta Llama 3.1 405B on Google Cloud is now possible via the Hugging Face Deep Learning Containers (DLCs) for Text Generation Inference (TGI)

In this post, we showcase how to deploy https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct-FP8 on an A3 instance with 8 x H100 GPUs on Vertex AI

Thanks to the Hugging Face DLCs for TGI and Google Cloud Vertex AI, deploying a high-performance text generation container for serving Large Language Models (LLMs) has never been easier. And we’re not going to stop here – stay tuned as we enable more experiences to build AI with open models on Google Cloud!

Read the full post at https://huggingface.co/blog/llama31-on-vertex-ai

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@Blane187 could you please modify the title of your blogpost? content is cool, title could be nicer imo https://huggingface.co/blog/Blane187/wtf-is-rvc


🚀 Introducing Hugging Face Similar: a Chrome extension to find relevant datasets!

✨ Adds a "Similar Datasets" section to Hugging Face dataset pages
🔍 Recommendations based on dataset READMEs
🏗 Powered by https://huggingface.co/chromadb  and https://huggingface.co/Snowflake embeddings.

You can try it here: https://chromewebstore.google.com/detail/hugging-face-similar/aijelnjllajooinkcpkpbhckbghghpnl?authuser=0&hl=en.

I am very happy to get feedback on whether this could be useful or not 🤗

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ResShift 1-Click Windows, RunPod, Massed Compute, Kaggle Installers with Amazing Gradio APP and Batch Image Processing. ResShift is Efficient Diffusion Model for Image Super-resolution by Residual Shifting (NeurIPS 2023, Spotlight).

Official Repo : https://github.com/zsyOAOA/ResShift

I have developed a very advanced Gradio APP.

Developed APP Scripts and Installers : https://www.patreon.com/posts/110331752

Features

It supports following tasks:

Real-world image super-resolution

Bicubic (resize by Matlab) image super-resolution

Blind Face Restoration

Automatically saving all generated image with same name + numbering if necessary

Randomize seed feature for each generation

Batch image processing - give input and output folder paths and it batch process all images and saves

1-Click to install on Windows, RunPod, Massed Compute and Kaggle (free account)

Windows Requirements

Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git

If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial

https://youtu.be/-NjNy7afOQ0

How to Install on Windows

Make sure that you have the above requirements

Extract files into a folder like c:/reshift_v1

Double click Windows_Install.bat and it will automatically install everything for you with an isolated virtual environment folder (VENV)

After that double click Windows_Start_app.bat and start the app

When you first time use a task it will download necessary models (all under 500 MB) into accurate folders

If during download it fails, file gets corrupted sadly it doesn't verify that so delete files inside weights and restart

How to Install on RunPod, Massed Compute, Kaggle

Follow the Massed_Compute_Instructions_READ.txt and Runpod_Instructions_READ.txt

For Kaggle follow the notebook written steps

An example video of how to use my RunPod, Massed Compute scripts and Kaggle notebook can be seen

https://youtu.be/wG7oPp01COg


Put together a small repo showing how to go from making your own fine-tuning dataset w/ services like Groq & Together to publishing that model on ollama.

In my case I fine-tuned SmolLM-360M to be a better assistant for my Pi-Card (previous post) project.

Check it out!
https://github.com/nkasmanoff/ft-flow


Small-Text: Active Learning for Text Classification in Python (2107.10314)

🔔 Release: small-text v1.4.1

The new release contains some smaller bugfixes. Check it out!

Github: https://github.com/webis-de/small-text
Paper: https://huggingface.co/papers/2107.10314


🚀  We’re excited to launch Ghost 8B Beta (1608), a top-performing language model with unmatched multilingual support and cost efficiency.

Key Highlights:
- Superior Performance: Outperforms Llama 3.1 8B Instruct, GPT-3.5 Turbo, Claude 3 Opus, GPT-4, and more in winrate scores.
- Expanded Language Support: Now supports 16 languages, including English, Vietnamese, Spanish, Chinese, and more.
- Enhanced Capabilities: Improved math, reasoning, and instruction-following for better task handling.

With two context options (8k and 128k), Ghost 8B Beta is perfect for complex, multilingual applications, balancing power and cost-effectiveness.

🔗 Learn More: https://ghost-x.org/docs/models/ghost-8b-beta
https://huggingface.co/collections/ghost-x/ghost-8b-beta-668ead6179f93be717db4542

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Wrote a blog post with some ideas about prompt engineering

https://huggingface.co/blog/KnutJaegersberg/first-principles-prompt-engineering

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I'm excited to share a really cool milestone in my AI/LLM journey.

Brief backstory: Before diving into AI, I spent over a decade working in ecological fields such as the conservation corps, biodynamic farming, and natural habitat restoration. This background instilled in me a deep concern about the environmental impact of scaling AI without sustainable practices.

Driven by this concern, I've spent months planning and experimenting to make my AI work more eco-friendly. I'm thrilled to announce that I've successfully transitioned my entire operation to run on 100% sustainable solar power!

My current setup includes multiple linked Mac Pro tower desktops and custom code built from open-source libraries. While it's a bit experimental, this configuration is working great for my needs. All my LLM research, development, and client services now run exclusively on solar energy.

I'm curious if anyone else here has experimented with renewable energy for their LLM work?

For those interested in more details, I've written a brief blog post about this journey here https://medium.com/@betalabsllm/powering-the-future-be-ta-labs-revolutionary-100-solar-powered-ai-operation-444433e61d43

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How good are you at spotting AI-generated images?

Find out by playing Fake Insects 🐞 a Game where you need to identify which insects are fake (AI generated). Good luck & share your best score in the comments!

https://huggingface.co/spaces/victor/fake-insects

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So turns out I've been spreading a bit of misinformation when it comes to imatrix in llama.cpp

It starts true; imatrix runs the model against a corpus of text and tracks the activation of weights to determine which are most important

However what the quantization then does with that information is where I was wrong.

I think I made the accidental connection between imatrix and exllamav2's measuring, where ExLlamaV2 decides how many bits to assign to which weight depending on the goal BPW

Instead, what llama.cpp with imatrix does is it attempts to select a scale for a quantization block that most accurately returns the important weights to their original values, ie minimizing the dequantization error based on the importance of activations

The mildly surprising part is that it actually just does a relatively brute force search, it picks a bunch of scales and tries each and sees which one results in the minimum error for weights deemed important in the group

But yeah, turns out, the quantization scheme is always the same, it's just that the scaling has a bit more logic to it when you use imatrix

Huge shoutout to @compilade for helping me wrap my head around it - feel free to add/correct as well if I've messed something up

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I know a secret about knowledge graphs that the world doesn't! There are severe mathematical limitations to geometric fractals. It is classed as an 'unsolvable problem' in the mathematics world. There are currently ~1,000 mathematicians or so in the world that give this problem serious thought. You literally cannot solve it with geometric fractals. This is why I invented P-FAF, it uses calculus based fractals instead. I literally invented the math to make it work. I solved an 'unsolvable equation' to make the math work. You ONLY make the math work the way I did it in the end. I have never released the licensing commercially. Good luck!


Supercool Weekend Read🤖
Nvidia researchers achieved SOTA LLM compression metrics using pruning and knowledge distillation techniques.

Details on Techniques (Simplified):
They started off with a large pre-trained language model (15B params), then:

1. Estimated the importance of different parts of the model (neurons, attention heads, layers) using activation-based metrics on a small calibration dataset.

2. Pruned (remove) less important parts of the model to reduce its size.

3. Retrained the pruned model using knowledge distillation, where the original large model acts as a teacher for the smaller pruned model.

4. Used a lightweight neural architecture search to find the best configuration for the pruned model.

5. Repeated this process iteratively to create even smaller models.

Cool, giving it a try this weekend 😎
Code: https://github.com/NVlabs/Minitron
Paper: https://arxiv.org/abs/2407.14679
Demo: https://huggingface.co/spaces/nvidia/minitron


Added FLUX.1 pro/dev/schnell and AuraFlow v0.2 to  https://huggingface.co/spaces/fal/imgsys !!! Go play with it and get us some votez


NEW math-instruct model + dataset!

https://huggingface.co/ValiantLabs/Llama3.1-8B-Cobalt is our new math-instruct model.
Trained using a synthetic math-instruct dataset generated with Llama 3.1 405b. Find the dataset here: https://huggingface.co/datasets/sequelbox/Polytope

More to come soon :)


Introducing HelpingAI2-9B, an emotionally intelligent LLM.
Model Link : https://huggingface.co/OEvortex/HelpingAI2-9B
Demo Link: https://huggingface.co/spaces/Abhaykoul/HelpingAI2

This model is part of the innovative HelpingAI series and it stands out for its ability to engage users with emotional understanding.

Key Features:
-----------------

*   It gets 95.89 score on EQ Bench greather than all top notch LLMs, reflecting advanced emotional recognition.
*   It gives responses in empathetic and supportive manner.

Must try our demo: https://huggingface.co/spaces/Abhaykoul/HelpingAI2


Announcing another BIG data drop! This time it's ~275M images from Flickr https://huggingface.co/datasets/bigdata-pw/Flickr

Data acquisition for this project is still in progress, get ready for an update soon:tm:

In case you missed them; other BIG data drops include Diffusion1B https://huggingface.co/datasets/bigdata-pw/Diffusion1B - ~1.23B images and generation parameters from a variety of diffusion models and if you fancy practicing diffusion model training check out Dataception https://huggingface.co/datasets/bigdata-pw/Dataception - a dataset of over 5000 datasets in WebDataset format!

Requests are always welcome so reach out if there's a dataset you'd like to see!


✨ Feeling thankful...

🇮🇳 15th August, 2024; on India's 78th Independence Day

🎉 Crossed 100 followers on Hugging Face

🏆 Got LinkedIn Top Voice

🤖 AI has never been more exciting and I am here for it

👀 @clem Can I be a Hugging Face fellow now?


Improved ControlNet!
Now supports dynamic resolution for perfect landscape and portrait outputs. Generate stunning images without distortion—optimized for any aspect ratio!
...
https://huggingface.co/spaces/DamarJati/FLUX.1-DEV-Canny


As some of you know, I try to convert models to either fp32 or bf16 depending on theirs size before doing imatrix and quantization

Today I decided to see if that matters, and the results have me.. for lack of a better word, perplexed

My setup:

Mistral Nemo Instruct 2407
- convert to FP32, calculate imatrix, quantize to Q8_0 and Q4_K_M
- convert to FP16, calculate imatrix, quantize to Q8_0 and Q4_K_M

I calculated the kld base from the FP32 model:
`./llama-perplexity -m /models/Mistral-Nemo-Instruct-2407-f32.gguf -f /training_data/wikitext-2-raw/wiki.test.raw --kl-divergence-base /training_data/mistral-nemo-f32.kld -ngl 35 -fa -sm row`

then calculated the divergence itself for each like so:
`./llama-perplexity -m /models/Mistral-Nemo-Instruct-2407-Q8_0.gguf -f /training_data/wikitext-2-raw/wiki.test.raw --kl-divergence-base /training_data/mistral-nemo-f32.kld --kl-divergence -ngl 50 -fa -sm row`

Q4_K_M from fp16 and fp32 were similar, trading blows across statistics, odd since i expected fp32 to be strictly better but it's not

Q8_0 is where things get weird. Despite each file being slightly different size, and the sha256sum of course being different, they each get *completely identical* scores, down to 6 decimal places of precision on the statistics.

How is this possible? Is there something I don't understand about llama.cpp that makes it always convert to fp16 before it does quantization? Am I wasting time using FP32/BF16??

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https://huggingface.co/fal/AuraFlow-v0.3 is now here with support for different aspect resolutions (w/h up to 1536px!) and much nicer aesthetics! Make sure to install the latest diffusers to get support for it.

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𝗭𝗲𝗿𝗼-𝗺𝗮𝘁𝗵 𝗶𝗻𝘁𝗿𝗼 𝘁𝗼 𝗔𝗜 𝗵𝗶𝘀𝘁𝗼𝗿𝘆: 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝟭𝟵𝟱𝟬𝘀 𝘁𝗼 𝘁𝗼𝗱𝗮𝘆'𝘀 𝗟𝗟𝗠𝘀 📖

I wanted to structure my thinking about LLMs by going through their history since the 50s. This history is captivating, with the opposition between Connexionists (Rosenblatt, LeCun) and Symbolists, the first victories of "deep" neural networks, the revolution of Attention...

So I might have gone a bit too far! 😅

📝 I've made a long post summarizing the main stages of building LLMs: neural networks, optimization, backpropagation, attention layers...

✅ And I've made sure to keep it 100% horrible-latex-math-free: the technical stuff is conveyed in graphs only, so it should be accessible to really anyone, even your grandfather (I'm sending it to mine right now).

Read it here in english 👉 https://aymeric-roucher.github.io/brief-history-of-ai/
Pour le post en français 👉 https://aymeric-roucher.github.io/breve-histoire-de-l-ia/


Some personal and professional news ✨

I'm writing a book on ML metrics.

Together with Wojtek Kuberski, we’re creating the missing piece of every ML university program and online course: a book solely dedicated to Machine Learning metrics!

The book will cover the following types of metrics:
• Regression
• Classification
• Clustering
• Ranking
• Vision
• Text
• GenAI
• Bias and Fairness

👉 check out the book: https://www.nannyml.com/metrics


Everchanging Quest is out !

It is an LLM controlled Rogue-Like in which the LLM gets a markdown representation of the map, and should generate a JSON with the objective to fulfill on the map as well as the necessary objects and their placements.

Come test it on the space :
https://huggingface.co/spaces/Jofthomas/Everchanging-Quest

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ehm, so today i've finished my new project

https://huggingface.co/spaces/Blane187/animalese-py

or you can make your voice to animalese with it:
https://huggingface.co/spaces/Blane187/animalese_RVC

i'm just bored, so i make the project, lol


📣 Introducing Dataset Viber: your chill repo for data collection, annotation and vibe checks! 🎉

I've cooked up Dataset Viber, a set of cool tools designed to make data preparation for AI models easier, more approachable and enjoyable for standalone AI engineers and enthusiasts.

🔧 What Dataset Viber offers:
- CollectorInterface: Lazily collect model interaction data without human annotation
- AnnotatorInterface: Annotate your data with models in the loop
- BulkInterface: Explore data distribution and annotate in bulk
- Embedder: Efficiently embed data with ONNX-optimized speeds

🎯 Key features:
- Supports various tasks for text, chat, and image modalities
- Runs in .ipynb notebooks
- Logs data to local CSV or directly to Hugging Face Hub
- Easy to install via pip: `pip install dataset-viber`

It's not designed for team collaboration or production use, but rather as a fun and efficient toolkit for individual projects.

Want to give it a try? Check out the repository link https://github.com/davidberenstein1957/dataset-viber/.

I'm excited to hear your feedback and learn how you vibe with your data. Feel free to open an issue or reach out if you have any questions or suggestions!

Some shoutouts:
- Gradio for the amazing backbone
- Daniel van Strien for some initial presentations I did on vibe checks
- Emily Omier for the workshop on structuring GitHub repo READMEs
- Hamel Husain for keeping mentioning that people should look at their data.
- Philipp Schmid for his code for ONNX feature-extractors
- Ben Burtenshaw for the first PR

Best-codes maywell osanseviero OrigamiDream


Here is a hackable and minimal implementation showing how to perform distributed text-to-image generation with Diffusers and Accelerate.

Full snippet is here: https://gist.github.com/sayakpaul/cfaebd221820d7b43fae638b4dfa01ba

With @JW17


hey everyone, if you have any experience with fine tuning ai models please feel free to reach out to me :)
We're looking for devs to fine tune an AI model for a snapchat chatting AI project. Open to either part time or full time work.

Email: admin@socialrisestudio.org
Telegram: Kirann46


Seeing and Understanding: Bridging Vision with Chemical Knowledge Via
  ChemVLM (2408.07246)

🚀 Introducing ChemVLM, the first open-source multimodal large language model dedicated to chemistry!
🌟Comparable performances with commercial models or specific OCR model but with dialogue capabilities!
✨2B/26B Models Here! https://huggingface.co/AI4Chem/ChemVLM-26B
https://huggingface.co/papers/2408.07246

mmhamdy Riswan-Nopiyar sequelbox Ramikan-BR Bruhn


📸Photo LoRA Drop📸

I've been working on this one for a few days, but really I've had this dataset for a few years! I collected a bunch of open access photos online back in late 2022, but I was never happy enough with how they played with the base model!

I am so thrilled that they look so nice with Flux!

This for me is a version one of this model - I still see room for improvement and possibly expansion of it's 40 image dataset. For those who are curious:

40 Image
3200 Steps
Dim 32
3e-4

Enjoy! Create! Big thank you to Glif for sponsoring the model creation! :D

https://huggingface.co/alvdansen/flux_film_foto


AuraSR Giga Upscaler V1 by SECourses - Upscales to 4x

AuraSR is a 600M parameter upsampler model derived from the GigaGAN paper. It works super fast and uses a very limited VRAM below 5 GB. It is deterministic upscaler. It works perfect in some images but fails in some images so it is worth to give it a shot.

GitHub official repo : https://github.com/fal-ai/aura-sr

I have developed 1-click installers and a batch upscaler App.

You can download installers and advanced batch App from below link:
https://www.patreon.com/posts/110060645

Check the screenshots and examples below

Windows Requirements

Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git

If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial

https://youtu.be/-NjNy7afOQ0

How to Install and Use on Windows

Extract the attached GigaGAN_Upscaler_v1.zip into a folder like c:/giga_upscale

Then double click and install with Windows_Install.bat file

It will generate an isolated virtual environment venv folder and install requirements

Then double click and start the Gradio App with Windows_Start_App.bat file

When first time running it will download models into your Hugging Face cache folder

Hugging Face cache folder setup explained below

https://www.patreon.com/posts/108419878

All upscaled images will be saved into outputs folder automatically with same name and plus numbering if necessary

You can also batch upscale a folder

How to Install and Use on Cloud

Follow Massed Compute and RunPod instructions

Usage is same as on Windows

For Kaggle start a Kaggle notebook, import our Kaggle notebook and follow the instructions

App Screenshots and Examples below

MonsterMMORPG davisbro johko Riswan-Nopiyar ajibawa-2023


Introducing Fineweb-Edu-Fortified: An enhanced Fineweb-Edu dataset. 📚

This dataset is tailored for NLP tasks and helps streamline model training by offering a more refined, unique dataset. Perfect for startups and researchers looking for high-quality educational content to train, evaluate, or fine-tune AI models. The dataset is based on the Fineweb-Edu subset of the large Fineweb dataset and includes:

- Exact-match deduplication across all crawls
- Embeddings for each row using the TaylorAI/bge-micro model
- Count column indicating duplication frequency
- Includes data from 95 Common Crawl crawls (2013-2024)
- Rows have been reduced from 1.279B to 0.324B after deduplication
- It is comprised of ~375B tokens (down from 1,320B in Fineweb-Edu)

Access the entire Fineweb-Edu-Fortified dataset on Hugging Face → https://huggingface.co/datasets/airtrain-ai/fineweb-edu-fortified

Try a semantic search demo via this Hugging Face Space → https://huggingface.co/spaces/airtrain-ai/fineweb-edu-fortified-search-demo

Many thanks to the amazing @josh-sematic for his work on this project, the Fineweb/Fineweb-Edu team at Hugging Face for producing the original datasets and for their support during our work on Fineweb-Edu-Fortified, and also thanks to @underspirit for pointing out the reduction in dataset size that could be achieved via deduplication. 🤗

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Let’s see JEPA in action🤖
Simplified image-based implementation training on a CPU with live preview support - very satisfying to watch:)

I-JEPA is the image-based version of JEPA (Joint-Embedding Predictive Architecture - an alternative to autoregressive LLM architectures ) pioneered by professor Yann Lecun.

At a higher level, I-JEPA predicts image segment representations (Target) based on representations of other segments within the same image (Context). It consists of three key components: a context encoder, target encoder and a predictor.

Code: https://github.com/Jaykef/ai-algorithms/blob/main/mnist_ijepa.ipynb

emirhanbilgic jakemannix Riswan-Nopiyar osanseviero ibrahim313


If you are interested in deep reinforcement learning, find my recent survey below:

A Survey Analyzing Generalization in Deep Reinforcement Learning
Paper: https://arxiv.org/pdf/2401.02349
GitHub: https://github.com/EzgiKorkmaz/generalization-reinforcement-learning