sosoai (Sunyoung Hwang)

reacted to fdaudens's post with 👍 8 days ago

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Want to learn to build an AI Agent? I put together a cookbook for creating your own news research agent with OpenAI GPT-OSS:

- Searches headlines & specific sites
- Pulls full articles when you need depth
- Summarizes with clickable sources
- Runs in a simple Gradio chat UI
- No GPU, no local setup — just open-weight GPT-OSS models via Hugging Face

If you’ve been wanting to try agents but weren’t sure where to start, this is an end-to-end example you can fork, run, and adapt.

Full guide + code https://huggingface.co/blog/fdaudens/openai-gpt-oss-agent-inference-providers

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reacted to aiqtech's post with ❤️ 5 months ago

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✨ High-Resolution Ghibli Style Image Generator ✨
🌟 Introducing FLUX Ghibli LoRA
Hello everyone! Today I'm excited to present a special LoRA model for FLUX Dev.1. This model leverages a LoRA trained on high-resolution Ghibli images for FLUX Dev.1 to easily create beautiful Ghibli-style images with stunning detail! 🎨

space: aiqtech/FLUX-Ghibli-Studio-LoRA
model: openfree/flux-chatgpt-ghibli-lora

🔮 Key Features

Trained on High-Resolution Ghibli Images - Unlike other LoRAs, this one is trained on high-resolution images, delivering sharper and more beautiful results
Powered by FLUX Dev.1 - Utilizing the latest FLUX model for faster generation and superior quality
User-Friendly Interface - An intuitive UI that allows anyone to create Ghibli-style images with ease
Diverse Creative Possibilities - Express various themes in Ghibli style, from futuristic worlds to fantasy elements

🖼️ Sample Images

Include "Ghibli style" in your prompts
Try combining nature, fantasy elements, futuristic elements, and warm emotions
Add "[trigger]" tag at the end for better results

🚀 Getting Started

Enter your prompt (e.g., "Ghibli style sky whale transport ship...")
Adjust image size and generation settings
Click the "Generate" button
In just seconds, your beautiful Ghibli-style image will be created!

🤝 Community
Want more information and tips? Join our community!
Discord: https://discord.gg/openfreeai

Create your own magical world with the LoRA trained on high-resolution Ghibli images for FLUX Dev.1! 🌈✨

reacted to mlabonne's post with 👍 about 1 year ago

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Large models are surprisingly bad storytellers.

I asked 8 LLMs to "Tell me a bedtime story about bears and waffles."

Claude 3.5 Sonnet and GPT-4o gave me the worst stories: no conflict, no moral, zero creativity.

In contrast, smaller models were quite creative and wrote stories involving talking waffle trees and bears ostracized for their love of waffles.

Here you can see a comparison between Claude 3.5 Sonnet and NeuralDaredevil-8B-abliterated. They both start with a family of bears but quickly diverge in terms of personality, conflict, etc.

I mapped it to the hero's journey to have some kind of framework. Prompt engineering can definitely help here, but it's still disappointing that the larger models don't create better stories right off the bat.

Do you know why smaller models outperform the frontier models here?

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reacted to ucsahin's post with 🚀 about 1 year ago

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Florence-2 has a great capability of detecting various objects in a zero-shot setting with the task prompt "<OD>". However, if you want to detect specific objects that the base model is not able to in its current form, you can easily finetune it for this particular task. Below I show how to finetune the model to detect tables in a given image, but a similar process can be applied to detect any objects. Thanks to @andito , @merve , and @SkalskiP for sharing the fix for finetuning the Florence-2 model. Please also check their great blog post at https://huggingface.co/blog/finetune-florence2.

Colab notebook: https://colab.research.google.com/drive/1Y8GVjwzBIgfmfD3ZypDX5H1JA_VG0YDL?usp=sharing
Finetuned model: ucsahin/Florence-2-large-TableDetection

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reacted to m-ric's post with 👍 about 1 year ago

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💰 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗽𝗿𝗶𝗰𝗲 𝗼𝗳 𝗮𝗻𝘆 𝗟𝗟𝗠 𝗔𝗣𝗜 𝗿𝗲𝗾𝘂𝗲𝘀𝘁 ⇒ 𝘁𝗼𝗸𝗲𝗻𝗰𝗼𝘀𝘁

I've just found out about 𝙰𝚐𝚎𝚗𝚝𝙾𝚙𝚜-𝙰𝙸/𝚝𝚘𝚔𝚎𝚗𝚌𝚘𝚜𝚝 (https://github.com/AgentOps-AI/tokencost).
𝗧𝗵𝗶𝘀 𝗹𝗶𝗯𝗿𝗮𝗿𝘆 𝗴𝗶𝘃𝗲𝘀 𝘆𝗼𝘂 𝘁𝗵𝗲 𝗽𝗿𝗶𝗰𝗲 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗰𝗮𝗹𝗹𝘀 𝘁𝗼 𝗮𝗻𝘆 𝗟𝗟𝗠 𝗔𝗣𝗜: OpenAI, Anthropic, Mistral, AWS or Databricks...

For any model, you can use as input either string prompts or messages, and get as outputs either the price or token count.

Congrats to the AgentOps-AI team: this will be very useful when trying to get a ballpark estimate of a project's price, to compare APIs, or for precise monitoring of usage!

✨ Daily reminder: 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗮𝗻 𝗔𝟭𝟬𝟬 𝗰𝗼𝘀𝘁𝘀 𝘆𝗼𝘂 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 $𝟬.𝟬𝟬/𝗵𝗼𝘂𝗿 (or 0.00€ in current exchange rates) on a HF space with ZeroGPU!
Learn more on ZeroGPU 👉 https://www.datacenterdynamics.com/en/news/hugging-face-launches-zerogpu-project-to-democratize-ai-gives-away-10-million-worth-of-compute/

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reacted to beomi's post with 🔥 over 1 year ago

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🚀 **InfiniTransformer, Gemma/Llama3 based Implementation!** 🌌

> Update @ 2024.04.19: It now supports Llama-3!

> Note: this implementation is unofficial

This implementation is designed to handle virtually infinite context lengths.

Here's the github repo: https://github.com/Beomi/InfiniTransformer

📄 **Read the original Paper:** https://arxiv.org/abs/2404.07143

## **Focus on Infini-Attention**

- **2 Types of Implementation available:** Attention-layer only implementation / Model & Train-wise implementation
- **Fixed(segment dependent) Memory Usage:** Enables training on larger models and longer sequences without the memory overhead typical of standard Transformer implementations.
- **Infinite Context Capability:** Train with unprecedented sequence lengths—imagine handling up to 1 million sequence lengths on standard hardware!
- You could train Gemma-2B with 1M sequence length with 2K segmentation size with single H100 GPU.

## **Try InfiniTransformer**

1. **Clone the repository:**

bash
   git clone https://github.com/Beomi/InfiniTransformer

2. **Install necessary tools:**

bash
   pip install -r requirements.txt
   pip install -e git+https://github.com/huggingface/transformers.git@b109257f4f#egg=transformers

3. **Dive Deep into Custom Training:**
- Train with extensive sequence lengths using scripts such as ./train.gemma.infini.noclm.1Mseq.sh.

for more detailed info, please visit Repo: https://github.com/Beomi/InfiniTransformer

Look forward to see your feedbacks! 😊

ps. Training loss plot is here 😉

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reacted to jeffboudier's post with 🚀 over 1 year ago

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TGI v2.0.2 is out!
- New models (idefics2, phi3)
- Cleaner VLM support in the openai layer
- Upgraded to pytorch 2.3.0

https://github.com/huggingface/text-generation-inference/releases/tag/v2.0.2

Kudos @Narsil @olivierdehaene @drbh and so many contributors!

posted an update over 1 year ago

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Wow i can post on HF now!
Love HF so much 🤗❤️

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reacted to codelion's post with 🔥 over 1 year ago

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We just released a new MoE model (meraGPT/mera-mix-4x7B) that is half as large as Mixtral-8x7B while still been competitive with it across different benchmarks. mera-mix-4x7B achieves 76.37 on the open LLM eval.

You can check mera-mix-4x7B out on HF here - meraGPT/mera-mix-4x7B

reacted to akhaliq's post with ❤️ over 1 year ago

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Chain-of-Thought Reasoning Without Prompting

paper page: Chain-of-Thought Reasoning Without Prompting (2402.10200)

In enhancing the reasoning capabilities of large language models (LLMs), prior research primarily focuses on specific prompting techniques such as few-shot or zero-shot chain-of-thought (CoT) prompting. These methods, while effective, often involve manually intensive prompt engineering. Our study takes a novel approach by asking: Can LLMs reason effectively without prompting? Our findings reveal that, intriguingly, CoT reasoning paths can be elicited from pre-trained LLMs by simply altering the decoding process. Rather than conventional greedy decoding, we investigate the top-k alternative tokens, uncovering that CoT paths are frequently inherent in these sequences. This approach not only bypasses the confounders of prompting but also allows us to assess the LLMs' intrinsic reasoning abilities. Moreover, we observe that the presence of a CoT in the decoding path correlates with a higher confidence in the model's decoded answer. This confidence metric effectively differentiates between CoT and non-CoT paths. Extensive empirical studies on various reasoning benchmarks show that the proposed CoT-decoding substantially outperforms the standard greedy decoding.

reacted to dhuynh95's post with ❤️ over 1 year ago

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🪟32k-context BERT for embedding and RAG on long corpus

Monarch Mixer is a new architecture to enable long context BERT for large corpus and can be fine-tuned for large context retrieval.

Quite interesting and important as BERT is still the most used LLM in production for "old school" tasks like classification, NER, embeddings, but is also a key component for RAG.

Paper: https://arxiv.org/abs/2310.12109
Blog: https://hazyresearch.stanford.edu/blog/2024-01-11-m2-bert-retrieval
GitHub: https://github.com/HazyResearch/m2

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reacted to victor's post with ❤️ over 1 year ago

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Let's play a little game, how would you build the Rabbit R1 with open source tech? Here is my stack:

- openai/whisper-small for awesome Speech-to-Text with low latency
- mistralai/Mixtral-8x7B-Instruct-v0.1 for an awesome super powerful LLM Brain
- coqui/XTTS-v2 for a nice and clean voice

Which stack will you personally choose?

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reacted to Tonic's post with ❤️ over 1 year ago

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🙋🏻‍♂️hey there folks,

Everyone's🗣️talking about microsoft's new e5mistral embeddings model

🤔🤔 but did you actually try it yet ?

Well , now you can, just check it out. it's a new way to serve and create embeddings.

try it hosted on GPUZero : Tonic/e5
or served on an A10G : Tonic/e5

you get best results actually building with it though, so use it in your app !

Our demo is coming soon too so let's work together if you want :-)

1 reply

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Sunyoung Hwang PRO

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Sunyoung Hwang PRO

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