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"value": "For example a genuine restaurant review is: \"Food doesnโt get better than this. I was sad when I finished, actually sad. To die for.\" - ",
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] | Hey, we've been researching how sentiment analysis models work on real world data and would like to share a comparison tool and leaderboard that we've built: https://addmaple.com/sentiment
Compare 12 models - top RoBERTa models, plus Google/AWS commercial offerings and GPT4o.
Rather than just a score, you can explore results by those with the highest disagreement between models, which gives a nice intuition for the strengths and weaknesses of each model.
For example a genuine restaurant review is: "Food doesnโt get better than this. I was sad when I finished, actually sad. To die for." - https://addmaple.com/sentiment/public-reviews/manteca/C9bvMuyAeF1g
SiEBERT gets it right, as do Google and OpenAI but all other models fail.
We'd love your feedback
Hitra
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] | Today lets discuss about 32-bit (FP32) and 16-bit (FP16) floating-point!
Floating-point numbers are used to represent real numbers (like decimals) and they consist of three parts:
```
Sign bit:
Indicates whether the number is positive (0) or negative (1).
Exponent:
Determines the scale of the number (i.e., how large or small it is by shifting the decimal point).
Mantissa (or fraction):
Represents the actual digits of the number.
```
32-bit Floating Point (FP32)
Total bits: 32 bits
Sign bit: 1 bit
Exponent: 8 bits
Mantissa: 23 bits
For example:
A number like -15.375 would be represented as:
Sign bit: 1 (negative number)
Exponent: Stored after being adjusted by a bias (127 in FP32).
Mantissa: The significant digits after converting the number to binary.
16-bit Floating Point (FP16)
Total bits: 16 bits
Sign bit: 1 bit
Exponent: 5 bits
Mantissa: 10 bits
Example:
A number like -15.375 would be stored similarly:
Sign bit: 1 (negative number)
Exponent: Uses 5 bits, limiting the range compared to FP32.
Mantissa: Only 10 bits for precision.
Precision and Range
FP32: Higher precision and larger range, with about 7 decimal places of accuracy.
FP16: Less precision (around 3-4 decimal places), smaller range but faster computations and less memory use. | {
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if you don't want to wait for the next transformers release install transformers from my PR https://github.com/huggingface/transformers/pull/32938 and initialize SigLIP from there | {
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@clefourrier and generally the HuggingFace evaluation team put together a fantastic guidebook covering a lot about ๐๐ฉ๐๐๐จ๐๐ง๐๐ข๐ก from basics to advanced tips.
link : https://github.com/huggingface/evaluation-guidebook
I havenโt finished it yet, but i'am enjoying every piece of it so far. Huge thanks @clefourrier and the team for this invaluable resource ! | {
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"value": "๐ฆโโฌ ๐๐ก๐๐ญ ๐ข๐ฌ ๐๐๐ ๐ฉ๐ข๐?",
"raw": "๐ฆโโฌ ๐๐ก๐๐ญ ๐ข๐ฌ ๐๐๐ ๐ฉ๐ข๐?",
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] | Ok, you're finally convinced that synthetic data works... โ๏ธ
๐๐จ๐ฐ ๐ฒ๐จ๐ฎ ๐ฐ๐๐ง๐ญ ๐ญ๐จ ๐ ๐๐ง๐๐ซ๐๐ญ๐ ๐๐ง ๐ข๐ง๐ฌ๐ญ๐ซ๐ฎ๐๐ญ๐ข๐จ๐ง ๐๐๐ญ๐๐ฌ๐๐ญ ๐๐จ๐ซ ๐๐ข๐ง๐-๐ญ๐ฎ๐ง๐ข๐ง๐ ๐ข๐ง ๐ ๐ฅ๐๐ง๐ ๐ฎ๐๐ ๐ ๐จ๐ญ๐ก๐๐ซ ๐ญ๐ก๐๐ง ๐๐ง๐ ๐ฅ๐ข๐ฌ๐ก.
But how do you get started?
I explore how to do this with Magpie in my new article
https://huggingface.co/blog/anakin87/multilingual-magpie
---
๐ฆโโฌ ๐๐ก๐๐ญ ๐ข๐ฌ ๐๐๐ ๐ฉ๐ข๐?
It's a recent technique for creating synthetic instruction datasets.
Magpie is based on a simple but ingenious idea ๐
if you prompt an instruction-tuned model with a pre-query template, you can make it generate a plausible user query/instruction
Here's an example:
model: Llama-3-8B-Instruct
pre-query template: "<|begin_of_text|><|start_header_id|>user<|end_header_id|>"
generated user instruction: "What are some of the responsibilities of a commercial pilot?"
You can then feed this instruction back into the same model to get the assistant response.
By repeating this process, it's possible to generate large synthetic datasets with relatively little effort.
๐ช The authors demonstrate that using these datasets for Supervised Fine Tuning (SFT) can yield strong performance, even competitive with the original instruct model.
๐ง๐๐๐ง๐๐ซ๐๐ญ๐ข๐ง๐ ๐ง๐จ๐ง-๐๐ง๐ ๐ฅ๐ข๐ฌ๐ก ๐๐๐ญ๐
Most Language Models are primarily trained on English texts, so they tend to produce data in English.
How can we overcome this?
Earlier approaches were complex or costly.
Then @mrm8488 found a simple solution: add the target language to the pre-query template.
For Spanish, the template becomes "<|begin_of_text|><|start_header_id|>user<|end_header_id|>spanish:".
This method works for Spanish and German!
โ Unfortunately, it does not work well for other languages (๐ฎ๐น, ๐ณ๐ฑ, ...)
๐ | {
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] | Bellman, the Swedish finetune, has once again returned in his biggest incarnation yet, at 12b. Based on Mistral-Nemo-Instruct: https://huggingface.co/neph1/Mistral-Nemo-Instruct-bellman-12b | {
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] | Iโm recently experimenting with the Flux-Ultra Realism and Real Anime LoRA models, using the Flux.1-dev model as the base. The model and its demo example are provided in the Flux LoRA DLC collections.๐
๐ฅณDemo : ๐ https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC
๐ฅณModel:
- https://huggingface.co/prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0
- https://huggingface.co/prithivMLmods/Flux-Dev-Real-Anime-LoRA
๐ฅณFor more details, please visit the README.md of the Flux LoRA DLC Space & https://huggingface.co/collections/prithivMLmods/lora-space-collections-6714b72e0d49e1c97fbd6a32 | {
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] | ๐ Introducing Websim.ai User Projects Dataset - https://huggingface.co/datasets/nyuuzyou/websim
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- Primarily in English, with potential for multilingual content in generated websites
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The dataset can be used for analyzing AI-assisted web development trends, studying user behavior in LLM-powered creative tools, and exploring the capabilities of language models in web design. | {
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With the incredibly accurate LoRAs we see emerge for high quality models like FLUX from services like fal.ai that offer training within single digit minutes, e.g. 2 min per 1000 iterations.
Why the hell are people publishing private LoRAs as public models?!
Take a look at this listing: https://huggingface.co/models?other=base_model:adapter:black-forest-labs%2FFLUX.1-dev&sort=created
I would expect that people that hold a HF account have some kind of forward thinking. Heck, do you really want to give anyone the power to create ultra realistic images of yourself?!
Didn't we learn anything from social media?
I am puzzled.. | {
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(October 12 - October 19, 2024)",
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(October 12 - October 19, 2024)",
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] | Last Week in Medical AI: Top LLM Research Papers/Models ๐ฅ
๐
(October 12 - October 19, 2024)
Medical LLM & Other Models:
- OLAPH: Factual Biomedical LLM QA
- LLMD: Interpreting Longitudinal Medical Records
- LifeGPT: Generative Transformer for Cells
- MedCare: Decoupled Clinical LLM Alignment
- Y-Mol: Biomedical LLM for Drug Development
Frameworks and Methodologies:
- MedINST: Biomedical Instructions Meta Dataset
- Democratizing Medical LLMs via Language Experts
- MCQG-SRefine: Iterative Question Generation
- Adaptive Medical Language Agents
- MeNTi: Medical LLM with Nested Tools
Medical LLM Applications:
- AGENTiGraph: LLM Chatbots with Private Data
- MMed-RAG: Multimodal Medical RAG System
- Medical Graph RAG: Safe LLM via Retrieval
- MedAide: Multi-Agent Medical LLM Collaboration
- Synthetic Clinical Trial Generation
Medical LLMs & Benchmarks:
- WorldMedQA-V: Multimodal Medical LLM Dataset
- HEALTH-PARIKSHA: RAG Models Evaluation
- Synthetic Data for Medical Vision-Language
Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels as well!
- Youtube: https://youtu.be/LROOjWXUgvg?si=s-nNDOSD3BrsHYjQ
- Spotify : https://open.spotify.com/episode/12xeN2vnOTRdDrHbWqhV6I?si=bd7c8d9fee8049fd
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] | ๐ Announcement for the Lovely community! ๐
Just launched the https://huggingface.co/spaces/zamal/DeepSeek-VL-1.3B-Chat on Hugging Face, and it's ready for YOU to explore! ๐ฌ๐ผ๏ธ
This full-fledged model is perfect for advanced image and text interactions, with zero GPU required. The Deepseek VL-1.3B Chat typically needs around 8 GB of VRAM and storage of almost 4 GB, but now you can experience it hassle-free right on our space!
Want something lighter? Weโve also uploaded a 4 bit quantized version (just around 1GB!), available on my profile. Perfect for those with limited hardware. ๐๐
Come try it now and see what this model can do! ๐โจ
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- Checkpointing: Uses the new PyTorch distributed saving method (.distcp format) for flexible model reloading across different GPU configurations.
- Configuration: Utilizes data classes and YAML files for intuitive setup and modification.
- Profiling: Integrates with xFormers' profiler for automatic MFU and HFU calculation, plus memory profiling.
- Slurm Integration: Includes `stool.py` for seamless job launching on Slurm clusters.
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- 7B parameter Mamba model (trained on 200B tokens) shows competitive results with Llama 7B on benchmarks like ARC, MMLU, and BBH.
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"value": "The Optimum docs about model inference with ONNX Runtime is now much clearer and simpler!",
"raw": "The Optimum docs about model inference with ONNX Runtime is now much clearer and simpler!",
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"value": "You want to deploy your favorite model on the hub but you don't know how to export it to the ONNX format? You can do it in one line of code as follows:",
"raw": "You want to deploy your favorite model on the hub but you don't know how to export it to the ONNX format? You can do it in one line of code as follows:",
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"raw": "```py\nfrom optimum.onnxruntime import ORTModelForSequenceClassification\n\n# Load the model from the hub and export it to the ONNX format\nmodel_id = \"distilbert-base-uncased-finetuned-sst-2-english\"\nmodel = ORTModelForSequenceClassification.from_pretrained(model_id, export=True)\n```",
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"code": "from optimum.onnxruntime import ORTModelForSequenceClassification\n\n# Load the model from the hub and export it to the ONNX format\nmodel_id = \"distilbert-base-uncased-finetuned-sst-2-english\"\nmodel = ORTModelForSequenceClassification.from_pretrained(model_id, export=True)",
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"value": "Check out the whole guide ๐ ",
"raw": "Check out the whole guide ๐ ",
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] | Interested in performing inference with an ONNX model?โก๏ธ
The Optimum docs about model inference with ONNX Runtime is now much clearer and simpler!
You want to deploy your favorite model on the hub but you don't know how to export it to the ONNX format? You can do it in one line of code as follows:
```py
from optimum.onnxruntime import ORTModelForSequenceClassification
# Load the model from the hub and export it to the ONNX format
model_id = "distilbert-base-uncased-finetuned-sst-2-english"
model = ORTModelForSequenceClassification.from_pretrained(model_id, export=True)
```
Check out the whole guide ๐ https://huggingface.co/docs/optimum/onnxruntime/usage_guides/models | {
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"value": "As you may have probably heard, in the past weeks three Tech Giants (Microsoft, Amazon and Google) announced that they would bet on nuclear reactors to feed the surging energy demand of data centers, driven by increasing AI data and computational flows. ",
"raw": "As you may have probably heard, in the past weeks three Tech Giants (Microsoft, Amazon and Google) announced that they would bet on nuclear reactors to feed the surging energy demand of data centers, driven by increasing AI data and computational flows. ",
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"value": "I try to explain the state of AI energy consumptions, its environmental impact and the key points of \"turning AI nuclear\" in my last article on HF community blog: ",
"raw": "I try to explain the state of AI energy consumptions, its environmental impact and the key points of \"turning AI nuclear\" in my last article on HF community blog: ",
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] | Hi HuggingFacers!๐ค
As you may have probably heard, in the past weeks three Tech Giants (Microsoft, Amazon and Google) announced that they would bet on nuclear reactors to feed the surging energy demand of data centers, driven by increasing AI data and computational flows.
I try to explain the state of AI energy consumptions, its environmental impact and the key points of "turning AI nuclear" in my last article on HF community blog: https://huggingface.co/blog/as-cle-bert/ai-is-turning-nuclear-a-review
Enjoy the reading!๐ฑ | {
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] | Last Thursday at KaggleX organized by Google, I presented a workshop on "Unlocking the Power of Large Language Models (LLMs) for Business Applications" where I explained how we can reduce the size of LLM models to make them more suitable for business use and addressing common resource limitations.
https://drive.google.com/file/d/1p5sT4_DeyBuwCqmYt4dCJKZOgLMpESzR/view | {
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] | Every adult on the planet knows what a vector is and has the basic understanding of how they are utilized right in their heads. You just don't know it as vector math. You do not know a 2-D vector as a 2-D vector, you know it as a graph. Want to know more? Check out this video, I break down the concept in about 10 minutes and I am positive you will fully understand it by the end: https://youtu.be/Iny2ughcGsA | {
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"raw": "This text critiques AI language models, particularly in education, arguing their use hinders deep learning, critical thinking, and social-emotional development despite automating tasks. It raises copyright concerns, highlighting lawsuits against companies like OpenAI and Microsoft. AI \"creativity\" is challenged, emphasizing its reliance on stochastic processes and lack of true understanding. Ethical implications, including bias and misuse, are explored, along with environmental costs. The text contrasts the human brain's complex temporalities and adaptability with the static nature of current AI models, which lack genuine long-term memory and continuous learning.",
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] | Artificial Minds, Human Consequences: Unraveling AIโs Impact on Education, Cognition, and Cultural Production
https://empereur-pirate.medium.com/artificial-minds-human-consequences-unraveling-ais-impact-on-education-cognition-and-cultural-a503b88d4524
This text critiques AI language models, particularly in education, arguing their use hinders deep learning, critical thinking, and social-emotional development despite automating tasks. It raises copyright concerns, highlighting lawsuits against companies like OpenAI and Microsoft. AI "creativity" is challenged, emphasizing its reliance on stochastic processes and lack of true understanding. Ethical implications, including bias and misuse, are explored, along with environmental costs. The text contrasts the human brain's complex temporalities and adaptability with the static nature of current AI models, which lack genuine long-term memory and continuous learning. | {
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] | Made a notable change to the TTS Arena fork. I do not think anyone is interested in which bottomfeeder TTS is better than another beside it. So one of the top 5 TTS is always chosen in a challenge for more scrutiny. Also these top 5 are taken from preliminary results.
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"raw": "SambaNova โ๏ธ ",
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"raw": "โก Inference API with cURL Demo: ",
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"value": "๐Sambanova API Documentation : (grab your APIs here) ",
"raw": "๐Sambanova API Documentation : (grab your APIs here) ",
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] | SambaNova โ๏ธ
โก Inference API with cURL Demo: https://huggingface.co/spaces/prithivMLmods/sambanova-inference-api
๐Sambanova API Documentation : (grab your APIs here) https://cloud.sambanova.ai/apis
```
export SAMBANOVA_API_KEY=<your token>
```
Sambanova's Inference API.
```
pip install sambanova-gradio
```
SambaNova X Gradio
```
import gradio as gr
import sambanova_gradio
gr.load(
name='Meta-Llama-3.1-405B-Instruct',
src=sambanova_gradio.registry,
).launch()
```
๐ Documentation: https://community.sambanova.ai/docs
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118977614113249 | [
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] | I'm now working on finetuning of coding models. If you are GPU-hungry like me, you will find quantized models very helpful. But quantization for finetuning and inference are different and incompatible. So I made two collections here.
Inference (GGUF, via Ollama, CPU is enough)
https://huggingface.co/collections/onekq-ai/ollama-ready-coding-models-67118c3cfa1af2cf04a926d6
Finetuning (Bitsandbytes, QLora, GPU is needed)
https://huggingface.co/collections/onekq-ai/qlora-ready-coding-models-67118771ce001b8f4cf946b2
For quantization, the inference models are far more popular on HF than finetuning models. I use https://huggingface.co/QuantFactory to generate inference models (GGUF), and there are a few other choices.
But there hasn't been such a service for finetuning models. DIY isn't too hard though. I made a few myself and you can find the script in the model cards. If the original model is small enough, you can even do it on a free T4 (available via Google Colab).
If you know a (small) coding model worthy of quantization, please let me know and I'd love to add it to the collections. | {
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"value": "๐ข Excited to share that our studies ๐ \"Large Language Models in Targeted Sentiment Analysis for Russian\" has recently become in ๐ Springer Lobachevskii Journal of Mathematics ๐ฅณโจ ...",
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"value": "In this studies we provide such a diverse look and experiments over various ๐ค LLM models ๐ค scaled from 7B in two different modes: โ๏ธ zero-shot and ๐ฅ fine-tuned (Flan-T5 only) using Three-Hop reasoning technique.",
"raw": "In this studies we provide such a diverse look and experiments over various ๐ค LLM models ๐ค scaled from 7B in two different modes: โ๏ธ zero-shot and ๐ฅ fine-tuned (Flan-T5 only) using Three-Hop reasoning technique.",
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"value": "๐ application on Chain-of-Thought for Implicit Sentiment Analysis",
"raw": "๐ application on Chain-of-Thought for Implicit Sentiment Analysis",
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] | ๐ข Excited to share that our studies ๐ "Large Language Models in Targeted Sentiment Analysis for Russian" has recently become in ๐ Springer Lobachevskii Journal of Mathematics ๐ฅณโจ ...
๐ https://link.springer.com/article/10.1134/S1995080224603758
In this studies we provide such a diverse look and experiments over various ๐ค LLM models ๐ค scaled from 7B in two different modes: โ๏ธ zero-shot and ๐ฅ fine-tuned (Flan-T5 only) using Three-Hop reasoning technique.
We showcase the importance of performing:
๐ text translation into English
๐ application on Chain-of-Thought for Implicit Sentiment Analysis
More:
๐ Arxiv: https://arxiv.org/abs/2404.12342
๐งโ๐ป๏ธ Code: https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework
๐ค Models: https://huggingface.co/papers/2404.12342
๐ฅ Video @NLPSummit: https://www.youtube.com/watch?v=qawLJsRHzB4
THOR: https://github.com/scofield7419/THOR-ISA | {
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"value": " have developed a novel method called KV Prediction that significantly reduces the \"time to first token\" (TTFT) for on-device LLM inference.",
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] | Good folks at @Apple have developed a novel method called KV Prediction that significantly reduces the "time to first token" (TTFT) for on-device LLM inference.
Some highlights of the paper:
โข Uses a small auxiliary transformer model to efficiently predict the KV cache of a larger base model
โข Reduces TTFT by up to 4x while retaining 60-80% accuracy on benchmarks
โข Achieves Pareto-optimal efficiency-accuracy trade-off compared to baselines
โข Demonstrates 15-50% relative accuracy improvements on TriviaQA at equal TTFT FLOP budgets
โข Shows up to 30% accuracy gains on HumanEval code completion at fixed TTFT FLOP counts
โข Validated on Apple M2 Pro CPU, proving FLOP gains translate to real-world speedups
So, how's it done?
Based on the KV Prediction method described in the paper, here are the key steps for how it's done:
1. Choose a base model and an auxiliary model:
- The base model is a larger, pretrained transformer model that will be used for final generation.
- The auxiliary model is a smaller transformer model used to efficiently process the input prompt.
2. Design the KV predictor:
- Create a set of learned linear projections to map from the auxiliary model's KV cache to the base model's KV cache.
- Define a mapping from auxiliary cache layers to base cache layers.
3. Training process:
- Pass input tokens through the auxiliary model to get its KV cache.
- Use the KV predictor to generate a predicted KV cache for the base model.
- Run the base model using the predicted KV cache and compute losses.
- Backpropagate errors through the frozen base model to update the auxiliary model and KV predictor.
4. Inference process:
- Process the input prompt with the auxiliary model to get its KV cache.
- Use the KV predictor to generate the predicted base model KV cache.
- Run a single token generation step with the base model using the predicted KV cache.
- Continue autoregressive generation with the base model as normal.
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] | A new `timm` release (1.0.11) is out now. A also wrote an article on one of the included models: https://huggingface.co/blog/rwightman/mambaout
Featured in the release are:
* The MambaOut model, a cheeky arch inspired by SSM but without the SSM part, a ConvNeXt with gating.
* Several timm trained MambaOut variations with arch tweaks and ImageNet-12k pretrain to verify scaling, supplement ported weights.
* The smallest MobileNetV4, a 0.5x width scaled Conv-Small.
* Two impressive MobileNetV3 Large models outperforming all previous, using MNV4 Small recipe.
* 'Zepto,' a new compact ConvNeXt variant even smaller than the previous Atto, 2.2M params, RMSNorm, and solid results for its size.
* Newly ported SigLIP SO400M/16 ViT multi-lingual weights, the largest i18n weights, prevous was B/16.
* Two ImageNet-1k fine-tuned SigLIP SO400M models at 378x378
* InternViT 300M weight port. A really solid ViT encoder distilled from OpenGVLab 6B VL model encoder.
* An assortment of very small, sub 1M param pretrained test models to improve library unit tests and serve low-resource applications.
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] | China is advancing rapidly in AI technology while maintaining a strong focus on governance ๐จ๐ณ๐
We've collected key AI governance documents released since 2017 and will continue updating them in this organization on the hub ๐China LLMs on Hugging Face
โจ https://huggingface.co/spaces/zh-ai-community/china-ai-policy-research
Any feedback is welcome๐ค
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1. Meta Segment Anything Model 2.1: An updated checkpoint with improved results on visually similar objects, small objects and occlusion handling. A new developer suite will be added to make it easier for developers to build with SAM 2.
Model checkpoints: https://huggingface.co/collections/reach-vb/sam-21-6702d40defe7611a8bafa881
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Model checkpoints: https://huggingface.co/collections/facebook/layerskip-666b25c50c8ae90e1965727a
3. SALSA: New code enables researchers to benchmark AI-based attacks to validate security for post-quantum cryptography.
Repo: https://github.com/facebookresearch/LWE-benchmarking
4. Meta Lingua: A lightweight and self-contained codebase designed to train language models at scale.
Repo: https://github.com/facebookresearch/lingua
5. Meta Open Materials: New open source models and the largest dataset to accelerate AI-driven discovery of new inorganic materials.
Model checkpoints: https://huggingface.co/fairchem/OMAT24
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Model checkpoint: https://huggingface.co/facebook/MEXMA
7. Self-Taught Evaluator: a new method for generating synthetic preference data to train reward models without relying on human annotations.
Model checkpoint: https://huggingface.co/facebook/Self-taught-evaluator-llama3.1-70B
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"value": "๐๐ผ๐ ๐๐ผ ๐ฟ๐ฒ-๐ฟ๐ฎ๐ป๐ธ ๐๐ผ๐๐ฟ ๐๐ป๐ถ๐ฝ๐ฝ๐ฒ๐๐ ๐ถ๐ป ๐ฅ๐๐ โ ColBERT, Rerankers, Cross-Encoders",
"raw": "๐๐ผ๐ ๐๐ผ ๐ฟ๐ฒ-๐ฟ๐ฎ๐ป๐ธ ๐๐ผ๐๐ฟ ๐๐ป๐ถ๐ฝ๐ฝ๐ฒ๐๐ ๐ถ๐ป ๐ฅ๐๐ โ ColBERT, Rerankers, Cross-Encoders",
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"value": "Letโs say youโre doing RAG, and in an effort to improve performance, you try to rerank a few possible source snippets by their relevancy to a query.",
"raw": "Letโs say youโre doing RAG, and in an effort to improve performance, you try to rerank a few possible source snippets by their relevancy to a query.",
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"value": "How can you score similarity between your query and any source document? ๐ค ๐ โ๏ธ ๐",
"raw": "How can you score similarity between your query and any source document? ๐ค ๐ โ๏ธ ๐",
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"value": "๐ญ. ๐๐๐๐ ๐๐๐ฒ ๐ฒ๐บ๐ฏ๐ฒ๐ฑ๐ฑ๐ถ๐ป๐ด๐ : ๐ก๐ผ-๐ถ๐ป๐๐ฒ๐ฟ๐ฎ๐ฐ๐๐ถ๐ผ๐ป ๐๏ธ",
"raw": "๐ญ. ๐๐๐๐ ๐๐๐ฒ ๐ฒ๐บ๐ฏ๐ฒ๐ฑ๐ฑ๐ถ๐ป๐ด๐ : ๐ก๐ผ-๐ถ๐ป๐๐ฒ๐ฟ๐ฎ๐ฐ๐๐ถ๐ผ๐ป ๐๏ธ",
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"value": "This means that you encode each token from both the query and the doc as separate vectors, then average the tokens of each separately to get in total 2 vectors, then you compute similarity via cosine or something.",
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"value": "โก๏ธ Notable examples: Check the top of the MTEB leaderboard!",
"raw": "โก๏ธ Notable examples: Check the top of the MTEB leaderboard!",
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"value": "๐ฎ. ๐๐ฎ๐๐ฒ-๐ถ๐ป๐๐ฒ๐ฟ๐ฎ๐ฐ๐๐ถ๐ผ๐ป: ๐๐ต๐ถ๐ ๐ถ๐ ๐๐ผ๐น๐๐๐ฅ๐ง ๐",
"raw": "๐ฎ. ๐๐ฎ๐๐ฒ-๐ถ๐ป๐๐ฒ๐ฟ๐ฎ๐ฐ๐๐ถ๐ผ๐ป: ๐๐ต๐ถ๐ ๐ถ๐ ๐๐ผ๐น๐๐๐ฅ๐ง ๐",
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"value": "These encode each token from both query and doc as separate vectors as before, but compare all together without previously averaging them and losing information.",
"raw": "These encode each token from both query and doc as separate vectors as before, but compare all together without previously averaging them and losing information.",
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"value": "This is more accurate than no-interaction but also slower because you have to compare n*m vectors instead of 2. At least you can store documents in memory. And ColBERT has some optimisations like pooling to be faster.",
"raw": "This is more accurate than no-interaction but also slower because you have to compare n*m vectors instead of 2. At least you can store documents in memory. And ColBERT has some optimisations like pooling to be faster.",
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"value": "โก๏ธ Notable examples: ColBERTv2, mxbai-colbert-large-v1, jina-colbert-v2",
"raw": "โก๏ธ Notable examples: ColBERTv2, mxbai-colbert-large-v1, jina-colbert-v2",
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"value": "๐ฏ. ๐๐ฎ๐ฟ๐น๐ ๐ถ๐ป๐๐ฒ๐ฟ๐ฎ๐ฐ๐๐ถ๐ผ๐ป: ๐๐ฟ๐ผ๐๐-๐ฒ๐ป๐ฐ๐ผ๐ฑ๐ฒ๐ฟ๐ ๐๏ธ",
"raw": "๐ฏ. ๐๐ฎ๐ฟ๐น๐ ๐ถ๐ป๐๐ฒ๐ฟ๐ฎ๐ฐ๐๐ถ๐ผ๐ป: ๐๐ฟ๐ผ๐๐-๐ฒ๐ป๐ฐ๐ผ๐ฑ๐ฒ๐ฟ๐ ๐๏ธ",
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"value": "Basically you run the concatenated query + document in a model to get a final score.",
"raw": "Basically you run the concatenated query + document in a model to get a final score.",
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"value": "This is the most accurate, but also the slowest since it gets really long when you have many docs to rerank! And you cannot pre-store embeddings.",
"raw": "This is the most accurate, but also the slowest since it gets really long when you have many docs to rerank! And you cannot pre-store embeddings.",
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"value": "โก๏ธ Notable examples: MixedBread or Jina AI rerankers!",
"raw": "โก๏ธ Notable examples: MixedBread or Jina AI rerankers!",
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"value": "๐ So what you choose is a trade-off between speed and accuracy: I think ColBERT is often a really good choice!",
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] | ๐๐ผ๐ ๐๐ผ ๐ฟ๐ฒ-๐ฟ๐ฎ๐ป๐ธ ๐๐ผ๐๐ฟ ๐๐ป๐ถ๐ฝ๐ฝ๐ฒ๐๐ ๐ถ๐ป ๐ฅ๐๐ โ ColBERT, Rerankers, Cross-Encoders
Letโs say youโre doing RAG, and in an effort to improve performance, you try to rerank a few possible source snippets by their relevancy to a query.
How can you score similarity between your query and any source document? ๐ค ๐ โ๏ธ ๐
๐ญ. ๐๐๐๐ ๐๐๐ฒ ๐ฒ๐บ๐ฏ๐ฒ๐ฑ๐ฑ๐ถ๐ป๐ด๐ : ๐ก๐ผ-๐ถ๐ป๐๐ฒ๐ฟ๐ฎ๐ฐ๐๐ถ๐ผ๐ป ๐๏ธ
This means that you encode each token from both the query and the doc as separate vectors, then average the tokens of each separately to get in total 2 vectors, then you compute similarity via cosine or something.
โก๏ธ Notable examples: Check the top of the MTEB leaderboard!
๐ฎ. ๐๐ฎ๐๐ฒ-๐ถ๐ป๐๐ฒ๐ฟ๐ฎ๐ฐ๐๐ถ๐ผ๐ป: ๐๐ต๐ถ๐ ๐ถ๐ ๐๐ผ๐น๐๐๐ฅ๐ง ๐
These encode each token from both query and doc as separate vectors as before, but compare all together without previously averaging them and losing information.
This is more accurate than no-interaction but also slower because you have to compare n*m vectors instead of 2. At least you can store documents in memory. And ColBERT has some optimisations like pooling to be faster.
โก๏ธ Notable examples: ColBERTv2, mxbai-colbert-large-v1, jina-colbert-v2
๐ฏ. ๐๐ฎ๐ฟ๐น๐ ๐ถ๐ป๐๐ฒ๐ฟ๐ฎ๐ฐ๐๐ถ๐ผ๐ป: ๐๐ฟ๐ผ๐๐-๐ฒ๐ป๐ฐ๐ผ๐ฑ๐ฒ๐ฟ๐ ๐๏ธ
Basically you run the concatenated query + document in a model to get a final score.
This is the most accurate, but also the slowest since it gets really long when you have many docs to rerank! And you cannot pre-store embeddings.
โก๏ธ Notable examples: MixedBread or Jina AI rerankers!
๐ So what you choose is a trade-off between speed and accuracy: I think ColBERT is often a really good choice!
Based on this great post by Jina AI ๐ https://jina.ai/news/what-is-colbert-and-late-interaction-and-why-they-matter | {
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DepthPro is a zero-shot depth estimation model by Apple, it's fast, sharp and accurate ๐ฅ
Demo: https://huggingface.co/spaces/akhaliq/depth-pro
Model: https://huggingface.co/apple/DepthPro
Paper page: https://huggingface.co/papers/2410.02073
The model consists of two encoders: an encoder for patches and an image encoder ๐ผ๏ธ The outputs of both are merged to decode to depth maps and get the focal length.
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โจ ๐ง๐ผ๐ฝ ๐๐ถ๐ด๐ต๐น๐ถ๐ด๐ต๐๐:
- ๐ย Multiple access tokens support: Easily manage multiple access tokens with new CLI commands. Perfect for handling multiple tokens with specific permissions in production or when collaborating with external teams.
- ๐ผ๏ธ Conversational VLMs inference is now supported withย InferenceClient's chat completion!
- ๐ Daily Papers API: Seamlessly search and retrieve detailed paper information from the Hub!
Weโve also introduced multiple bug fixes and quality-of-life improvements - thanks to the awesome contributions from our community! ๐ค
Check out the release notes here: https://huggingface.co/spaces/Wauplin/huggingface_hub/discussions/9
and you can try it out now ๐
```
pip install huggingface_hub==0.26.0
```
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๐ Check it out: https://huggingface.co/hbseong/HarmAug-Guard (Yes, INFERENCE CODE INCLUDED! ๐ก)
More details in our paper: https://arxiv.org/abs/2410.01524 ๐
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๐ https://huggingface.co/spaces/nmarafo/Child-Safe-Chatbot
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] | ๐ Introducing ColFlor: An Efficient, OCR-Free Vision-Language Document Retrieval Model ๐
Earlier this year, ColPali revolutionized document retrieval by eliminating the need for error-prone OCR pipelines. Instead, it directly processes the document images. However, with its 3 billion parameters, ColPali is computationally heavy for large-scale applications.
Thatโs where ColFlor comes inโa smaller, faster alternative! ๐ At 17x smaller than ColPali, ColFlor offers a more efficient, OCR-free document retrieval solution, making it ideal for users with limited computing resources (GPU Poor). ๐ก
Key Highlights:
๐ง 174M parameters (vs. 3B for ColPali)
โก 9.8x faster query encoding, 5.25x faster image encoding
๐ Only 1.8% performance drop on text-rich English documents
Check out the full blog post for more insights on modeling, training, and evaluations across various document retrieval tasks! ๐
Also, feel free to try our demo on huggingface ๐ค
๐ Resources:
๐ Blog post: https://huggingface.co/blog/ahmed-masry/colflor
๐ง Model: https://huggingface.co/ahmed-masry/ColFlor
๐ป Demo: https://huggingface.co/spaces/ahmed-masry/ColFlor-Demo
๐๏ธโโ๏ธ Training code: https://github.com/AhmedMasryKU/colflor
๐ Evaluation code: https://github.com/AhmedMasryKU/vidore-benchmark-colflor
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] | Here is how we can calculate the size of any LLM model:
Each parameter in LLM models is typically stored as a floating-point number. The size of each parameter in bytes depends on the precision.
32-bit precision: Each parameter takes 4 bytes.
16-bit precision: Each parameter takes 2 bytes
To calculate the total memory usage of the model:
Memory usage (in bytes) = No. of Parameters ร Size of Each Parameter
For example:
32-bit Precision (FP32)
In 32-bit floating-point precision, each parameter takes 4 bytes.
Memory usage in bytes = 1 billion parameters ร 4 bytes
1,000,000,000 ร 4 = 4,000,000,000 bytes
In gigabytes: โ 3.73 GB
16-bit Precision (FP16)
In 16-bit floating-point precision, each parameter takes 2 bytes.
Memory usage in bytes = 1 billion parameters ร 2 bytes
1,000,000,000 ร 2 = 2,000,000,000 bytes
In gigabytes: โ 1.86 GB
It depends on whether you use 32-bit or 16-bit precision, a model with 1 billion parameters would use approximately 3.73 GB or 1.86 GB of memory, respectively. | {
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] | ๐ Introducing Ukr-lit.com.ua Presentations Dataset - https://huggingface.co/datasets/nyuuzyou/ukr-lit
Dataset highlights:
- 18,001 presentations from ukr-lit.com.ua, a platform for storing and viewing presentations covering a wide range of subjects in Ukrainian school education
- Primarily in Ukrainian, with some Russian and English content
- Each entry includes: URL, title, download URL, filepath, and extracted text content (where available)
- Contains original PPT/PPTX files in addition to metadata
- Data covers a broad spectrum of educational topics and subjects taught in Ukrainian schools
- Dedicated to the public domain under Creative Commons Zero (CC0) license
The dataset can be used for analyzing educational presentation content across various subjects in Ukrainian and other languages, text classification tasks, and information retrieval systems. It's particularly valuable for examining trends in Ukrainian school education, teaching methodologies, and presentation materials used across different academic disciplines. The inclusion of original files allows for in-depth analysis of presentation formats and structures commonly used in Ukrainian educational settings, providing insights into the diverse range of subjects and teaching approaches in the Ukrainian school system. | {
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] | ๐ Super cool visualization of global PUT requests to Hugging Face over 24 hours, coded by object size, thanks to @port8080!
We're putting this analysis to work to help us architect a more geo-distributed system for the HF storage backend.
Originally shared on LinkedIn: https://www.linkedin.com/posts/ajitbanerjee_one-of-the-joys-of-working-on-the-xethub-activity-7252688424732614656-tFGD | {
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You simply:
- Define your dataset, including annotation guidelines, labels and fields
- Optionally label some records manually.
- Use an LLM to auto label your data with a human (you? your team?) in the loop!
Get started with this blog post:
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] | You can now build a custom text classifier without days of human labeling!
๐ LLMs work reasonably well as text classifiers.
๐ They are expensive to run at scale and their performance drops in specialized domains.
๐ Purpose-built classifiers have low latency and can potentially run on CPU.
๐ They require labeled training data.
Combine the best of both worlds: the automatic labeling capabilities of LLMs and the high-quality annotations from human experts to train and deploy a specialized model.
Blog: https://huggingface.co/blog/sdiazlor/custom-text-classifier-ai-human-feedback | {
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] | By far the coolest release of the day!
> The Open LLM Leaderboard, most comprehensive suite for comparing Open LLMs on many benchmarks, just released a comparator tool that lets you dig into the detail of differences between any models.
Here's me checking how the new Llama-3.1-Nemotron-70B that we've heard so much compares to the original Llama-3.1-70B. ๐ค๐
Try it out here ๐ https://huggingface.co/spaces/open-llm-leaderboard/comparator | {
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"value": "Want to see how two different versions of LLaMA stack up? Letโs walk through a step-by-step comparison of LLaMA-3.1 and LLaMA-3.2. ๐ฆ๐งต๐",
"raw": "Want to see how two different versions of LLaMA stack up? Letโs walk through a step-by-step comparison of LLaMA-3.1 and LLaMA-3.2. ๐ฆ๐งต๐",
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"value": "- Search for \"LLaMA-3.1\" and \"LLaMA-3.2\" in the model dropdowns.",
"raw": "- Search for \"LLaMA-3.1\" and \"LLaMA-3.2\" in the model dropdowns.",
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"value": "- Press the Load button. Ready to dive into the results!",
"raw": "- Press the Load button. Ready to dive into the results!",
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"raw": "- Head over to the Results tab.",
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"value": "- Here, youโll see the performance metrics for each model, beautifully color-coded using a gradient to highlight performance differences: greener is better! ๐",
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"value": "- Want to focus on a specific task? Use the Task filter to hone in on comparisons for tasks like BBH or MMLU-Pro.",
"raw": "- Want to focus on a specific task? Use the Task filter to hone in on comparisons for tasks like BBH or MMLU-Pro.",
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",
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"raw": "5/ With this tool, itโs never been easier to explore how small changes between model versions affect performance on a wide range of tasks. Whether youโre a researcher or enthusiast, you can instantly visualize improvements and dive into detailed comparisons.",
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"value": "๐ Try the ๐ค Open LLM Leaderboard Comparator now and take your model evaluations to the next level!",
"raw": "๐ Try the ๐ค Open LLM Leaderboard Comparator now and take your model evaluations to the next level!",
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] | ๐จ Weโve just released a new tool to compare the performance of models in the ๐ค Open LLM Leaderboard: the Comparator ๐
https://huggingface.co/spaces/open-llm-leaderboard/comparator
Want to see how two different versions of LLaMA stack up? Letโs walk through a step-by-step comparison of LLaMA-3.1 and LLaMA-3.2. ๐ฆ๐งต๐
1/ Load the Models' Results
- Go to the ๐ค Open LLM Leaderboard Comparator: https://huggingface.co/spaces/open-llm-leaderboard/comparator
- Search for "LLaMA-3.1" and "LLaMA-3.2" in the model dropdowns.
- Press the Load button. Ready to dive into the results!
2/ Compare Metric Results in the Results Tab ๐
- Head over to the Results tab.
- Here, youโll see the performance metrics for each model, beautifully color-coded using a gradient to highlight performance differences: greener is better! ๐
- Want to focus on a specific task? Use the Task filter to hone in on comparisons for tasks like BBH or MMLU-Pro.
3/ Check Config Alignment in the Configs Tab โ๏ธ
- To ensure youโre comparing apples to apples, head to the Configs tab.
- Review both modelsโ evaluation configurations, such as metrics, datasets, prompts, few-shot configs...
- If something looks off, itโs good to know before drawing conclusions! โ
4/ Compare Predictions by Sample in the Details Tab ๐
- Curious about how each model responds to specific inputs? The Details tab is your go-to!
- Select a Task (e.g., MuSR) and then a Subtask (e.g., Murder Mystery) and then press the Load Details button.
- Check out the side-by-side predictions and dive into the nuances of each modelโs outputs.
5/ With this tool, itโs never been easier to explore how small changes between model versions affect performance on a wide range of tasks. Whether youโre a researcher or enthusiast, you can instantly visualize improvements and dive into detailed comparisons.
๐ Try the ๐ค Open LLM Leaderboard Comparator now and take your model evaluations to the next level! | {
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] | All the way from Korea, a novel approach called Mentor-KD significantly improves the reasoning abilities of small language models.
Mentor-KD introduces an intermediate-sized "mentor" model to augment training data and provide soft labels during knowledge distillation from large language models (LLMs) to smaller models.
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2) Use the mentor to generate additional CoT rationales and soft probability distributions.
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- CoT rationales from both the teacher and mentor (rationale distillation).
- Soft labels from the mentor (soft label distillation).
Results show that Mentor-KD consistently outperforms baselines, with up to 5% accuracy gains on some tasks.
Mentor-KD is especially effective in low-resource scenarios, achieving comparable performance to baselines while using only 40% of the original training data.
This work opens up exciting possibilities for making smaller, more efficient language models better at complex reasoning tasks.
What are your thoughts on this approach? | {
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] | ๐จ I have $3,500 in Azure credits, including access to an H100 (96 Go), expiring on November 12, 2024.
I wonโt be able to use it all myself, so Iโm reaching out to the @huggingface community: Are there any open-source projets with data ready for some compute power?
Letโs collaborate and make the most of it together ๐
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Have you ever dreamt of an improbable books crossover, like Frodo from ๐๐ฐ๐ณ๐ฅ ๐ฐ๐ง ๐ต๐ฉ๐ฆ ๐๐ช๐ฏ๐จ๐ด becoming the main character of the ๐๐ฅ๐บ๐ด๐ด๐ฆ๐บ or Emma Bovary from ๐๐ข๐ฅ๐ข๐ฎ๐ฆ ๐๐ฐ๐ท๐ข๐ณ๐บ acting as a modern-days Shakespearean Juliet?
Well, all of this is now possible! I'm thrilled to introduce my latest opensource product for storytelling: ๐๐จ๐จ๐ค๐ฌ-๐ฆ๐ข๐ฑ๐๐ซ-๐๐ข ๐ฏ๐.๐.๐ !
Built with ReactJS and shipped directly to you on Spaces thanks to Docker, this webapp combines the power of two AI tools:
- gpt-4o-mini by OpenAI, which takes care of cooking new and intriguing plots starting from the user's instructions, the titles and the summaries of the two books to mix (summaries are scraped through Wikipedia)
- text2img realtime API by ModelsLab, which provides a stable diffusion pipeline to create a thumbnail for your newly-generated story
Everything is provided under a simple and intuitive UI, which uses chatscope's React template kit.
Curious of trying? The app is already live at:
https://huggingface.co/spaces/as-cle-bert/books-mixer-ai
And you can also have a tour of the GitHub repo (and leave a little โญ while you're there):
https://github.com/AstraBert/books-mixer-ai
The documentation is still under construction, but will become available soon๐
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] | Today I was able to solve a very difficult coding session with GPT-4o which ended up solving integrations on a very large scale. So I decided to look a bit more into how its reasoners work. Below is a fun markdown emoji outline about what I learned today and what I'm pursuing.
Hope you enjoy! Cheers, Aaron.
Also here are my favorite last 4 spaces I am working on:
1. GPT4O: https://huggingface.co/spaces/awacke1/GPT-4o-omni-text-audio-image-video
2. Claude:
https://huggingface.co/spaces/awacke1/AnthropicClaude3.5Sonnet-ACW
3. MSGraph M365: https://huggingface.co/spaces/awacke1/MSGraphAPI
4. Azure Cosmos DB: Now with Research AI! https://huggingface.co/spaces/awacke1/AzureCosmosDBUI
# ๐ OpenAI's O1 Models: A Quantum Leap in AI
## 1. ๐ค From ๐ฆ to ๐ง : O1's Evolution
- **Thinking AI**: O1 ponders before replying; GPT models just predict. ๐ก
## 2. ๐ AI Memory: ๐พ + ๐งฉ = ๐ง
- **Embeddings & Tokens**: Words โก๏ธ vectors, building knowledge. ๐
## 3. ๐ Swift Knowledge Retrieval
- **Vector Search & Indexing**: O1 finds info fast, citing reliable sources. ๐๐
## 4. ๐ณ Logic Trees with Mermaid Models
- **Flowchart Reasoning**: O1 structures thoughts like diagrams. ๐จ๐
## 5. ๐ป Coding Mastery
- **Multilingual & Current**: Speaks many code languages, always up-to-date. ๐ป๐
## 6. ๐ Breaking Records
- **92.3% MMLU Score**: O1 outperforms humans, setting new AI standards. ๐
## 7. ๐ก Versatile Applications
- **Ultimate Assistant**: From fixing code to advancing research. ๐ ๏ธ๐ฌ
## 8. ๐ Racing Toward AGI
- **OpenAI Leads**: O1 brings us closer to true AI intelligence. ๐
## 9. ๐ค O1's Reasoning Pillars
- **๐ง Chain of Thought**: Step-by-step logic.
- **๐ฒ MCTS**: Simulates options, picks best path.
- **๐ Reflection**: Self-improves autonomously.
- **๐๏ธโโ๏ธ Reinforcement Learning**: Gets smarter over time.
---
*Stay curious, keep coding!* ๐
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Yes, they may be subpar and may require changes to llama.cpp to support the interleaved sliding window
Yes, I got excited when a conversion worked and released them ASAP
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I have appended -TEST to the model names in an attempt to indicate that they are not final or perfect, but if people still feel mislead and that it's not the right thing to do, please post (civilly) below your thoughts, I will highly consider pulling the conversions if that's what people think is best. After all, that's what I'm here for, in service to you all ! | {
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model: https://huggingface.co/facebook/cotracker3
demo: https://huggingface.co/spaces/facebook/cotracker | {
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] | Today I found out about the existence of https://huggingface.co/utter-project/EuroLLM-1.7B-Instruct and unexpectedly it is really good. I think it's a very underrated model - give it a try https://huggingface.co/spaces/nyuuzyou/EuroLLM-1.7B-Instruct | {
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- Neurons as logic gates
- Weighted sums and activation functions
- Gradient descent and backpropagation
No complex equations or jargon, just clear explanations and helpful visuals!
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"value": "... When Foucault directly addresses the question of power, namely, one of his great theses: no, power does not repress, or it represses only secondarily. What does it do? It does something much more profound and, doubtless, more formidable that repressing: it forms, it shapes. It does not silence, it does worse: it makes speak. It disciplines, it standardizes [normalise]. But repression is entirely secondary in relation to the positive operations of power.",
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"value": "From the Deleuze Seminars at Universitรฉ Paris 8 translated by Purdue University -> ",
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] | Philosopher Gilles Deleuze in 1985-86 about society of control, probabilities, and power. Visionary words in an era of autoregressive models:
"The biopolitics of populations appears when right sets about administering life, says Foucault, administering life in any open multiplicities whatever. You see the importance of the difference between discipline and biopolitics. The one is in an open space, with large multiplicities to which limits are not assignable. They can only be treated by the calculus of probabilities, hence the development of the calculus of probabilities and the meaning [sens] of the social control of probabilities, the probabilities of marriage in a nation, the probabilities of mortality, probabilities of natality. Natality, nuptiality, mortality โฆ
... When Foucault directly addresses the question of power, namely, one of his great theses: no, power does not repress, or it represses only secondarily. What does it do? It does something much more profound and, doubtless, more formidable that repressing: it forms, it shapes. It does not silence, it does worse: it makes speak. It disciplines, it standardizes [normalise]. But repression is entirely secondary in relation to the positive operations of power.
Power does not repress, it disciplines, it manages, it controls, it standardizes, etcetera. It does not silence, it makes speak. It does not prevent acting, it makes act."
From the Deleuze Seminars at Universitรฉ Paris 8 translated by Purdue University -> https://deleuze.cla.purdue.edu/ | {
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] | The Synthetic Data Generator now directly integrates with Argilla, so you can generate and curate your own high-quality datasets from pure natural language!
Up next -> include dataset generation for text classification.
Other suggestions? Let us know.
Space: https://huggingface.co/spaces/argilla/synthetic-data-generator
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] | While Google's Transformer might have introduced "Attention is all you need," Microsoft and Tsinghua University are here with the DIFF Transformer, stating, "Sparse-Attention is all you need."
The DIFF Transformer outperforms traditional Transformers in scaling properties, requiring only about 65% of the model size or training tokens to achieve comparable performance.
The secret sauce? A differential attention mechanism that amplifies focus on relevant context while canceling out noise, leading to sparser and more effective attention patterns.
How?
- It uses two separate softmax attention maps and subtracts them.
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- It implements GroupNorm for each attention head independently.
- It is compatible with FlashAttention for efficient computation.
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- Superior long-context modeling (up to 64K tokens).
- Enhanced key information retrieval.
- Reduced hallucination in question-answering and summarization tasks.
- More robust in-context learning, less affected by prompt order.
- Mitigation of activation outliers, opening doors for efficient quantization.
Extensive experiments show DIFF Transformer's advantages across various tasks and model sizes, from 830M to 13.1B parameters.
This innovative architecture could be a game-changer for the next generation of LLMs. What are your thoughts on DIFF Transformer's potential impact? | {
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] | MixGen3 is an innovative image generation service that utilizes LoRA (Low-Rank Adaptation) models. Its key features include:
Integration of various LoRA models: Users can explore and select multiple LoRA models through a gallery.
Combination of LoRA models: Up to three LoRA models can be combined to express unique styles and content.
User-friendly interface: An intuitive interface allows for easy model selection, prompt input, and image generation.
Advanced settings: Various options are provided, including image size adjustment, random seed, and advanced configurations.
Main applications of MixGen3:
Content creation
Design and illustration
Marketing and advertising
Education and learning
Value of MixGen3:
Enhancing creativity
Time-saving
Collaboration possibilities
Continuous development
Expected effects:
Increased content diversity
Lowered entry barrier for creation
Improved creativity
Enhanced productivity
MixGen3 is bringing a new wave to the field of image generation by leveraging the advantages of LoRA models. Users can experience the service for free at
https://openfree-mixgen3.hf.space
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] | Model is always disabled?
#script...
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2",
token="xxxxxx")
That loads the model fine. But if used by index returned from VectorStoreIndex for QDrant like this:
#script...
query_engine = index_from_nodes.as_query_engine(llm=model, streaming=True)
response = query_engine.query(
"What is formula 1?"
)
response.print_response_stream()
It errors out with a disabled error:
AssertionError Traceback (most recent call last)
Cell In[34], line 1
----> 1 query_engine = index_from_nodes.as_query_engine(llm=model, streaming=True)
3 response = query_engine.query(
4 "What is formula 1?"
5 )
7 response.print_response_stream()
File ~/miniconda/lib/python3.9/site-packages/llama_index/core/indices/base.py:376, in BaseIndex.as_query_engine(self, llm, **kwargs)
370 from llama_index.core.query_engine.retriever_query_engine import (
371 RetrieverQueryEngine,
372 )
374 retriever = self.as_retriever(**kwargs)
375 llm = (
--> 376 resolve_llm(llm, callback_manager=self._callback_manager)
377 if llm
378 else Settings.llm
379 )
381 return RetrieverQueryEngine.from_args(
382 retriever,
383 llm=llm,
384 **kwargs,
385 )
File ~/miniconda/lib/python3.9/site-packages/llama_index/core/llms/utils.py:102, in resolve_llm(llm, callback_manager)
99 print("LLM is explicitly disabled. Using MockLLM.")
100 llm = MockLLM()
--> 102 assert isinstance(llm, LLM)
104 llm.callback_manager = callback_manager or Settings.callback_manager
106 return llm
AssertionError:
So why is the LLM disabled?
Thanks! | {
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/5kE1rvdIVfUftt7B__ysg.png",
"fullname": "Thomas Tong",
"name": "gtvracer",
"type": "user",
"isPro": true,
"isHf": false,
"isMod": false,
"followerCount": null,
"isFollowing": false
} | [] | [] | [
{
"reaction": "๐",
"users": [
"John6666"
],
"count": 1
}
] | 2024-10-16T00:17:18.000Z | 2024-10-16T01:56:58.460Z | [
{
"avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/6640bbd0220cfa8cbfdce080/wiAHUu5ewawyipNs0YFBR.png",
"fullname": "John Smith",
"name": "John6666",
"type": "user",
"isPro": true,
"isHf": false,
"isMod": false,
"followerCount": 398,
"isFollowing": false
}
] | /posts/gtvracer/732889644445945 | 607 | 1 |
936434772663807 | [
{
"type": "text",
"value": "๐ข We are giving extra two weeks before switching to the final stage of RuOpinionNE-2024.",
"raw": "๐ข We are giving extra two weeks before switching to the final stage of RuOpinionNE-2024.",
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"value": "We have already first baseline submission by ๐จโ๐ป RefalMachine that showcase F1 = 0.17 based on Qwen2 model series.",
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] | ๐ข We are giving extra two weeks before switching to the final stage of RuOpinionNE-2024.
โฐ The final stage starts since 1-st of November 2024.
We have already first baseline submission by ๐จโ๐ป RefalMachine that showcase F1 = 0.17 based on Qwen2 model series.
For those who wish to attend:
๐ Codalab: https://codalab.lisn.upsaclay.fr/competitions/20244
๐ Task: https://codalab.lisn.upsaclay.fr/competitions/20244#learn_the_details-overview
๐ Updates: https://t.me/RuOpinionNE2024
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๐งช Past experiments: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
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This book features new advances in game-changing AI and LLM technologies built by GenAItechLab.com. Written in simple English, it is best suited for engineers, developers, data scientists, analysts, consultants and anyone with an analytic background interested in starting a career in AI. The emphasis is on scalable enterprise solutions, easy to implement, yet outperforming vendors both in term of speed and quality, by several orders of magnitude.
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โก๏ธ Part 1: Hallucination-Free LLM with Real-Time Fine-Tuning
โก๏ธ Part 2: Outperforming Neural Nets and Classic AI
โก๏ธ Part 3: Innovations in Statistical AI
About the author
Vincent Granville is a pioneering GenAI scientist and machine learning expert, co-founder of Data Science Central (acquired by a publicly traded company in 2020), Chief AI Scientist atย ML Techniquesย andย GenAI Techlab, former VC-funded executive, author (Elsevier) and patent owner โ one related to LLM. Vincentโs past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, and CNET.
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] | Don't use an LLM when you can use a much cheaper model.
The problem is that no one tells you how to actually do it.
Just picking a pre-trained model (e.g., BERT) and throwing it at your problem won't work!
If you want a small model to perform well on your problem, you need to fine-tune it.
And to fine-tune it, you need data.
The good news is that you don't need a lot of data but instead high-quality data for your specific problem.
In the latest livestream, I showed you guys how to get started with Argilla on the Hub! Hope to see you at the next one.
https://www.youtube.com/watch?v=BEe7shiG3rY
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] | ๐จ New Agent Benchmark ๐จ
AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents
https://huggingface.co/datasets/ai-safety-institute/AgentHarm
Collaboration between UK AI Safety Institute and Gray Swan AI to create a dataset for measuring harmfulness of LLM agents.
The benchmark contains both harmful and benign sets of 11 categories with varied difficulty levels and detailed evaluation, not only testing success rate but also tool level accuracy.
We provide refusal and accuracy metrics across a wide range of models in both no attack and prompt attack scenarios.
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] | New York Times to Perplexity: Stop Using Our Stuff
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โWe are very much interested in working with every single publisher, including the New York Times,โ Srinivas said. โWe have no interest in being anyoneโs antagonist here.โ
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] | ๐ We're excited to share our project on mobile face detection using MediaPipe and ZETIC.MLange.
๐ Key highlights:
1. Introduction to Mediapipe face detection model
2. Developing on-device AI applications with ZETIC.MLange
3. Guide to creating object detection apps utilizing Mobile NPUs
๐ฑ Learn how to build a high-performance face detection app that operates entirely on-device, no cloud required! We explore:
- Real-time face analysis techniques
- Enhanced security measures
- Privacy protection strategies
๐ฅ Check out our demo video showcasing real-time face detection:
Face detection Demo: https://youtu.be/GXtJKk7MdjQ?si=41UFKL8IBwx5nlxs
๐ Full Tutorial
For a step-by-step guide and in-depth discussion, read our full blog post:
https://zetic.ai/blog/implementing-face-detection-on-device-ai-with-zetic-mlange
We'd love to hear your thoughts, experiences, or questions in the comments below. | {
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They don't make survey papers like they used to, but this is an exciting new survey on Transformer LM interpretability!
This comprehensive survey provides a technical deep dive into:
โข Transformer architecture components (attention, FFN, residual stream)
โข Methods for localizing model behavior:
- Input attribution (gradient & perturbation-based)
- Component importance (logit attribution, causal interventions)
โข Information decoding techniques:
- Probing, linear feature analysis
- Sparse autoencoders for disentangling features
โข Key insights on model internals:
- Attention mechanisms (induction heads, copy suppression)
- FFN neuron behaviors
- Residual stream properties
- Multi-component emergent behaviors
The paper offers a unified notation and connects insights across different areas of interpretability research. It's a must-read for anyone working on understanding large language models!
Some fascinating technical highlights:
- Detailed breakdowns of attention head circuits (e.g., IOI task)
- Analysis of factual recall mechanisms
- Overview of polysemanticity and superposition
- Discussion of grokking as circuit emergence
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- 21,172 air traffic control audio recordings from LiveATC.net for August 26, 2024
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"value": "But they didnโt release weights on the Hub: letโs wait for the community to train the first open-weights DiffTransformer! ๐",
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] | โก๏ธ ๐๐ก๐ข๐ฌ ๐ฆ๐จ๐ง๐ญ๐ก'๐ฌ ๐ฆ๐จ๐ฌ๐ญ ๐ข๐ฆ๐ฉ๐จ๐ซ๐ญ๐๐ง๐ญ ๐๐ซ๐๐๐ค๐ญ๐ก๐ซ๐จ๐ฎ๐ ๐ก: ๐๐ข๐๐๐๐ซ๐๐ง๐ญ๐ข๐๐ฅ ๐๐ซ๐๐ง๐ฌ๐๐จ๐ซ๐ฆ๐๐ซ ๐ฏ๐๐ฌ๐ญ๐ฅ๐ฒ ๐ข๐ฆ๐ฉ๐ซ๐จ๐ฏ๐๐ฌ ๐๐ญ๐ญ๐๐ง๐ญ๐ข๐จ๐ง โ ๐๐๐ญ๐ญ๐๐ซ ๐ซ๐๐ญ๐ซ๐ข๐๐ฏ๐๐ฅ ๐๐ง๐ ๐๐๐ฐ๐๐ซ ๐ก๐๐ฅ๐ฅ๐ฎ๐๐ข๐ง๐๐ญ๐ข๐จ๐ง๐ฌ!
Thought that self-attention could not be improved anymore?
Microsoft researchers have dropped a novel "differential attention" mechanism that amplifies focus on relevant context while canceling out noise. It sounds like a free lunch, but it does really seem to vastly improve LLM performance!
๐๐ฒ๐ ๐ถ๐ป๐๐ถ๐ด๐ต๐๐:
๐ง Differential attention computes the difference between two separate softmax attention maps, canceling out noise and promoting sparse attention patterns
๐ฅ DIFF Transformer outperforms standard Transformers while using 35-40% fewer parameters or training tokens
๐ Scales well to long contexts up to 64K tokens, leveraging increasing context length more effectively
๐ Dramatically improves key information retrieval, enhancing in-context learning, and possibly reducing risk of hallucinations ๐คฏ
๐ข Reduces activation outliers, potentially enabling lower-bit quantization without performance drop!
โ๏ธ Can be directly implemented using existing FlashAttention kernels
This new architecture could lead much more capable LLMs, with vastly improved strengths in long-context understanding and factual accuracy.
But they didnโt release weights on the Hub: letโs wait for the community to train the first open-weights DiffTransformer! ๐
Read their paper ๐ย https://huggingface.co/papers/2410.05258 | {
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Full files and article : https://www.patreon.com/posts/112099700
Download images in full resolution to see prompts and model names
All trainings are done with Kohya GUI, perfectly can be done locally on Windows, and all trainings were 1024x1024 pixels
Fine Tuning / DreamBooth works as low as 6 GB GPUs (0 quality degrade totally same as 48 GB config)
Best quality of LoRA requires 48 GB GPUs , 24 GB also works really good and minimum 8 GB GPU is necessary for LoRA (lots of quality degrade)
Full size grids are also shared for the followings: https://www.patreon.com/posts/112099700
Additionally, I have shared full training entire logs that you can see each checkpoint took time. I have shared best checkpoints, their step count and took time according to being either LoRA, Fine Tuning or Batch size 1 or 7 or 15 images or 256 images, so a very detailed article regarding completed.
Check the images to see all shared files in the post.
Furthermore, a very very detailed analysis having article written and all latest DreamBooth / Fine Tuning configs and LoRA configs are shared with Kohya GUI installers for both Windows, Runpod and Massed Compute.
Moreover, I have shared new 28 realism and 37 stylization testing prompts.
Current tutorials are as below:
Windows requirements CUDA, Python, cuDNN, and such : https://youtu.be/DrhUHnYfwC0
How to use SwarmUI : https://youtu.be/HKX8_F1Er_w
How to use FLUX on SwarmUI : https://youtu.be/bupRePUOA18
How to use Kohya GUI for FLUX training : https://youtu.be/nySGu12Y05k
How to use Kohya GUI for FLUX training on Cloud (RunPod and Massed Compute) : https://youtu.be/-uhL2nW7Ddw
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This app shows initial implementation of security, authentication, scopes, and access to Outlook, Calendar, Tasks, Onedrive and other apps for CRUD pattern as AI agent service skills to integrate with your AI workflow.
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URL: https://huggingface.co/spaces/awacke1/MSGraphAPI
Discussion: https://huggingface.co/spaces/awacke1/MSGraphAPI/discussions/5
Best of AI on @Azure and @Microsoft on @HuggingFace :
https://huggingface.co/microsoft
https://www.microsoft.com/en-us/research/
---
Aaron
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"raw": "> MLS -> WhisperVQ tokens -> Llama 3.1",
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] | Multimodal Ichigo Llama 3.1 - Real Time Voice AI ๐ฅ
> WhisperSpeech X Llama 3.1 8B
> Trained on 50K hours of speech (7 languages)
> Continually trained on 45hrs 10x A1000s
> MLS -> WhisperVQ tokens -> Llama 3.1
> Instruction tuned on 1.89M samples
> 70% speech, 20% transcription, 10% text
> Apache 2.0 licensed โก
Architecture:
> WhisperSpeech/ VQ for Semantic Tokens
> Llama 3.1 8B Instruct for Text backbone
> Early fusion (Chameleon)
I'm super bullish on HomeBrew/ Jan and early fusion, audio and text, multimodal models!
(P.S. Play with the demo on Hugging Face: https://huggingface.co/spaces/jan-hq/Ichigo-llama3.1-s-instruct)
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] | Just started going through the latest "State of AI Report 2024", and I cannot get over the predictions!
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NVIDIAโs dominance will remain largely unchallenged, investment in humanoid robots will decline, Appleโs on-device AI research will gain momentum, and a research paper by an AI scientist will be accepted at a major conference.
Lastly, a GenAI-based video game is expected to achieve breakout success.
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Exactly one year ago, I shared the initial version of this side-project on Hugging Face. Since then, there have been numerous changes under the hood. Nowadays it uses [Web-LLM](https://github.com/mlc-ai/web-llm), [Wllama](https://github.com/ngxson/wllama) and [SearXNG](https://github.com/searxng/searxng). I use it daily as my default search engine and have done my best to make it useful. I hope it's interesting for you too!
HF Space: https://huggingface.co/spaces/Felladrin/MiniSearch
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] | SwiftMistralCoreML
Hi Everyone,
I have created a Swift library to interact with Mistral 7B models in CoreML on macOS.
I hope you find it helpful.
https://github.com/cardona/SwiftMistralCoreML
An open-source Swift library that enables macOS and iOS projects to utilize the Mistral-Interact7B models (INT4 and upcoming FP16) in chat mode. This library includes a complete Swift implementation of the tokenizer and Byte Pair Encoding (BPE) encoder, providing an out-of-the-box solution for integrating advanced language models into your Swift applications.
Features
Full Swift Implementation: Includes tokenizer and BPE encoder written entirely in Swift.
CoreML Integration: Leverages Apple's CoreML framework to run Mistral-Interact7B models efficiently.
Multiple Decoding Strategies: Supports Greedy and Top-K sampling, with plans to add more strategies.
Chat Functionality: Designed to work in chat mode for interactive applications.
FP16 Support (Coming Soon): Future version will support FP16 models for improved performance.
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] | Hello Everyone,
I signed up as Pro and started a ZeroGPU space with a Gradio chatbot project as default. When building the space, it won't even start the sample Gradio app.. Pretty disappointing when right out of the box, it fails...
Have anyone encountered this yet?
Thanks...
This is the output, odd since it seems to be just a warning. So why wouldn't it start?
/usr/local/lib/python3.10/site-packages/gradio/components/chatbot.py:228: UserWarning: The 'tuples' format for chatbot messages is deprecated and will be removed in a future version of Gradio. Please set type='messages' instead, which uses openai-style 'role' and 'content' keys.
warnings.warn(
* Running on local URL: http://0.0.0.0:7860, with SSR โก
To create a public link, set `share=True` in `launch()`.
Stopping Node.js server... | {
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Medical AI Paper of the Week:",
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] | Last Week in Medical AI: Top Research Papers/Models
๐
(October 5 - October 12, 2024)
๐
Medical AI Paper of the Week:
MMedAgent: Learning to Use Medical Tools with Multi-modal Agent
YouTube podcast of weekly papers: https://youtu.be/OD3C5jirszw
Medical LLM & Other Models:
- LLM Framework for Rare Disease Phenotyping
- ONCOPILOT: CT Foundation Model for Tumors
- FMBench: Fairness in Medical MLLMs
- GP-GPT: LLM for Gene-Phenotype Mapping
- MedAdapter: Efficient LLM Medical Adaptation
- RespLLM: Multimodal LLM for Respiratory Health
- MDAgents: LLM Collaboration for Medical Decisions
- MedVisionLlama: LLM Medical Image Segmentation
Frameworks and Methodologies:
- ReXplain: AI-Driven Radiology Video Reports
- BioDiscoveryAgent: AI for Genetic Experiments
- ZODIAC: Multi-Agent Cardiological Diagnostics
- OLAPH: Improving Biomedical LLM Factuality
- OmniGenBench: Benchmarking Genomic Models
Medical LLM Applications:
- MMedAgent: Multimodal Medical Tool Use
- AI for Mental Health Support
- LLMs for Mental Disorders Detection
- PharmacyGPT: AI Pharmacist Framework
Medical LLMs & Benchmarks:
- CliMedBench: Chinese Medical LLM Benchmark
- MedSafetyBench: Evaluating Medical LLM Safety
AI in Healthcare Ethics:
- LLM-based Medical Dialogue Preference Alignment
- Trustworthiness in Medical Imaging Models | {
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] | ๐ก๐ฒ๐ ๐น๐ฒ๐ฎ๐ฑ๐ฒ๐ฟ๐ฏ๐ผ๐ฎ๐ฟ๐ฑ ๐ฟ๐ฎ๐ป๐ธ๐ ๐๐๐ ๐ ๐ณ๐ผ๐ฟ ๐๐๐ -๐ฎ๐-๐ฎ-๐ท๐๐ฑ๐ด๐ฒ: ๐๐น๐ฎ๐บ๐ฎ-๐ฏ.๐ญ-๐ณ๐ฌ๐ ๐๐ผ๐ฝ๐ ๐๐ต๐ฒ ๐ฟ๐ฎ๐ป๐ธ๐ถ๐ป๐ด๐! ๐งโโ๏ธ
Evaluating systems is critical during prototyping and in production, and LLM-as-a-judge has become a standard technique to do it.
First, what is "LLM-as-a-judge"?
๐ It's a very useful technique for evaluating LLM outputs. If anything you're evaluating cannot be properly evaluated with deterministic criteria, like the "politeness" of an LLM output, or how faithful it is to an original source, you can use LLM-judge instead : prompt another LLM with "Here's an LLM output, please rate this on criterion {criterion} on a scale of 1 to 5", then parse the number from its output, and voilร , you get your score.
๐ง But who judges the judge?
How can you make sure your LLM-judge is reliable? You can have a specific dataset annotated with scores provided by human judges, and compare how LLM-judge scores correlate with human judge scores.
๐ Before even running that benchmark, to get you started, there's a new option to get you started: a leaderboard that measures how well different model perform as judges!
And the outcome is surprising, models come in quite different orders from what we're used to in general rankings: probably some have much better bias mitigation than others!
Take a deeper look here ๐ https://huggingface.co/blog/arena-atla | {
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802713390155238 | [
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Adding "r.jina.ai/" before any url transforms it in Markdown using Jina AI's Reader! Here with @cyrilzakka's blog post. | {
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https://huggingface.co/spaces/takarajordan/CineDiffusion
Super excited to announce CineDiffusion๐ฅ, it creates images up to 4.2 Megapixels in Cinematic ultrawide formats like:
- 2.39:1 (Modern Widescreen)
- 2.76:1 (Ultra Panavision 70)
- 3.00:1 (Experimental Ultra-wide)
- 4.00:1 (Polyvision)
- 2.55:1 (CinemaScope)
- 2.20:1 (Todd-AO)
More to come soon!!
Thanks to @John6666 and @Resoldjew for your early support <3
And thanks to the team at ShuttleAI for their brand new Shuttle-3 model, what an amazing job.
https://huggingface.co/shuttleai/shuttle-3-diffusion | {
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Introducing the 435M model that outperforms Llama-Guard-3-8B while slashing 75% of the computation cost! ๐ป๐ฅ
๐ Check it out: https://huggingface.co/hbseong/HarmAug-Guard (Yes, INFERENCE CODE INCLUDED! ๐ก)
More details in our paper: https://arxiv.org/abs/2410.01524 ๐
#HarmAug #LLM # Safety #EfficiencyBoost #Research #AI #MachineLearning | {
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878288650656797 | [
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Let's observe Qwen2.5-coder:0.5b on OpenAI HumanEval.
`pip install observers`
And start collecting your data on the Hugging Face Hub.
Dataset: https://huggingface.co/datasets/davidberenstein1957/openai_records
Library: https://github.com/cfahlgren1/observers | {
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