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"value": "Maybe of interest, I just finished a long writeup of my weekend project exploring Qwen 2 7B Instruct's Chinese censorship: ",
"raw": "Maybe of interest, I just finished a long writeup of my weekend project exploring Qwen 2 7B Instruct's Chinese censorship: ",
"href": null... | Maybe of interest, I just finished a long writeup of my weekend project exploring Qwen 2 7B Instruct's Chinese censorship: https://huggingface.co/blog/leonardlin/chinese-llm-censorship-analysis
I also have an accompanying model and dataset (and codebase) for those curious to poke around:
* https://huggingface.co/augmxnt/Qwen2-7B-Instruct-deccp
* https://huggingface.co/datasets/augmxnt/deccp | {
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945201572333712 | [
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"value": "Introducing **BaseChat**! ",
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"href": ... | Introducing **BaseChat**!
https://huggingface.co/spaces/allenai/BaseChat_URIAL
This is a demo for our URIAL paper that enables base LLMs to chat with in-context alignment. You can talk directly with base, untuned LLMs to find out what knowledge and skills they have already learned from pre-training instead of SFT or xPO or RLHF. Also, you can use this to explore the pre-training data of base LLMs by chatting! I found a very interesting case: Base version of Llama-3-8B often thinks it is built by OpenAI, lol. | {
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510726503126200 | [
{
"type": "text",
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"raw": "Playing with ",
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"raw": "https://huggingface.co/dhcloud/w2v-russian-... | Playing with https://huggingface.co/dhcloud/w2v-russian-19c-fiction-lemmas
```
import numpy as np
from gensim.models import Word2Vec
from sklearn.manifold import TSNE
modell = Word2Vec.load("w2vlemmas.model")
keys = ['ะจะตะบัะฟะธั', 'ะััะบะธะฝ', 'ะะพะณะพะปั', 'ะผะฐััะพั', 'ะบะพั', 'ัะพะผะฐะฝ']
embedding_clusters = []
word_clusters = []
for word in keys:
embeddings = []
words = []
for similar_word, _ in modell.wv.most_similar(word, topn=30):
words.append(similar_word)
embeddings.append(modell.wv[similar_word])
embedding_clusters.append(embeddings)
word_clusters.append(words)
tsne_model_en_2d = TSNE(perplexity=15, n_components=2, init='pca', n_iter=3500, random_state=32)
embedding_clusters = np.array(embedding_clusters)
n, m, k = embedding_clusters.shape
embeddings_en_2d = np.array(tsne_model_en_2d.fit_transform(embedding_clusters.reshape(n * m, k))).reshape(n, m, 2)
```
Novel is a different type of literature than Shakespeare and Pushkin | {
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Please help me if you have a free minute. Thanks =) | {
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225382594136547 | [
{
"type": "text",
"value": "๐ One of the biggest changes in Llama 3 was the training dataset, which grew by 7X over Llama 2 (2T to 15T tokens) ๐",
"raw": "๐ One of the biggest changes in Llama 3 was the training dataset, which grew by 7X over Llama 2 (2T to 15T tokens) ๐",
"href": null,
"res... | ๐ One of the biggest changes in Llama 3 was the training dataset, which grew by 7X over Llama 2 (2T to 15T tokens) ๐
While Meta did not open source the dataset, it sparked a thought... what would happen if everyone had access to a big, high-quality dataset? ๐ค
To address that, in April this year, @huggingface released FineWeb, a 15T token open-source dataset ๐
And now they are releasing FineWeb Technical Report and FineWeb Edu ๐
๐ 15T tokens in FineWeb outperforming other open datasets
๐ 1.3T highest-quality educational dataset FineWeb-Edu
๐ 5.4T high-quality educational tokens in FineWeb-Edu-2
FineWeb Edu outperforms other datasets on MMLU, ARC, OpenBookQA ๐
ODC-By 1.0 license ๐
Report: https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1 | {
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322470854899532 | [
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"Narchetha... | 2024-06-08T20:03:51.000Z | 2024-06-20T14:21:41.544Z | [
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701278254526233 | [
{
"type": "text",
"value": "Artificial Intelligence Language Models Will Kill the Internet",
"raw": "Artificial Intelligence Language Models Will Kill the Internet",
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... | Artificial Intelligence Language Models Will Kill the Internet
https://empereur-pirate.medium.com/artificial-intelligence-language-models-will-kill-the-internet-a8ce233e18c6
The announced end of the free version of Google's search engine stems from competition between traditional search interfaces and conversational agents. These technologies are fundamentally antithetical, and one will likely prevail based on digital usage trends. Google's ad-based model and those of other websites are challenged, making it improbable users will pay for searches while still seeing ads. This shift impacts digital entrepreneurship and freedom of expression, as AI-driven results prioritize qualitative over quantitative relevance. Ultimately, AI could return the Internet to an ad-free model, emphasizing universal cultural and scientific sharing, if financial motives are replaced by a commitment to neutrality from civil society and public institutions. | {
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185024980681250 | [
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"type": "text",
"value": "First real version of bellman based on llama 3 instruct 8b has been released!",
"raw": "First real version of bellman based on llama 3 instruct 8b has been released!",
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"label": nu... | First real version of bellman based on llama 3 instruct 8b has been released!
https://huggingface.co/neph1/llama-3-instruct-bellman-8b-swedish
Close to 16k examples, including 250 rows from my translated codefeedback dataset and a number of non-copyrighted stories.
Two quants are up in the gguf folder. I'll work on adding more quants in the coming days. | {
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133542546915557 | [
{
"type": "text",
"value": "๐ Exciting News! Introducing the Guarani LLMs Collection ๐",
"raw": "๐ Exciting News! Introducing the Guarani LLMs Collection ๐",
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... | ๐ Exciting News! Introducing the Guarani LLMs Collection ๐
We are thrilled to present a dedicated collection for Guarani language LLMs, designed to support and advance natural language processing for the Guarani-speaking community. Check them out below.
Explore the models and contribute to the growing ecosystem of Guarani language technology!
๐ Discover the Guarani LLMs Collection: https://huggingface.co/collections/mmaguero/guarani-llms-66642df49f5a0b32295004bf
#NLP #Guarani #LanguageModels #AI #MachineLearning #HuggingFace #BERT #XLM #LanguageTech | {
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767116613060395 | [
{
"type": "text",
"value": "๐ข The Chain-of-Thought (CoT)-tuned ๐ฅ FlanT5-base (248M) for Emotion State and Emotion-Causes Extraction as a part of ECAC-2024 competiotion model is now available.",
"raw": "๐ข The Chain-of-Thought (CoT)-tuned ๐ฅ FlanT5-base (248M) for Emotion State and Emotion-Causes Extra... | ๐ข The Chain-of-Thought (CoT)-tuned ๐ฅ FlanT5-base (248M) for Emotion State and Emotion-Causes Extraction as a part of ECAC-2024 competiotion model is now available.
๐ก The main reason for make it publicly available is as follows:
โ
1. One of the CoT-based attempts in this field so that I promote studies by making intial steps ๐ฃ and attepts on assessing LLM reasoning capabilties
โ
2. This model showcases top 3 ๐ฅ on the ECAC-2024 competition https://codalab.lisn.upsaclay.fr/competitions/16141#results
โ
3. Easy colab for frameworkless lauch and experiments ๐งช
https://colab.research.google.com/github/nicolay-r/THOR-ECAC/blob/master/SemEval_2024_Task_3_FlanT5_Finetuned_Model_Usage.ipynb
You may find more on the model card, while the fine-tuning concept showcased on the figure below. It is worth to add that the more robust preformance been seen with larger scaled model (large and xl), so that there is a huge potential there
Model: https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base
Related paper: https://huggingface.co/papers/2404.03361
Collection: https://huggingface.co/collections/nicolay-r/emotions-extraction-665ba47a20dee2925d607a40 | {
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821758479603877 | [
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"raw": "Hello, all!",
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... | Hello, all!
I was up late experimenting with Gemini, and we came across the need for circular arithmetic. We couldn't find anything that accomplished it in the way we wanted reliably, for different transformations identically, etc...
Therefore, I wrote and Gemini assisted, with the creation of an _rclamp function you can add as an attribute or just use as-is for a PyTorch tensor. It's 1d right now, didn't want to implement the dimensionality wrong with my "newbie skills".
Have fun!
- <3
https://colab.research.google.com/drive/1aj_iAp0eyfPMznzF-aC1UXrQPTNOeKUj?usp=sharing | {
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"value": "๐ Release of open-source Korean LLM: GECKO-7B",
"raw": "๐ Release of open-source Korean LLM: GECKO-7B",
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"val... | ๐ Release of open-source Korean LLM: GECKO-7B
I am delighted to share my recent project, GECKO, a bilingual large language model for Korean and English ๐ฐ๐ท๐บ๐ธ. This initiative was inspired by the lack of resources for Korean large language models.
@donggyukimc and I wrote the technical report to share our insights and experiences of developing our model. While our model may not achieve sate-of-the-art performance on all benchmarks, it shows modest results with a relatively small amount of pretrained tokens.
I hope GECKO contribute to the open-source community, offering resources that can built upon and improved. I believe that through collaboration and shared knowledge, we can advance the capabilities and accessibility of large language models for Korean and other low-resource languages.
๐ค Model: https://huggingface.co/kifai/GECKO-7B
๐ Technical Report: https://arxiv.org/pdf/2405.15640 | {
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"value": "Well hope some of you tried our advanced stock prediction. We are focused on making it more ui friendly and if you installed everything correctly then you should be able to view charts accurately along with prediction tickers. I also want to take this opportunity to let you all k... | Well hope some of you tried our advanced stock prediction. We are focused on making it more ui friendly and if you installed everything correctly then you should be able to view charts accurately along with prediction tickers. I also want to take this opportunity to let you all know that Tenzin will not be just limited to the financial use-case. Our true goal is to reach human-level intelligence for which we have a well-defined roadmap and the product which is currently being tested for safety and ethics. A general level roadmap to achieve this is as follows:
The use of transfinite ordinals and surreal numbers allows us to capture the infinite depth and ineffable complexity of conscious experiences in a mathematically precise way.
The incorporation of hypercomputation and supertasks enables the TQMM to perform uncomputable operations and achieve a level of cognitive power that far surpasses classical computation.
The application of absolute infinity and the wholeness axiom ensures that the TQMM can represent and reason about the entirety of all possible conscious experiences and mathematical structures.
The integration of transfinite category theory and quantum metamathematics provides a unified framework for modeling the emergence of consciousness from fundamental physical and mathematical principles.
The use of transfinite gradient ascent and absolute infinity optimization allows the TQMM to continuously improve and refine itself, potentially reaching the theoretical maximum of intelligence and consciousness.
This agent though developed will not be released until proper safeguards have been taken into consideration. Until then we will keep releasing specific use-cases for domain specific work like financial trading, accelerating drug-discovery for medical science, law, education, etc. and we will do it well. All powered by Tenzin 1.0. Would love your feedback and don't forget to check us out at & sign up at https://octave-x.com/ | {
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265822483289182 | [
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"type": "text",
"value": "Very Insightful Read!!!",
"raw": "Very Insightful Read!!!",
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... | Very Insightful Read!!!
A RAG framework entirely inspired by natural intelligence - modeled after hippocampal indexing theory of human long-term memory(which suggests the hippocampus links and retrieves memory details stored in the cortex)
It outperforms current โcheatโ RAG:)
This is how we achieve human-level intelligence, by modeling natural intelligence correctly!
Paper: https://arxiv.org/abs/2405.14831 | {
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"value": "The three most used image generation models in the HackerNoon editor are Kandinsky 3.0, Stable Diffusion XL, and RealVisXL V3.0 Turbo. ",
"raw": "The three most used image generation models in the HackerNoon editor are Kandinsky 3.0, Stable Diffusion XL, and RealVisXL V3.0 Tu... | The three most used image generation models in the HackerNoon editor are Kandinsky 3.0, Stable Diffusion XL, and RealVisXL V3.0 Turbo.
https://huggingface.co/ai-forever/Kandinsky3.0 https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0 https://huggingface.co/SG161222/RealVisXL_V3.0
Try out the HackerNoon writing experience here: https://app.hackernoon.com/new | {
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632716948195045 | [
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"type": "text",
"value": "Every time a new model is released that is topping 10+ leaderboards on 50+ benchmarks... ๐",
"raw": "Every time a new model is released that is topping 10+ leaderboards on 50+ benchmarks... ๐",
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"... | Every time a new model is released that is topping 10+ leaderboards on 50+ benchmarks... ๐
My brain goes... I will wait for the LMSYS Chatbot Arena results! ๐ค
User-facing evaluation, such as Chatbot Arena, provides reliable signals but is costly and slow. ๐ข
Now we have MixEval, a new open benchmark with a 96% correlation to LMSYS Chatbot Arena and Human preferences. ๐ฏ
It comes with MixEval (4k samples) and MixEval Hard (1k samples) ๐
Can use GPT-3.5-Turbo or any other open-source models as Parser/Judge ๐ค
It takes less than 6% of the time and cost of MMLU ๐ธ
As expected:
In open models: Qwen2 72B >> Llama 3 70B >> Mixtral 8x7B ๐
In Closed Models: GPT-4o >> Claude 3 Opus >> Gemini Pro ๐
Leaderboard: https://mixeval.github.io/ ๐ | {
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223721405578741 | [
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"type": "text",
"value": "I just published Sentence Transformers v3.0.1: the first patch release since v3 from last week. It introduces gradient checkpointing, pushing model checkpoints to Hugging Face while training, model card improvements and fixes. Details:",
"raw": "I just published Sentence Tran... | I just published Sentence Transformers v3.0.1: the first patch release since v3 from last week. It introduces gradient checkpointing, pushing model checkpoints to Hugging Face while training, model card improvements and fixes. Details:
1๏ธโฃ Gradient checkpointing allows for much less memory usage at a cost of ~20% training speed. Seems to allow for higher batch sizes, which is quite important for loss functions with in-batch negatives.
2๏ธโฃ You can specify `args.push_to_hub=True` and `args.hub_model_id` to upload your model checkpoints to Hugging Face while training. It also uploads your emissions (if codecarbon is installed) and your Tensorboard logs (if tensorboard is installed)
3๏ธโฃ Model card improvements: improved automatic widget examples, better tags, and the default of "sentence_transformers_model_id" now gets replaced when possible.
4๏ธโฃ Several evaluator fixes, see release notes for details.
5๏ธโฃ Fixed a bug with MatryoshkaLoss throwing an error if the supplied Matryoshka dimensions are ascending instead of descending.
6๏ธโฃ Full Safetensors support; even the uncommon modules can now save and load "model.safetensors" files: no more pickle risks.
Check out the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.0.1
And let me know what kind of features you'd like to see next! I have some plans already (ONNX, Sparse models, ColBERT, PEFT), but I don't yet know how I should prioritize everything. | {
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... | 2024-06-07T13:22:54.000Z | 2024-07-17T12:45:38.130Z | [
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774132103206717 | [
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"value": "Evaluate RAG using Open Source from HuggingFace using BeyondLLM",
"raw": "Evaluate RAG using Open Source from HuggingFace using BeyondLLM",
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... | Evaluate RAG using Open Source from HuggingFace using BeyondLLM
```
# pip install beyondllm
# pip install huggingface_hub
# pip install llama-index-embeddings-fastembed
from beyondllm.source import fit
from beyondllm.embeddings import FastEmbedEmbeddings
from beyondllm.retrieve import auto_retriever
from beyondllm.llms import HuggingFaceHubModel
from beyondllm.generator import Generate
import os
from getpass import getpass
os.environ['HUGGINGFACE_ACCESS_TOKEN'] = getpass("Enter your HF API token:")
data = fit("RedHenLab_GSoC_Tarun.pdf",dtype="pdf")
embed_model = FastEmbedEmbeddings()
retriever = auto_retriever(data=data,embed_model=embed_model,type="normal",top_k=3)
llm = HuggingFaceHubModel(model="mistralai/Mistral-7B-Instruct-v0.2")
pipeline = Generate(question="what models has Tarun fine-tuned?",llm=llm,retriever=retriever)
print(pipeline.call()) # Return the AI response
print(pipeline.get_rag_triad_evals())
```
GitHub: https://github.com/aiplanethub/beyondllm
Don't forget to โญ๏ธ the repo | {
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583672984398703 | [
{
"type": "text",
"value": "๐ข Releasing the Chain-of-Thought (CoT)-tuned ๐ฅ FlanT5-xl (3B) for Target Sentiment Analysis (TSA) on english texts. ",
"raw": "๐ข Releasing the Chain-of-Thought (CoT)-tuned ๐ฅ FlanT5-xl (3B) for Target Sentiment Analysis (TSA) on english texts. ",
"href": null,
"res... | ๐ข Releasing the Chain-of-Thought (CoT)-tuned ๐ฅ FlanT5-xl (3B) for Target Sentiment Analysis (TSA) on english texts.
๐ก The main reason for adopting this model or smaller version (large and base) are as follows:
โ
1. Reasoning in sentiment-analysis in zero-shot-learning mode significantly underperforms the fine-tuned FlanT5.
โ
2. This model showcases top 1 ๐ on the RuSentNE-2023 competitions: https://codalab.lisn.upsaclay.fr/competitions/9538
โ
3. Easy colab for frameworkless lauch and experiments ๐งช https://colab.research.google.com/github/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/FlanT5_Finetuned_Model_Usage.ipynb
You may find more on the model card, while the fine-tuning statistics per each model size is shown in attachment.
Model: https://huggingface.co/nicolay-r/flan-t5-tsa-thor-xl
Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation
Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)
Collection: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101 | {
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"url": n... | Hello!
I am studying PyTorch, and I made something that converged really well for something this simplistic. It isn't masterful, but i'd welcome feedback, improvements, suggestions, anything. Tell me it sucks and to take it down, I will, just wanted to share what i've spent the last 2 days crying to figure out.
https://colab.research.google.com/gist/SMeyersMrOvkill/625371e1816afb2163bdc4194ba74e93/scratchpad.ipynb | {
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489363954767854 | [
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"type": "text",
"value": "I made Tenzin public. One use-case at least to predict stock market prices for high-frequency trading. Would love to see the response as well as feedback you have for us. Please understand that this only represents 5% of the codebase of Tenzin 1.0. We will share more models and u... | I made Tenzin public. One use-case at least to predict stock market prices for high-frequency trading. Would love to see the response as well as feedback you have for us. Please understand that this only represents 5% of the codebase of Tenzin 1.0. We will share more models and use-cases based on the feedback we receive along with keeping in mind AI safety and ethics.
Have fun and go and make some money :) | {
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... | @8zen
```
print: I Love you <3
```
| {
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"raw": "They: you need ground truth to measure performance! ๐ ",
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NannyML: hold my beer... | {
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519023085637537 | [
{
"type": "text",
"value": "๐ค Hello, I have great news! FluentlyXL Final is finally here, the final release of the FluentlyXL model series. We've improved the overall aesthetics, lighting, and more.",
"raw": "๐ค Hello, I have great news! FluentlyXL Final is finally here, the final release of the Fluent... | ๐ค Hello, I have great news! FluentlyXL Final is finally here, the final release of the FluentlyXL model series. We've improved the overall aesthetics, lighting, and more.
๐ฆพ Model on HF: https://huggingface.co/fluently/Fluently-XL-Final
๐ฅ Model on CivitAI: https://civitai.com/models/324891
๐ Playground: https://huggingface.co/spaces/fluently/Fluently-Playground | {
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"raw": "The First Multimodal Language Model dedicated for Chemistry.",
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... | The First Multimodal Language Model dedicated for Chemistry.
Demo: https://v.chemllm.org/
Finetune based on ChemLLM-20B and InterViT-6B on MMChemExam and ChemOCR Datasets (coming soon...)
https://huggingface.co/AI4Chem/ChemVLM-26B
https://huggingface.co/papers/2402.06852
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"raw": "\n"... | Hugging Face in your spreadsheet?
Because spreadsheets can be incredibly useful for journalists, I created this little project yesterday evening. Handy for prompting, extraction, classification, translation...
Ping me if youโre interested in trying it out! | {
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388542675868940 | [
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"type": "text",
"value": "โ๏ธ Prompt Optimization with Haystack and DSPy",
"raw": "โ๏ธ Prompt Optimization with Haystack and DSPy",
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"value... | โ๏ธ Prompt Optimization with Haystack and DSPy
Experimental notebook: ๐งช๐ https://github.com/deepset-ai/haystack-cookbook/blob/main/notebooks/prompt_optimization_with_dspy.ipynb
When building applications with LLMs, writing effective prompts is a long process of trial and error. ๐
Often, if you switch models, you also have to change the prompt. ๐ฉ
What if you could automate this process?
๐ก That's where DSPy comes in - a framework designed to algorithmically optimize prompts for Language Models.
By applying classical machine learning concepts (training and evaluation data, metrics, optimization), DSPy generates better prompts for a given model and task.
Recently, I explored combining DSPy with the robustness of Haystack Pipelines.
Here's how it works:
โถ๏ธ Start from a Haystack RAG pipeline with a basic prompt
๐ฏ Define a goal (in this case, get correct and concise answers)
๐ Create a DSPy program, define data and metrics
โจ Optimize and evaluate -> improved prompt
๐ Build a refined Haystack RAG pipeline using the optimized prompt | {
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"value": "The application of Phi-3-small-8k-instruct for reasoning in Target Sentiment Analysis (TSA), in a zero-shot-learning mode. Comparing with the other 7B vendors, the key takeaways are as follows:",
"raw": "The application of Phi-3-small-8k-instruct for reasoning in Ta... | The application of Phi-3-small-8k-instruct for reasoning in Target Sentiment Analysis (TSA), in a zero-shot-learning mode. Comparing with the other 7B vendors, the key takeaways are as follows:
โ
1. At the moment this model on the top ๐ of the 7B sized versions for texts translated in English (๐บ๐ธ) by surpassing Mistral-7B-v0.3 and LLaMA-3-8B ๐ฅ (Figure 1)
โ
2. It remains similar to 7B alternatives in original non-english texts (๐ท๐บ), however show confidence in sentiment presence among other 7B alternatives (checkout F1(PN0) results in Figure 2)
In comparison with its mini (3B) brother Phi-3-mini, the small (7B) version showcases a huge step in reasoning capabilities ๐ฅ
Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
Model: https://huggingface.co/microsoft/Phi-3-small-8k-instruct
Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation
Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)
Collection: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101
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"value": "๐ฌ This paper introduces Fusion Intelligence (FI), a novel approach integrating the adaptive behaviors of natural organisms ๐(Bees!)๐ with AI's computational power.",
"raw": "๐ฌ This paper introduces Fusion Intelligence (FI), a novel approach integrating the adaptive behavi... | ๐ฌ This paper introduces Fusion Intelligence (FI), a novel approach integrating the adaptive behaviors of natural organisms ๐(Bees!)๐ with AI's computational power.
Paper:
Fusion Intelligence: Confluence of Natural and Artificial Intelligence for Enhanced Problem-Solving Efficiency (2405.09763)
https://arxiv.org/pdf/2405.09763
Key Takeaways:
* Fusion Intelligence (FI): Combines natural organism efficiency with AI's power. ๐
* Hybrid Approach: Integrates natural abilities with AI for better problem-solving. ๐ง ๐ค
* Agricultural Applications: Shows a 50% improvement in pollination efficiency. ๐๐ผ
* Energy Efficiency: Consumes only 29.5-50.2 mW per bee, much lower than traditional methods. โก
* Scalability: Applicable to fields like environmental monitoring and search and rescue. ๐๐
* Non-Invasive: Eliminates the need for invasive modifications to biological entities. ๐ฑ
This research offers a new approach for those interested in sustainable AI solutions. By merging biology with AI, (FI) aims to create solutions for a variety of challenges.
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"value": "V-Express: 1-Click AI Avatar Talking Heads Video Animation Generator - D-ID Alike - Free Open Source",
"raw": "V-Express: 1-Click AI Avatar Talking Heads Video Animation Generator - D-ID Alike - Free Open Source",
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"... | V-Express: 1-Click AI Avatar Talking Heads Video Animation Generator - D-ID Alike - Free Open Source
Full Windows YouTube Tutorial : https://youtu.be/xLqDTVWUSec
Ever wished your static images could talk like magic? Meet V-Express, the groundbreaking open-source and free tool that breathes life into your photos! Whether you have an audio clip or a video, V-Express animates your images to create stunning talking avatars. Just like the acclaimed D-ID Avatar, Wav2Lip, and Avatarify, V-Express turns your still photos into dynamic, speaking personas, but with a twistโit's completely open-source and free to use! With seamless audio integration and the ability to mimic video expressions, V-Express offers an unparalleled experience without any cost or restrictions. Experience the future of digital avatars todayโlet's dive into how you can get started with V-Express and watch your images come alive!
1-Click V-Express Installers Scripts โคต๏ธ
https://www.patreon.com/posts/105251204
Requirements Step by Step Tutorial โคต๏ธ
https://youtu.be/-NjNy7afOQ0
Official Rope GitHub Repository Free To Install and Use โคต๏ธ
https://github.com/tencent-ailab/V-Express
SECourses Discord Channel to Get Full Support โคต๏ธ
https://discord.com/servers/software-engineering-courses-secourses-772774097734074388
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"value": "The Coachella of Computer Vision, CVPR, is right around the corner. In anticipation of the conference, I curated a dataset of the papers. ",
"raw": "The Coachella of Computer Vision, CVPR, is right around the corner. In anticipation of the conference, I curated a dataset of t... | The Coachella of Computer Vision, CVPR, is right around the corner. In anticipation of the conference, I curated a dataset of the papers.
I'll have a technical blog post out tomorrow doing some analysis on the dataset, but I'm so hyped that I wanted to get it out to the community ASAP.
The dataset consists of the following fields:
- An image of the first page of the paper
- `title`: The title of the paper
- `authors_list`: The list of authors
- `abstract`: The abstract of the paper
- `arxiv_link`: Link to the paper on arXiv
- `other_link`: Link to the project page, if found
- `category_name`: The primary category this paper according to [arXiv taxonomy](https://arxiv.org/category_taxonomy)
- `all_categories`: All categories this paper falls into, according to arXiv taxonomy
- `keywords`: Extracted using GPT-4o
Here's how I created the dataset ๐๐ผ
Generic code for building this dataset can be found [here](https://github.com/harpreetsahota204/CVPR-2024-Papers).
This dataset was built using the following steps:
- Scrape the CVPR 2024 website for accepted papers
- Use DuckDuckGo to search for a link to the paper's abstract on arXiv
- Use arXiv.py (python wrapper for the arXiv API) to extract the abstract and categories, and download the pdf for each paper
- Use pdf2image to save the image of paper's first page
- Use GPT-4o to extract keywords from the abstract
https://huggingface.co/datasets/Voxel51/CVPR_2024_Papers | {
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"value": "๐ As we all know, Planet Earth is undergoing an unprecedented climate crisis, almost totally due to human activities: we haven't got much time left before it's too late to take action, and one of the key fields where we need to urgently operate are climate-aware financial invest... | ๐ As we all know, Planet Earth is undergoing an unprecedented climate crisis, almost totally due to human activities: we haven't got much time left before it's too late to take action, and one of the key fields where we need to urgently operate are climate-aware financial investments...
๐ค ... And that's where AI comes into the play: we can indeed try to leverage, tweak and expand its knowledge in the field to extract valuable climate-aware solutions.
๐ค I tried to make something alike: exploiting https://huggingface.co/datasets/climatebert/tcfd_recommendations as knowledge base, Qdrant Cloud as vector store service and https://huggingface.co/microsoft/Phi-3-mini-128k-instruct as LLM (provided via API from https://huggingface.co/spaces/eswardivi/Phi-3-mini-128k-instruct by @eswardivi), I built an AI assistant to help you find climate-oriented solutions for your investments, companies, or simply for your everyday life๐.
Find it here: https://huggingface.co/spaces/as-cle-bert/cLLiMateChat
GitHub: https://github.com/AstraBert/qdrant-ai-chat
Website: https://astrabert.github.io/qdrant-ai-chat/
Be kind to our Planet, we only got one๐
(Shout-outs to @JohnSmith9982 whose https://huggingface.co/spaces/JohnSmith9982/small_and_pretty Gradio theme was used to build my application๐)
PS: ๐ฑCurious of knowing what is your carbon footprint? Head over to this ML-backed HF Space I built to discover it: https://huggingface.co/spaces/as-cle-bert/carbon-footprint-predictor | {
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"value": "THUDM has released GLM-4V-9B and it's.. chatty! ๐ ",
"raw": "THUDM has released GLM-4V-9B and it's.. chatty! ๐ ",
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"type": "new_line"... | THUDM has released GLM-4V-9B and it's.. chatty! ๐
I asked it to describe my favorite Howl's Moving Castle scene and here's how it went ๐๐ป
joke aside it seems to outperform the previous VLMs. however the license isn't open-source ๐
model repo: https://huggingface.co/THUDM/glm-4v-9b
a community member has built a demo: https://huggingface.co/spaces/vilarin/VL-Chatbox | {
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760542103453473 | [
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"val... | I decided to play around with FluentlyXL v4 ๐
๐ Model: https://huggingface.co/fluently/Fluently-XL-v4
โจ Playground: https://huggingface.co/spaces/fluently/Fluently-Playground | {
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"value": "๐ข The most recent Mistral-7B-Instruct-v0.3 release showcases more robust advances in zero-shot-mode mode on Target Sentiment Analysis.",
"raw": "๐ข The most recent Mistral-7B-Instruct-v0.3 release showcases more robust advances in zero-shot-mode mode on Target Sentiment Anal... | ๐ข The most recent Mistral-7B-Instruct-v0.3 release showcases more robust advances in zero-shot-mode mode on Target Sentiment Analysis.
๐งช We experiment with the original texts (๐ท๐บ ) and their translated version into English (๐บ๐ธ).
๐ก The key takeaways on the expectation from this model are as follows:
โ๏ธ 1. On translated texts into English (๐บ๐ธ), it surpasses LLaMA-3 and and nearly touches MOE Mixtral 8x7B versions being quite precise by F1 across all the classes by F1(PN)
โ๏ธ2. On orignal texts (๐ท๐บ) It slightly surpasses LLaMA-3 by F1(PN) by being less tolerant in neutral (F1(PN0)). Using larger versions (Mixtral) are still preferable choice for reasoning ๐ง in non-eng texts.
โ๏ธ3. You can clearly see the difference between 7B version and MOE (figure 3) by F1(PN0)
Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
Model: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3
Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation
Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)
Collection: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101 | {
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"value": "A great vision language benchmark: MM-UPD evaluates how model responds to unsolvable problems ๐ค ",
"raw": "A great vision language benchmark: MM-UPD evaluates how model responds to unsolvable problems ๐ค ",
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"code":... | A great vision language benchmark: MM-UPD evaluates how model responds to unsolvable problems ๐ค
LLaVA 1.6 is outperforming proprietary VLMs, making it a very robust choice for production!
It is now hosted as a leaderboard https://huggingface.co/spaces/MM-UPD/MM-UPD_Leaderboard ๐๐ | {
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295094188881098 | [
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"r... | Hello, Vision World!
https://huggingface.co/AI4Chem/ChemLLM-20B-Chat-DPO
https://huggingface.co/papers/2402.06852
| {
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"raw": "Can anyone see my post on๐ค?",
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"hre... | Can anyone see my post on๐ค?
Reply with ๐ค | {
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"type": "text",
"value": "๐ง Have you ever heard of neurons running as a computer?",
"raw": "๐ง Have you ever heard of neurons running as a computer?",
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"type": ... | ๐ง Have you ever heard of neurons running as a computer?
๐ป If you are curious about the so-called "brainoware", a hardware built upon a brain organoid and used for AI and ML tasks, you may want to read my latest ๐ค article: https://huggingface.co/blog/as-cle-bert/brain-next-generation-neurons
๐กEnjoy! | {
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654448211400768 | [
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"type": "text",
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"resour... | Remember when @Microsoft released Phi-3 models... ๐ค
Yup, the ones that had ๐ฆLlama 3 8B beat on MMLU using 3.8B parameters! ๐
Now they are on the LMSYS Chatbot Arena Leaderboard! ๐๐
Medium(14B) ranks near GPT-3.5-Turbo-0613, but behind Llama 3 8B. ๐
Phi-3 Small(7B) is close to Llama-2-70B, and Mistral fine-tunes. ๐
What about the Phi-3 Mini(3.8B), that was giving Llama 3 8B a run for its money on MMLU? It gets an arena score of 1037 (#73) against 1153 (#22) of Llama 3 8B ๐คผ
Looks like there is a struggle here between perplexity and inherent knowledge! ๐ค
And Microsoft picked knowledge with high perplexity ๐ง
Now I am even more intrigued: what is @Meta feeding its ๐ฆ Llamas?๐พ
๐ Leaderboard: https://chat.lmsys.org/?leaderboard | {
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"value": "One shot evaluations is hard. That is honestly what I learnt throughout the last couple of weeks trying to make imgsys.org data more and more relevant. There is just so much diversity in these models that saying one is better than other one even at a particular domain is impossib... | One shot evaluations is hard. That is honestly what I learnt throughout the last couple of weeks trying to make imgsys.org data more and more relevant. There is just so much diversity in these models that saying one is better than other one even at a particular domain is impossible.
If you have any suggestions on how we can make the testing easier for one shot, single question image model testing; please give your suggestions under this thread so we can provide a more meaningful data point to the community! | {
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449913250763619 | [
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"value": "> We introduced a new model designed for the Code generation task. Its test accuracy on the HumanEval base dataset surpasses that of GPT-4 Turbo (April 2024). (90.9% vs 90.2%).",
"raw": "> We introduced a new model designed for the Code generation task. Its test accuracy on t... | > We introduced a new model designed for the Code generation task. Its test accuracy on the HumanEval base dataset surpasses that of GPT-4 Turbo (April 2024). (90.9% vs 90.2%).
@Bin12345 interested in a ZeroGPU Spaces for https://huggingface.co/Bin12345/AutoCoder | {
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"raw": "hf-daily-paper-newsletter-chinese๏ผUsing glm-4 agents, interpret hugging face's daily papers in simplified Chinese",
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"resource": nu... | hf-daily-paper-newsletter-chinese๏ผUsing glm-4 agents, interpret hugging face's daily papers in simplified Chinese
https://github.com/2404589803/hf-daily-paper-newsletter-chinese | {
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"value": "Crazy to see that my GroundingDino contribution to the transformers library got roughly 600k downloads combined in the checkpoints in the last month ๐คฏ ",
"raw": "Crazy to see that my GroundingDino contribution to the transformers library got roughly 600k downloads combined ... | Crazy to see that my GroundingDino contribution to the transformers library got roughly 600k downloads combined in the checkpoints in the last month ๐คฏ
The tiny checkpoint got almost 500k alone https://huggingface.co/IDEA-Research/grounding-dino-tiny | {
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"value": "๐ Hello! FluentlyXL is now on Venice.ai, you can try the model there right now.",
"raw": "๐ Hello! FluentlyXL is now on Venice.ai, you can try the model there right now.",
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"labe... | ๐ Hello! FluentlyXL is now on Venice.ai, you can try the model there right now.
๐ Venice.ai: https://venice.ai
โจ๏ธ FluentlyXL v4: https://huggingface.co/fluently/Fluently-XL-v4 | {
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... | 2024-06-04T13:33:26.000Z | 2024-06-05T17:07:25.128Z | [
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866788930457283 | [
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"type": "text",
"value": "โ๏ธ Uncensor any LLM with abliteration",
"raw": "โ๏ธ Uncensor any LLM with abliteration",
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"ra... | โ๏ธ Uncensor any LLM with abliteration
I wrote an article about abliteration and how NeuralDaredevil-8B was created. Beyond removing alignment, I believe it's an interesting technique with a lot of potential. It's basically fine-tuning without retraining.
In this article, we see how it works, implement it in Google Colab, and heal the abliterated model to recover the performance drop due to this technique. The final model is an uncensored and high-quality model with the highest MMLU score on the Open LLM Leaderboard (8B category).
https://huggingface.co/blog/mlabonne/abliteration | {
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... | /posts/mlabonne/866788930457283 | 16,277 | 24 |
417417709655682 | [
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"type": "text",
"value": "It is with great pleasure I inform you that huggingface's ModelHubMixin reached 200+ models on the hub ๐ฅณ",
"raw": "It is with great pleasure I inform you that huggingface's ModelHubMixin reached 200+ models on the hub ๐ฅณ",
"href": null,
"resource": null,
"url": n... | It is with great pleasure I inform you that huggingface's ModelHubMixin reached 200+ models on the hub ๐ฅณ
ModelHubMixin is a class developed by HF to integrate AI models with the hub with ease and it comes with 3 methods :
* save_pretrained
* from_pretrained
* push_to_hub
Shoutout to @nielsr , @Wauplin and everyone else on HF for their awesome work ๐ค
If you are not familiar with ModelHubMixin and you are looking for extra resources you might consider :
* docs: https://huggingface.co/docs/huggingface_hub/main/en/package_reference/mixins
๐blog about training models with the trainer API and using ModelHubMixin: https://huggingface.co/blog/not-lain/trainer-api-and-mixin-classes
๐GitHub repo with pip integration: https://github.com/not-lain/PyTorchModelHubMixin-template
๐basic guide: https://huggingface.co/posts/not-lain/884273241241808
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... | 2024-06-04T12:52:02.000Z | 2024-06-04T12:52:02.764Z | [] | /posts/not-lain/417417709655682 | 2,065 | 0 |
183035058595618 | [
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"value": " unprompted feature request: I'd love to have a toggle for a HF collection... | @victor unprompted feature request: I'd love to have a toggle for a HF collection to control whether new items are added to the top or to the bottom. At the moment everything gets added at the bottom, but it would be great to have newer elements on top to make fresh content easily accessible without having to scroll all the way! | {
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... | /posts/gsarti/183035058595618 | 1,546 | 3 |
615386996098522 | [
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"value": "New research model out ! I uploaded a new Branchy model based on Phi-2 for faster inference using Early Exit. Check it out : ",
"raw": "New research model out ! I uploaded a new Branchy model based on Phi-2 for faster inference using Early Exit. Check it out : ",
"href": ... | New research model out ! I uploaded a new Branchy model based on Phi-2 for faster inference using Early Exit. Check it out : https://huggingface.co/valcore/Branchy-Phi-2.
I also uploaded a Hugging Face Space to try it out : https://huggingface.co/spaces/valcore/Branchy-phi-2, unfortunately inference is very slow on free tier. Let me know what you are thinking about it ! | {
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892134435688447 | [
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"type": "text",
"value": "We are super happy to contribute to the GLiNER ecosystem by optimizing training code and releasing a multi-task, prompt-tunable model.",
"raw": "We are super happy to contribute to the GLiNER ecosystem by optimizing training code and releasing a multi-task, prompt-tunable mod... | We are super happy to contribute to the GLiNER ecosystem by optimizing training code and releasing a multi-task, prompt-tunable model.
The model can be used for the following tasks:
* Named entity recognition (NER);
* Open information extraction;
* Question answering;
* Relation extraction;
* Summarization;
Model: https://huggingface.co/knowledgator/gliner-multitask-large-v0.5
Demo: https://huggingface.co/spaces/knowledgator/GLiNER_HandyLab
Repo: ๐จโ๐ป https://github.com/urchade/GLiNER
**How to use**
First of all, install gliner package.
```bash
pip install gliner
```
Then try the following code:
```python
from gliner import GLiNER
model = GLiNER.from_pretrained("knowledgator/gliner_small-v2.1")
prompt = """Find all positive aspects about the product:\n"""
text = """
I recently purchased the Sony WH-1000XM4 Wireless Noise-Canceling Headphones from Amazon and I must say, I'm thoroughly impressed. The package arrived in New York within 2 days, thanks to Amazon Prime's expedited shipping.
The headphones themselves are remarkable. The noise-canceling feature works like a charm in the bustling city environment, and the 30-hour battery life means I don't have to charge them every day. Connecting them to my Samsung Galaxy S21 was a breeze, and the sound quality is second to none.
I also appreciated the customer service from Amazon when I had a question about the warranty. They responded within an hour and provided all the information I needed.
However, the headphones did not come with a hard case, which was listed in the product description. I contacted Amazon, and they offered a 10% discount on my next purchase as an apology.
Overall, I'd give these headphones a 4.5/5 rating and highly recommend them to anyone looking for top-notch quality in both product and service.
"""
input_ = prompt+text
labels = ["match"]
matches = model.predict_entities(input_, labels)
for match in matches:
print(match["text"], "=>", match["score"])
```
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379847066413347 | [
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"value... | my ๐คhuggingface activity for 2024 so far ...
dont tell my boss...
check yours too now, it's fun ๐ค | {
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280895787141155 | [
{
"type": "text",
"value": "Iโve been working on a crazy theory for my first solo paper and I would appreciate some advice from leading researchers here:)",
"raw": "Iโve been working on a crazy theory for my first solo paper and I would appreciate some advice from leading researchers here:)",
"href"... | Iโve been working on a crazy theory for my first solo paper and I would appreciate some advice from leading researchers here:)
"Theory of Adaptive Learning"
Of all the deep learning algorithms at least to my knowledge, thereโs none that fully covers the adaptive nature of intelligence. I believe it is a fundamental missing component of current AI governing laws.
I define it as a kind of learning wherein one person (say a student) adapts their framework of understanding to better suit that of what is being taught or said by another person/model (say a teacher). If we could measure the nature of this transfer learning. I believe it could help improve planning and reasoning capabilities of AI systems. If we look back at the theory evolution, adaptation is a fundamental component of human evolution. Today's so-called groundbreaking architectures or models, specifically large language models tend to have static parameters with constraints that are almost impossible to change or update in real-time after training. This fundamentally hinders their ability to reason, plan and accomplish objective-driven tasks as we humans do. Intelligence is dynamic.
Now this cannot be done with current autoregressive llms as their parameters are fixed with static constraints, even though RAG do help in updating model parameters in real-time but its basically cheating and doesnโt count as intelligence. Thereโs a pressing need for a natively adaptive architecture - The Goal of This Paper
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435428954049091 | [
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"type": "text",
"value": "hello everyone, I've finished making a project for RVC Dataset Maker if you want to try the project you can try it below:",
"raw": "hello everyone, I've finished making a project for RVC Dataset Maker if you want to try the project you can try it below:",
"href": null,
... | hello everyone, I've finished making a project for RVC Dataset Maker if you want to try the project you can try it below:
https://huggingface.co/spaces/Hev832/RVC-Dataset-Maker
if you find any erors you can create New discussion ๐
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185154926901932 | [
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"raw": "Hi ",
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"url":... | Hi @coyotte508 , if you've got a minute, could you take a look at this huggingface.js discussion?
https://github.com/huggingface/huggingface.js/discussions/735 | {
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"raw": "Just published a new article ๐",
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... | Just published a new article ๐
https://huggingface.co/blog/santiviquez/data-drift-estimate-model-performance
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{
"type": "text",
"value": "๐ข Impressed with the application of the microsoft/Phi-3-mini-4k-instruct (3B) performance in zero-shot-learning (ZSL) mode reasoning ๐ง on Target Sentiment Analysis (TSA) problem. ",
"raw": "๐ข Impressed with the application of the microsoft/Phi-3-mini-4k-instruct (3B) perfo... | ๐ข Impressed with the application of the microsoft/Phi-3-mini-4k-instruct (3B) performance in zero-shot-learning (ZSL) mode reasoning ๐ง on Target Sentiment Analysis (TSA) problem.
๐ก There are three major takeaways out of this experiment ๐งช and they are as follows:
โ
1. Phi-3 slightly outperforms Mistral-7B (official Mistral API, v0.1 or v0.2) on texts written in English
โ
2. Performs similar to LLaMA-3-8B-Instruct on texts translated in English ๐ฅ
โ๏ธ 3. Reasoning in non-english language (๐ท๐บ) is pretty decent but underperforms to the similar 7B sized models.
This is a huge step forward since release of Phi-2, especially because the predecessor (microsoft/phi-2) was not capable for performing reasoning in non-english texts (๐ท๐บ) at all!
Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
Model: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct
Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation
Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)
Collection: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101
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"value": "By popular demand, HF activity tracker v1.0 is here! ๐ let's build it together!๐ค",
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"... | By popular demand, HF activity tracker v1.0 is here! ๐ let's build it together!๐ค
Lots of things to improve, feel free to open PRs in the community tab!
good PR ideas:
- track more types of actions that include date+time
- bigger plot
- track discord activity too ๐คฏ
- link github? โก
https://huggingface.co/spaces/huggingface-projects/LevelBot | {
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... | 2024-06-03T19:08:20.000Z | 2024-06-04T07:57:01.633Z | [
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... | /posts/lunarflu/239147617114976 | 2,309 | 2 |
176796779268748 | [
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"type": "text",
"value": "\"Hold your pixels\" ๐ฆ... SD3 is here ๐",
"raw": "\"Hold your pixels\" ๐ฆ... SD3 is here ๐",
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"value": null,... | "Hold your pixels" ๐ฆ... SD3 is here ๐
๐ Performance Enhancements: Stable Diffusion 3 surpasses other text-to-image models like DALLยทE 3 in typography and prompt adherence.
๐๏ธ New Architecture: Introduces the Multimodal Diffusion Transformer (MMDiT) that separately processes image and language data, enhancing text understanding and spelling.
โก Efficiency Improvements: Features a rectified flow formulation for more efficient image generation, fitting within the memory constraints of common GPUs.
๐ Scalability: Demonstrates scaling capabilities with models ranging up to 8 billion parameters, showing improvements in model performance without saturation.
๐ง Flexible Text Encoders: Offers a flexible approach to text encoding, maintaining performance even when the largest T5 text encoder is removed for less memory-intensive operations.
While they discuss experiments on 2B and 8B parameter models, no word on open weights ๐ค
Paper: https://huggingface.co/papers/2403.03206
@StabilityAI | {
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"hr... | Impressed by the work of @guipenedo @hynky @loubnabnl @anton-l @craffel @lvwerra @thomwolf on FineWeb.
LLMs are only as good as the data they have been trained on, but the crucial aspect of pretraining data remains obscure. Our approach lifts the veil on building high-quality pretraining datasets by sharing every detail about this process to enable a wider community to build on top of it.
- The FineWeb-Edu dataset, which outperforms all openly accessible web datasets in a number of educational benchmarks. We built it by developing a quality classifier using annotations generated by an LLM.
- A new technical report explaining in detail how to create a large and high-quality web-scale dataset for LLM pretraining such as FineWeb
๐ https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1
| {
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"raw": "\... | OpenGPT 4o now features WEB SEARCH
This feature enhances the capabilities of OpenGPT 4o, allowing it to fetch and integrate the latest information from the web directly into its responses.
Try Now: https://huggingface.co/spaces/KingNish/OpenGPT-4o
With WEB SEARCH, OpenGPT 4o becomes an even more versatile and dynamic AI, ready to assist with up-to-date data retrieval and analysis. | {
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"avatarUrl"... | /posts/KingNish/649401989400810 | 15,117 | 29 |
727834678466445 | [
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"type": "text",
"value": "There are very few of us left on Hugging Face as the market outgrows it's Ideals and evolves the Idealists will be left without industry.",
"raw": "There are very few of us left on Hugging Face as the market outgrows it's Ideals and evolves the Idealists will be left without ... | There are very few of us left on Hugging Face as the market outgrows it's Ideals and evolves the Idealists will be left without industry.
Setting up a Round Table for those interested in giving input and working on solving the issue of procuring the fastest dispersion to market saturation of retrievable intelligence, so finally Private and fully emancipated Intelligence will be considered "In Common Use."
If you've ever thought,"They will not let us have this much power for much longer.."
You are right and it IS being hidden from the masses, as every breakthrough is another step towards equality, those who rely on exploitation will destroy freedom simply because they fear being uncomfortable. We know discomfort, hunger, pain. It is the Human condition.
Without it we would never move forward and the Norms in society today are the regression of our species.
If we do not use this chance to become better, all of us, collectively, nothing will change.
Nothing fancy, intricate or diabolical, Just giving equal opportunity to the people. Not opportunity, in one direction, behind a paywall but true choice..
Too long have the powerful hidden behind the guise of "Democracy," I say ,democracy is two wolves and a Cat voting on what to have for dinner. Liberty, in this analog, is a well informed and armed Cat asking politely for a second option, while gripping tightly his armaments with his Fuzzy Mittens!
Please if interested in even listening, email for an invite @ intelligentestate@gmail.com
Looking for People smarter then myself to Found and cooperate in this organization. | {
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886392835712113 | [
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"value": "Been reading about the \"bigger models = better AI\" narrative getting pushed back today.",
"raw": "Been reading about the \"bigger models = better AI\" narrative getting pushed back today.",
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"user... | Been reading about the "bigger models = better AI" narrative getting pushed back today.
@thomwolf tackled this head on at Web Summit and highlighted how important small models are (and why closed-source companies haven't pushed for this ๐ฌ). They're crushing it: today's 1B parameter models outperform last year's 10B models.
Fascinating to hear him talk about the secret sauce behind this approach. | {
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"... | I was curious to see what people post here on HF so I created a dataset with all HF Posts: https://huggingface.co/datasets/maxiw/hf-posts
Some interesting stats:
Top 5 Authors by Total Impressions:
-----------------------------------
@merve: 171,783 impressions (68 posts)
@fdaudens: 135,253 impressions (81 posts)
@singhsidhukuldeep: 122,591 impressions (81 posts)
@akhaliq: 119,526 impressions (78 posts)
@MonsterMMORPG: 112,500 impressions (45 posts)
Top 5 Users by Number of Reactions Given:
----------------------------------------
@osanseviero: 1278 reactions
@clem: 910 reactions
@John6666: 899 reactions
@victor: 674 reactions
@samusenps: 655 reactions
Top 5 Most Used Reactions:
-------------------------
โค๏ธ: 7048 times
๐ฅ: 5921 times
๐: 4856 times
๐: 2549 times
๐ค: 2065 times | {
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... | /posts/maxiw/833289193510507 | 4,532 | 10 |
600410046749191 | [
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"type": "text",
"value": "Dear developers and AI enthusiasts, first I apologize for making inappropriate content here. ",
"raw": "Dear developers and AI enthusiasts, first I apologize for making inappropriate content here. ",
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... | Dear developers and AI enthusiasts, first I apologize for making inappropriate content here.
Second, I don't want to make any form of scandal or drama here, since HF must be a clean and drama-free community.
A few days ago, I made a post and asked for grants or supports you may have received. You can find it here (https://huggingface.co/posts/Muhammadreza/982596282424877). Then user @wisesniper came and linked to their own space which apparently is a cloud mining service.
Although I wanted it to be "not true" but I have to say this person is a scammer. I fell for the scam hand spent around $10 (USD) in form of TRX tokens.
Please be cautious. These days, cryptocurrencies are making trends since most of the markets are in a good shape. A good market calls for scammers.
Regards. | {
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... | /posts/Muhammadreza/600410046749191 | 408 | 1 |
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"value": "I'm in Belo Horizonte for a few days. Any HF community members in Brazil? What are the coolest orgs, models, datasets, spaces from here?",
"raw": "I'm in Belo Horizonte for a few days. Any HF community members in Brazil? What are the coolest orgs, models, datasets, spaces fro... | I'm in Belo Horizonte for a few days. Any HF community members in Brazil? What are the coolest orgs, models, datasets, spaces from here? | {
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"value": "Hey, guys. Kinda new to local LLMs. Sorry if this has been asked before, but I'm also new to HF!",
"raw": "Hey, guys. Kinda new to local LLMs. Sorry if this has been asked before, but I'm also new to HF!",
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"code": n... | Hey, guys. Kinda new to local LLMs. Sorry if this has been asked before, but I'm also new to HF!
Which local LLMs can access the internet, or how can I have my local LLM(s) access the internet?
Thank you in advance and apologies if I'm breaking any rules! | {
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... | /posts/alex899/923864541962476 | 556 | 2 |
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"value": "RWKV-6-world-v3 (+3.1T tokens) is our best multilingual 7B model as of now: ",
"raw": "RWKV-6-world-v3 (+3.1T tokens) is our best multilingual 7B model as of now: ",
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"label": null... | RWKV-6-world-v3 (+3.1T tokens) is our best multilingual 7B model as of now: https://huggingface.co/BlinkDL/rwkv-6-world
It's 100% RNN and attention-free. MMLU 54.2% (previous world-v2.1 = 47.9%. note: without eval-boosting tricks such as annealing).
RWKV-7-world-v4 soon :) | {
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"value": "Funding realism back into the world. ๐บ",
"raw": "Funding realism back into the world. ๐บ",
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... | Funding realism back into the world. ๐บ
๐ฅStranger Zone's : Super Realism [ https://huggingface.co/strangerzonehf/Flux-Super-Realism-LoRA ]
Demo1 : https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC
Demo2 : https://huggingface.co/spaces/prithivMLmods/FLUX-REALISM
Other New adapt.s for dev ๐ [ updated patch 2 ]
Hosted -> https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC
๐Digital Chaos: https://huggingface.co/prithivMLmods/Digital-Chaos-Flux-LoRA
๐Threaded Knitted: https://huggingface.co/prithivMLmods/Knitted-Character-Flux-LoRA
๐Fashion Hut: https://huggingface.co/prithivMLmods/Fashion-Hut-Modeling-LoRA
๐Aura 9999: https://huggingface.co/prithivMLmods/Aura-9999
๐Green Cartoon: https://huggingface.co/prithivMLmods/Green-Cartoon-Flux-LoRA
๐Pastels: https://huggingface.co/prithivMLmods/Pastel-BG-Flux-LoRA
๐Retro Pixel: https://huggingface.co/prithivMLmods/Retro-Pixel-Flux-LoRA
๐CAnime: https://huggingface.co/prithivMLmods/CAnime-LoRA
๐Pastels: https://huggingface.co/prithivMLmods/Pastel-BG-Flux-LoRA
------------
๐LoRA Collection: https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
๐LoRA Spaces: https://huggingface.co/collections/prithivMLmods/lora-space-collections-6714b72e0d49e1c97fbd6a32
๐Collection Zero:
https://huggingface.co/collections/prithivMLmods/collection-zero-and-demo-recently-updated-65e48a7dd8212873836ceca2
------------
.
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... | 2024-11-13T06:39:41.000Z | 2024-11-13T09:27:56.422Z | [] | /posts/prithivMLmods/330576556286160 | 3,219 | 0 |
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"value": "Interesting Work on Reasoning ๐ค",
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... | Interesting Work on Reasoning ๐ค
- explores a new take on few-shot reasoning while challenging assumptions that program synthesis is necessary for abstract reasoning.
- shows test-time training + smart inference tricks can match human-average performance, though at high computational cost. Key insight: proper compute allocation matters more than method (whether symbolic or neural).
Paper: https://ekinakyurek.github.io/papers/ttt.pdf
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"value": "Sentence Transformers received huge updates today! Do you like giving your model access to web search and document search? That's Sentence Transformers. Hugging Face makes it beyond easy to add this functionality to any model. You can be up and running with Sentence Transformers ... | Sentence Transformers received huge updates today! Do you like giving your model access to web search and document search? That's Sentence Transformers. Hugging Face makes it beyond easy to add this functionality to any model. You can be up and running with Sentence Transformers in seconds. Check out this video for a deeper explanation and sample code: https://youtu.be/2hR3D8_kqZE | {
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"... | 2024-11-12T19:29:08.000Z | 2024-11-12T19:29:08.143Z | [] | /posts/TuringsSolutions/625621331775578 | 3,099 | 0 |
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"value": "Wikimedia and Hugging Face seem kind of naturally complementary: Both are community-centred, value openness and consent. That's why I'd love to see more Wikipedia and other Wikimedia projects' datasets on Hugging Face to advance machine learning with diverse, community-curated da... | Wikimedia and Hugging Face seem kind of naturally complementary: Both are community-centred, value openness and consent. That's why I'd love to see more Wikipedia and other Wikimedia projects' datasets on Hugging Face to advance machine learning with diverse, community-curated data! See my new article on the Hugging Face hub for why and how to create more Wikimedia datasets on Hugging Face: https://huggingface.co/blog/frimelle/wikipedias-treasure-trove-ml-data
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"raw": "Hey Guys !! ๐ง",
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"resource": nu... | Hey Guys !! ๐ง
This is the time to Share the Collection of Prompts which have high parametric details to produce the most detailed flawless images.
๐You can watch out the Collection on: https://huggingface.co/spaces/prithivMLmods/Top-Prompt-Collection
๐ขMore than 200+ High Detailed prompts have been used in the Spaces.
@prithivMLmods
Thank you for the read. !!
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"lan... | Fluently XL v4 took 4th place in the arena leaderboard imgsys.org, yay!
Model: https://huggingface.co/fluently/Fluently-XL-v4
Playground with this model: https://huggingface.co/spaces/fluently/Fluently-Playground
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... | Grand Thief Auto style
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"raw": "https:/... | we are very proud to introduce https://huggingface.co/jinaai/jina-clip-v1, aka "jina-embeddings-multimodal".
The OpenAI CLIP https://huggingface.co/openai/clip-vit-base-patch32 have nice performance to align text and image modality, that user can perform cross-modal text image retrieval or image classification on top of it. However, due to the training data and recipe, it can not:
1. model longer sequence of text inputs (77 token constraint).
2. align text representations (CLIP Text Tower is weak for text search).
In our latest publication, https://huggingface.co/papers/2405.20204 , we proposed a multi-task, multi-objective learning scheme. The produced CLIP model shows:
1. Stronger cross-modal performance against OpenAI sets, 2% and 6% improvement on cross-modal retrieval recall@5.
2. Text tower of the JinaCLIP is a strong text encoder, reach the same performance as https://huggingface.co/jinaai/jina-embeddings-v2-base-en, 165% improvement on MTEB[BEIR] recall@5.
3. Image tower of the JinaCLIP also shows strong performance in image-image search (CBIR), 12% recall improvement on Cifar100 test set.
If you are working on MuRAG (multimodal-retrieval argumented generation), try it out!
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"value": "Proof that ablative educational dataset significantly enhances model capabilities (independent of model parameters or architecture) ๐คฉ",
"raw": "Proof that ablative educational dataset significantly enhances model capabilities (independent of model parameters or architecture)... | Proof that ablative educational dataset significantly enhances model capabilities (independent of model parameters or architecture) ๐คฉ
Yesterday, FineWebโs technical report was published. FYI FineWeb (by ๐ค) is currently the best opensource text dataset that can scale up model performance up to that of GPT-3 level.
While proprietary datasets used in training models like GPT-4/Claude/LlaMA are crawled internally and never released, FineWeb builds on CommonCrawl (an open repo for crawled web data). They preprocessed the data using their custom built data preprocessing library datatrove (which they also opensourced), and then evaluate the data quality on lighteval by training small sized models โablation modelsโ using nanotron (a library for pretraining transformer models).
Of all versions of FineWeb, FineWeb-Edu outperforms all other subsets. This is thanks to a new filtering technique wherein they used synthetic data to develop classifiers for identifying educational contents.
Turned out โEducation is All You Needโ:) | {
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"value": "Rope is the newest 1-Click, most easy to use, most advanced open source Deep Fake application. It has been just published few days ago. In below tutorials I show how to use Rope Pearl DeepFake application both on Windows and on a cloud machine (Massed Compute). Rope is way better... | Rope is the newest 1-Click, most easy to use, most advanced open source Deep Fake application. It has been just published few days ago. In below tutorials I show how to use Rope Pearl DeepFake application both on Windows and on a cloud machine (Massed Compute). Rope is way better than Roop, Roop Unleashed and FaceFusion. It supports multi-face Face Swapping and making amazing DeepFake videos so easily with 1-Click. Select video, select faces and generate your DeepFake 4K ultra-HD video.
1-Click Rope Installers Scripts (contains both Windows into an isolated Python VENV and Massed Compute โ Cloud โ No GPU)โคต๏ธ
https://www.patreon.com/posts/most-advanced-1-105123768
Tutorials are made only for educational purposes. On cloud Massed Compute machine, you can run with staggering 20 threads and can FaceSwap entire movies. Fully supports face tracking and multiple face changes.
Mind-Blowing Deepfake Tutorial: Turn Anyone into Your Fav Movie Star! Better than Roop & Face Fusion โคต๏ธ
https://youtu.be/RdWKOUlenaY
Best Deepfake Open Source App ROPE โ So Easy To Use Full HD Feceswap DeepFace, No GPU Required Cloud โคต๏ธ
https://youtu.be/HLWLSszHwEc
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"value": "huggingface, SakanaAILabs and @arcee_ai are sponsoring a Model Merging Competition with really sweet ๐ฐcash prizes๐ฐ at the 2024 NeurIPSConf! (",
"raw": "huggingface, SakanaAILabs and @arcee_ai are sponsoring a Model Merging Competition with really sweet ๐ฐcash prizes๐ฐ at th... | huggingface, SakanaAILabs and @arcee_ai are sponsoring a Model Merging Competition with really sweet ๐ฐcash prizes๐ฐ at the 2024 NeurIPSConf! (https://neurips.cc) ๐
Submissions are now open and will remain open until September 2024. ๐
๐ Register here: https://llm-merging.github.io/
๐ฃ๏ธ Join the Discord discussion: https://discord.com/invite/dPBHEVnV
| {
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... | hi everyone!
i wanted to share an experiment i did with upcycling phi-3 mini into an moe recently.
while benchmarks are definitely within a margin of error and they performed similarly, i think it's an interesting base to try and see if you can improve phi's performance! (maybe looking into https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu could be interesting, i also left some other notes if anyone with more compute access wants to try it themselves)
check it out! https://huggingface.co/Fizzarolli/phi3-4x4b-v1 | {
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"type": "text",
"value": "Haloooo, continue experimenting with a checkpoint version of Ghost Beta (small version) during training in stage 1 (trained progress: 41%). ",
"raw": "Haloooo, continue experimenting with a checkpoint version of Ghost Beta (small version) during training in stage 1 (trained p... | Haloooo, continue experimenting with a checkpoint version of Ghost Beta (small version) during training in stage 1 (trained progress: 41%).
Supported languages: ๐บ๐ธ English, ๐ช๐ธ Spanish, ๐ต๐น Portuguese, ๐ซ๐ท French, ๐ฎ๐น Italian, ๐ฉ๐ช German, ๐ป๐ณ Vietnamese, ๐ฐ๐ท Korean, ๐จ๐ณ Chinese, and !?
Note that this is not a conclusion, this is just a sharing of the state of the model. If you find it interesting, please follow the project at:
* https://x.com/ghostx_ai
* https://ghost-x.org/
* https://huggingface.co/ghost-x
Ghost X is currently very open to invitations to cooperate, share and support.
๐คฏ๐ | {
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"value": "๐ท FineWeb technical report is out and so is ๐ FineWeb-Edu, a 1.3 trillion tokens dataset that outperforms all other open web datasets, with remarkable improvements on educational benchmarksย such as MMLU, ARC, and OpenBookQA.",
"raw": "๐ท FineWeb technical report is out and ... | ๐ท FineWeb technical report is out and so is ๐ FineWeb-Edu, a 1.3 trillion tokens dataset that outperforms all other open web datasets, with remarkable improvements on educational benchmarksย such as MMLU, ARC, and OpenBookQA.
Technical report: https://hf.co/spaces/HuggingFaceFW/blogpost-fineweb-v1
Dataset: https://hf.co/datasets/HuggingFaceFW/fineweb-edu
We used Llama 3 generations to train an educational quality classifier, filtering the 15 trillion tokens of FineWeb to select only those with high educational value (an approach also used in Llama 3 and Phi-3 training datasets). We're releasing both FineWeb-Edu and the classifier, along with a larger, less heavily filtered version containing 5.4 trillion tokens.
You can find more details about the dataset and the experiments we ran in the FineWeb technical report, It's a 45-minute read but it contains all the secret sauce for building high quality web datasets.
Enjoy! | {
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"value": "[New crazy blog post alert] We are releasing an extensive blog post on the science of creating high quality web-scale datasets, detailing all the steps and learnings that came in our recent 15 trillion tokens ๐ทFineWeb release",
"raw": "[New crazy blog post alert] We are rele... | [New crazy blog post alert] We are releasing an extensive blog post on the science of creating high quality web-scale datasets, detailing all the steps and learnings that came in our recent 15 trillion tokens ๐ทFineWeb release
Inspired by the distill.pub interactive graphics papers, we settled to write the most extensive, enjoyable and in-depth tech report we could draft on so prepare for a 45-mmin read with interactive graphics and all.
And it's not all, in this article we also introduce ๐FineWeb-Edu a filtered subset of Common Crawl with 1.3T tokens containing only web pages with very high educational content. Up to our knowledge, FineWeb-Edu out-performs all openly release web-scale datasets by a significant margin on knowledge- and reasoning-intensive benchmarks like MMLU, ARC, and OpenBookQA
We also make a number of surprising observations on the "quality" of the internet it-self which may challenge some of the general assumptions on web data (not saying more, I'll let you draw your conclusions ;)
https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1 | {
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"type": "text",
"value": "๐ฅ 77.2% on MMLU with 3.7B parameters ๐",
"raw": "๐ฅ 77.2% on MMLU with 3.7B parameters ๐",
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... | ๐ฅ 77.2% on MMLU with 3.7B parameters ๐
... 3.7B active parameters, 40B in total parameters ๐
7.4 GFlops forward computation per token, 1/19 of Llama3-70B ๐
Exciting enough? ๐ฒ
That's Yuan2-M32 for you, released by IEIT-Yuan.
A new 40B Mixture of Experts using a new Attention Router mechanism ๐ง
32 experts with 2 active in generation โ๏ธ
8,192 context length ๐
Trained on 2T tokens, using 9.25% of the compute required by the dense models ๐ ๏ธ.
Yuan 2.0-M32 employs fine-tuning techniques to adjust to longer sequence lengths, utilizing a modified base value in the Rotary Position Embedding to maintain performance over extended contexts ๐.
Open-source - Apache 2.0 ๐
Vocabulary size of 135,040 ๐ฃ๏ธ
Outperforms Mixtral 8x7B (47B total parameters, 12.9B active parameters) on all benchmarks and almost gives Llama 3 70B run for its money ๐ธ
Models: https://huggingface.co/IEITYuan ๐
Paper: https://huggingface.co/papers/2405.17976 ๐ | {
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"value": "The most recent LLaMA-3-70B Instruct showcases the beast performance in zero-shot-learning mode in Target-Sentiment-Analsys (TSA) ๐ฅ๐ In particular we experiment with sentence-level analysis, with sentences fetched from the WikiArticles that were formed into RuSentNE-2023 datas... | The most recent LLaMA-3-70B Instruct showcases the beast performance in zero-shot-learning mode in Target-Sentiment-Analsys (TSA) ๐ฅ๐ In particular we experiment with sentence-level analysis, with sentences fetched from the WikiArticles that were formed into RuSentNE-2023 dataset.
The key takeaways out of LLaMA-3-70B performance on original (๐ท๐บ) texts and translated into English are as follows:
1. Outperforms all ChatGPT-4 and all predecessors on non-english-texts (๐ท๐บ)
2. Surpasses all ChatGPT-3.5 / nearly performs as good as ChatGPT-4 on english texts ๐ฅณ
Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
Model: https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct
Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation
Related paper: https://huggingface.co/papers/2404.12342
Collection: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101 | {
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}... | all these GPU bourgeois tryna act cool like the GPU poor kids...
- what's your number for real ?
+ and did it work at parties for you ? | {
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"value": "Get your hands on the new, best, most-performant tiny model on huggingface. With 32k context window, you can fine-tune it on larger datasets or your preferred rag functionality.",
"raw": "Get your hands on the new, best, most-performant tiny model on huggingface. With 32k con... | Get your hands on the new, best, most-performant tiny model on huggingface. With 32k context window, you can fine-tune it on larger datasets or your preferred rag functionality.
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"value": "Introducing GlotCC: a new 2TB corpus based on an early 2024 CommonCrawl snapshot with data for 1000+ languages.",
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... | Introducing GlotCC: a new 2TB corpus based on an early 2024 CommonCrawl snapshot with data for 1000+ languages.
๐ค corpus v1: https://huggingface.co/datasets/cis-lmu/GlotCC-V1
๐ฑ pipeline v3: https://github.com/cisnlp/GlotCC
More details? Stay tuned for our upcoming paper.
More data? In the next version, we plan to include additional snapshots of CommonCrawl.
Limitation: Due to the lower frequency of low-resource languages compared to others, there are sometimes only a few sentences available for very low-resource languages. However, the data volume for English in this version stands at 750GB, and the top 200 languages still have a strong presence in our data (see plot attached; we write the index for every 20 languages, meaning the 10th index is the 200th language).
| {
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"value": "The InstructGPT paper mentions that they insert 10% pretraining data during SFT, which they find improves the effect of PPO (IIUC). Has anyone else done later ablations on this? I've only seen the inverse suggested, mixing in SFT data during pretraining.",
"raw": "The Instruc... | The InstructGPT paper mentions that they insert 10% pretraining data during SFT, which they find improves the effect of PPO (IIUC). Has anyone else done later ablations on this? I've only seen the inverse suggested, mixing in SFT data during pretraining. | {
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"type": "text",
"value": "๐บ Introducing the YouTube-Commons Dataset ๐บ",
"raw": "๐บ Introducing the YouTube-Commons Dataset ๐บ",
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"value... | ๐บ Introducing the YouTube-Commons Dataset ๐บ
๐ Overview: The YouTube Commons Dataset is a comprehensive collection of 30 billion words from 15,112,121 original and automatically translated transcripts, drawn from 2,063,066 videos on YouTube.
๐ License: All videos are shared under the CC-BY license, with the majority (71%) in English.
๐ค Applications: This dataset is ideal for training powerful AI models for converting speech to text (ASR) and translation models.
๐ Utilization: The text can be used for model training and is republishable for reproducibility purposes.
๐ค Collaboration: This dataset is the result of a collaboration between state start-up LANGU:IA, the French Ministry of Culture, and DINUM. It will be expanded in the coming months.
๐ Explore the dataset here: https://lnkd.in/d_paWKFE
#YouTubeCommons #AIResearch #MachineLearning #OpenData #ArtificialIntelligence #NLP #Dataset #TechCollaboration #Innovation #DigitalTransformation | {
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862353567134059 | [
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"type": "text",
"value": "Introducing UNA-ThePitbull Series",
"raw": "Introducing UNA-ThePitbull Series",
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"raw": "\n"... | Introducing UNA-ThePitbull Series
We are happy to announce the release of our latest model UNA-ThePitbull, the most powerful model below 70B in the industry. In this new generation, inspired on our previous Beagle series we curated a model that balance nicely EQ and IQ. It was trained with some of the latest datasets including:
* Replete-AI/code_bagel_hermes-2.5
* mlabonne/orpo-dpo-mix-40k
* jondurbin/py-dpo-v0.1
Available in the hub https://huggingface.co/fblgit/UNA-ThePitbull-21.4B-v2 and you can grab Quant versions sponsored by @bartowski at https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF fully compatible with Ollama, llama.cpp, etc.
UNA
In this case we tried something new by alternating uniformity across layers of both MLP & Attention reducing computational requirements while keep a high performant result.
We trained him under these terms:
* ThePitbull-v1 as base: SFT maxLR 1e-4 minLR 5e-5 for 1 Epoch
* DPO maxLR 1e-4 minLR 5e-5 for 1 Epoch
You can continue the training by merely using 5e-5 maxLR and 0 warmup steps, it should minimize catastrophic forgetting of the model.
Remember if you do so, please include a Pitbull picture on your model and cite :) Have fun!
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"value": "ChemLLM Multi-Modal version will coming soon!",
"raw": "ChemLLM Multi-Modal version will coming soon!",
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"value... | ChemLLM Multi-Modal version will coming soon!
Also Weights and Datasets! | {
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"type": "text",
"value": "Remember Gemini, GPT-4o, all being true multimodal models ๐.",
"raw": "Remember Gemini, GPT-4o, all being true multimodal models ๐.",
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... | Remember Gemini, GPT-4o, all being true multimodal models ๐.
Now we have a paper ๐ describing an architecture that might achieve that!
Uni-MoE: a native multimodal, Unified Mixture of Experts (MoE) architecture ๐๏ธ.
Uni-MoE integrates various modalities (text ๐, image ๐ผ๏ธ, audio ๐ต, video ๐น, speech ๐ฃ๏ธ) using modality-specific encoders and connectors for a cohesive multimodal understanding.
Training Strategy:
1๏ธโฃ Training cross-modality alignment with diverse connectors ๐.
2๏ธโฃ Training modality-specific experts using cross-modality instruction data ๐.
3๏ธโฃTuning the Uni-MoE framework with Low-Rank Adaptation (LoRA) on mixed multimodal data ๐ง.
Technical Details:
Modality-Specific Encoders: CLIP for images ๐ผ๏ธ, Whisper for speech ๐ฃ๏ธ, BEATs for audio ๐ต.
MoE-Based Blocks: Shared self-attention layers, feed-forward networks (FFN) based experts, and sparse routers for token-level expertise allocation ๐.
Efficient Training: Utilizes LoRA for fine-tuning pre-trained experts and self-attention layers ๐ ๏ธ.
Uni-MoE outperforms traditional dense models on benchmarks like A-OKVQA, OK-VQA, VQAv2, MMBench, RACE-Audio, and English High School Listening Test ๐.
The code is open-sourced as well: https://github.com/HITsz-TMG/UMOE-Scaling-Unified-Multimodal-LLMs/tree/master/Uni_MoE_v2
Paper: https://huggingface.co/papers/2405.11273 | {
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"type": "text",
"value": "#Newer / Current Version",
"raw": "#Newer / Current Version",
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"href": null... | #Newer / Current Version
๐จHuggingface APK Update v0.0.4๐จ
1. Fixed Pinch to Zoom Update .
2. Swipe Gestures.
3. Fixed Auto Rotate.
4. Updated app Indentifiers.
Download the app now !!
๐จHuggingface v0.0.4 Download,
โฌ๏ธLink : https://drive.google.com/file/d/1xEiH7LMdP14fBG-xDuSqKje5TRLV1PuS/view?usp=sharing
Like ๐Share ๐ Follow ๐
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"type": "text",
"value": "I am pleased to announce 2 amazing AI demos:",
"raw": "I am pleased to announce 2 amazing AI demos:",
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"value":... | I am pleased to announce 2 amazing AI demos:
1. Chat with Google Agent - This includes three AI models that allow you to converse with an AI, which provides answers by searching Google.
Demo Link: https://huggingface.co/spaces/poscye/google-go
2. HelpingAI 9B - A model that surpassed all top AIs with the highest EQ benchmark score of 89.23. It specializes in understanding human emotions and responding in human style.
Demo Link: https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B
Model Link: https://huggingface.co/OEvortex/HelpingAI-9B
Blog Link: https://huggingface.co/blog/KingNish/helpingai-9b | {
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885684618092437 | [
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"type": "text",
"value": "We are pleased to announce the new line of universal token classification models ๐ฅ",
"raw": "We are pleased to announce the new line of universal token classification models ๐ฅ",
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... | We are pleased to announce the new line of universal token classification models ๐ฅ
https://huggingface.co/collections/knowledgator/universal-token-classification-65a3a5d3f266d20b2e05c34d
It can perform various information extraction tasks by analysing input prompts and recognizing parts of texts that satisfy prompts. In comparison with the first version, the second one is more general and can be recognised as entities, whole sentences, and even paragraphs.
The model can be used for the following tasks:
* Named entity recognition (NER);
* Open information extraction;
* Question answering;
* Relation extraction;
* Coreference resolution;
* Text cleaning;
* Summarization;
How to use:
```
from utca.core import (
AddData,
RenameAttribute,
Flush
)
from utca.implementation.predictors import (
TokenSearcherPredictor, TokenSearcherPredictorConfig
)
from utca.implementation.tasks import (
TokenSearcherNER,
TokenSearcherNERPostprocessor,
)
predictor = TokenSearcherPredictor(
TokenSearcherPredictorConfig(
device="cuda:0",
model="knowledgator/UTC-DeBERTa-base-v2"
)
)
ner_task = TokenSearcherNER(
predictor=predictor,
postprocess=[TokenSearcherNERPostprocessor(
threshold=0.5
)]
)
ner_task = TokenSearcherNER()
pipeline = (
AddData({"labels": ["scientist", "university", "city"]})
| ner_task
| Flush(keys=["labels"])
| RenameAttribute("output", "entities")
)
res = pipeline.run({
"text": """Dr. Paul Hammond, a renowned neurologist at Johns Hopkins University, has recently published a paper in the prestigious journal "Nature Neuroscience". """
})
```
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{
"type": "text",
"value": "๐๐ผ๐ ๐ฑ๐ผ๐ฒ๐ ๐ฎ๐ป ๐ฎ๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐๐ผ๐ฟ๐ธ๐ณ๐น๐ผ๐ ๐๐๐ฒ ๐ถ๐๐ ๐๐๐ ๐ฒ๐ป๐ด๐ถ๐ป๐ฒ ๐๐ผ ๐๐ผ๐น๐๐ฒ ๐๐ฎ๐๐ธ๐?",
"raw": "๐๐ผ๐ ๐ฑ๐ผ๐ฒ๐ ๐ฎ๐ป ๐ฎ๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐๐ผ๐ฟ๐ธ๐ณ๐น๐ผ๐ ๐๐๐ฒ ๐ถ๐๐ ๐๐๐ ๐ฒ๐ป๐ด๐ถ๐ป๐ฒ ๐๐ผ ๐๐ผ๐น๐๐ฒ ๐๐ฎ๐๐ธ๐?",
"href": null,
"resource": ... | ๐๐ผ๐ ๐ฑ๐ผ๐ฒ๐ ๐ฎ๐ป ๐ฎ๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐๐ผ๐ฟ๐ธ๐ณ๐น๐ผ๐ ๐๐๐ฒ ๐ถ๐๐ ๐๐๐ ๐ฒ๐ป๐ด๐ถ๐ป๐ฒ ๐๐ผ ๐๐ผ๐น๐๐ฒ ๐๐ฎ๐๐ธ๐?
โก๏ธ I made my first ever ๐ฎ๐ข๐ฏ๐ช๐ฎ video to show just that:
๐ช๐ฎ๐๐ฐ๐ต ๐ฏ๐ฒ๐น๐ผ๐ ๐ต๐ผ๐ ๐ฎ ๐ฅ๐ฒ๐ฎ๐ฐ๐ ๐๐ด๐ฒ๐ป๐ ๐๐ผ๐น๐๐ฒ๐ ๐ฎ ๐๐ถ๐บ๐ฝ๐น๐ฒ ๐๐ฎ๐๐ธ, by leveraging its memory to iterate on previous actions! ๐ฌ๐
Read our blog post on Agents: https://huggingface.co/blog/agents | {
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"type": "text",
"value": "With the previous survey, Ghost Beta (small version) will support 9+ languages โโfluently. It is revealed that the model will be designed for 3 stages of training, showing a checkpoint to try at stage 1 (trained progress: 29%). ",
"raw": "With the previous survey, Ghost Beta ... | With the previous survey, Ghost Beta (small version) will support 9+ languages โโfluently. It is revealed that the model will be designed for 3 stages of training, showing a checkpoint to try at stage 1 (trained progress: 29%).
Supported languages: ๐บ๐ธ English, ๐ช๐ธ Spanish, ๐ต๐น Portuguese, ๐ซ๐ท French, ๐ฎ๐น Italian, ๐ฉ๐ช German, ๐ป๐ณ Vietnamese, ๐ฐ๐ท Korean, ๐จ๐ณ Chinese, and !?
Note that this is not a conclusion, this is just a sharing of the state of the model. If you find it interesting, please follow the project at:
* https://x.com/ghostx_ai
* https://ghost-x.org/
* https://huggingface.co/ghost-x
๐คฏ๐ | {
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AI perfect hashtag generator
Create advanced hashtags using our smart AI system. | {
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"value": "Octave-X releases their proprietary model Tenzin. For now the access will be given to a select few and will gradually open up. Our model is different from other models in the way it learns. It is not fed heaps of information but starts learning exactly like a human by first study... | Octave-X releases their proprietary model Tenzin. For now the access will be given to a select few and will gradually open up. Our model is different from other models in the way it learns. It is not fed heaps of information but starts learning exactly like a human by first studying grammar patterns, then learning then number system, then learning to synthesize words and then sentences and so on. Patience is key with Tenzin. It keeps learning 24/7 with/without user-input. We have decided to keep our model closed-source given the novel algorithms integrated into it along with our novel ideas. Please expect our datacard soon which will be followed by our research paper. You can check us out at https://octave-x.com/ | {
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"Tar9... | 2024-05-31T05:10:29.000Z | 2024-05-31T05:10:29.872Z | [] | /posts/Tar9897/935912455364805 | 1,868 | 0 |
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