Inference for PROs
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52
Victor Mustar PRO
victor
AI & ML interests
Building the UX of this website
Recent Activity
liked
a model
about 10 hours ago
nomic-ai/modernbert-embed-base
liked
a model
about 10 hours ago
bartowski/QVQ-72B-Preview-GGUF
Articles
Organizations
victor's activity
reacted to
merve's
post with π₯
about 17 hours ago
Post
679
supercharge your LLM apps with smolagents π₯
however cool your LLM is, without being agentic it can only go so far
enter smolagents: a new agent library by Hugging Face to make the LLM write code, do analysis and automate boring stuff!
Here's our blog for you to get started https://huggingface.co/blog/smolagents
however cool your LLM is, without being agentic it can only go so far
enter smolagents: a new agent library by Hugging Face to make the LLM write code, do analysis and automate boring stuff!
Here's our blog for you to get started https://huggingface.co/blog/smolagents
reacted to
cfahlgren1's
post with π
about 20 hours ago
Post
2033
The
deepseek-ai/DeepSeek-V3 is very good! I have been playing with it and found it is really good at one-shotting a pretty good landing page.
You can play with it here: https://deepseek-artifacts.vercel.app
All the responses get saved in the cfahlgren1/react-code-instructions dataset. Hopefully we can build one of the biggest, highest quality frontend datasets on the hub πͺ
You can play with it here: https://deepseek-artifacts.vercel.app
All the responses get saved in the cfahlgren1/react-code-instructions dataset. Hopefully we can build one of the biggest, highest quality frontend datasets on the hub πͺ
reacted to
sequelbox's
post with π
about 20 hours ago
Post
1336
Check out the early preview of the upcoming Tachibana-QVQ dataset: code-reasoning and code-instruct data generated with
Qwen/QVQ-72B-Preview
Link here: sequelbox/Tachibana-QVQ-PREVIEW
more to come :)
Link here: sequelbox/Tachibana-QVQ-PREVIEW
more to come :)
- 1 reply
reacted to
ivanfioravanti's
post with π
3 days ago
Post
1427
Probably most of you already knows this trick but just in case:
π€ Unable to connect to Hugging Face Spaces Dev Mode through local Cursor? π‘ Don't worry there is an easy trick!
- right click Connect with VS Code
- copy link in your browser
- vscode://vscode-remote/...
- replace vscode with cursor and go
- cursor://vscode-remote/...
π€ Unable to connect to Hugging Face Spaces Dev Mode through local Cursor? π‘ Don't worry there is an easy trick!
- right click Connect with VS Code
- copy link in your browser
- vscode://vscode-remote/...
- replace vscode with cursor and go
- cursor://vscode-remote/...
reacted to
merve's
post with π
5 days ago
Post
3833
QwQ can see π₯
Qwen team released QvQ, a large vision LM with reasoning π±
it outperforms proprietary VLMs on several benchmarks, comes with open weights and a demo!
Check them out β¬οΈ
Demo Qwen/QVQ-72B-preview
Model Qwen/QVQ-72B-Preview
Read more https://qwenlm.github.io/blog/qvq-72b-preview/
Congratulations @JustinLin610 and team!
Qwen team released QvQ, a large vision LM with reasoning π±
it outperforms proprietary VLMs on several benchmarks, comes with open weights and a demo!
Check them out β¬οΈ
Demo Qwen/QVQ-72B-preview
Model Qwen/QVQ-72B-Preview
Read more https://qwenlm.github.io/blog/qvq-72b-preview/
Congratulations @JustinLin610 and team!
- 2 replies
reacted to
AdinaY's
post with ππ₯
5 days ago
Post
3410
The Chinese community is shipping π’
DeepSeek V3 (685 B MoE) has quietly released on the hub!
Base: deepseek-ai/DeepSeek-V3-Base
Instruct: deepseek-ai/DeepSeek-V3
Canβt wait to see whatβs next!
DeepSeek V3 (685 B MoE) has quietly released on the hub!
Base: deepseek-ai/DeepSeek-V3-Base
Instruct: deepseek-ai/DeepSeek-V3
Canβt wait to see whatβs next!
-
- 1 reply
reacted to
vincentg64's
post with π₯
5 days ago
Post
2172
LLM 2.0, RAG & Non-Standard Gen AI on GitHub https://mltblog.com/3DsyZSq
In this article, I share my latest Gen AI and LLM advances, featuring innovative approaches radically different from both standard AI and classical ML/NLP. The focus is on doing better with less, using efficient architectures, new algorithms and evaluation metrics. It originates from research that I started long ago. It gained significant momentum in the last two years. See background and history at https://mltblog.com/4g2sKTv.
OpenAI, Perplexity, Anthropic, Llama and others typically follow the trend and implement solutions very similar to mines within 3 to 6 months after I publish new milestones. For instance, multi-tokens, knowledge graph tokens, multi-indexes, real-time fine-tuning, mixtures of experts, LLM routers, small enterprise sub-LLMs, prompt distillation, relevancy scoring engine, deep contextual retrieval, optimum agentic chunking, and modern UI instead of the basic prompt box. I keep adding new features all the time, staying ahead of competition.
β‘οΈ Read full article with links to GitHub, at https://mltblog.com/3DsyZSq
In this article, I share my latest Gen AI and LLM advances, featuring innovative approaches radically different from both standard AI and classical ML/NLP. The focus is on doing better with less, using efficient architectures, new algorithms and evaluation metrics. It originates from research that I started long ago. It gained significant momentum in the last two years. See background and history at https://mltblog.com/4g2sKTv.
OpenAI, Perplexity, Anthropic, Llama and others typically follow the trend and implement solutions very similar to mines within 3 to 6 months after I publish new milestones. For instance, multi-tokens, knowledge graph tokens, multi-indexes, real-time fine-tuning, mixtures of experts, LLM routers, small enterprise sub-LLMs, prompt distillation, relevancy scoring engine, deep contextual retrieval, optimum agentic chunking, and modern UI instead of the basic prompt box. I keep adding new features all the time, staying ahead of competition.
β‘οΈ Read full article with links to GitHub, at https://mltblog.com/3DsyZSq
-
- 1 reply
reacted to
as-cle-bert's
post with π
5 days ago
Post
2118
I got my GitHub Wrapped for 2024 today!π₯
Get yours here on HuggingFace π as-cle-bert/what-a-git-year
GitHub repo with the code to reproduce it π https://github.com/AstraBert/what-a-git-year
Hope that everybody had a Git year!π
Get yours here on HuggingFace π as-cle-bert/what-a-git-year
GitHub repo with the code to reproduce it π https://github.com/AstraBert/what-a-git-year
Hope that everybody had a Git year!π
- 1 reply
reacted to
nicolay-r's
post with β€οΈ
5 days ago
Post
2032
π’ Deligted to share the most recent milestone on quick deployment of Named Entity Recognition (NER) in Gen-AI powered systems.
Releasing the bulk-ner 0.25.0 which represent a tiny framework that would save you time for deploing NER with any model.
π Why is this important? In the era of GenAI the handling out textual output might be challenging. Instead, recognizing named-entities via domain-oriented systems for your donwstream LLM would be preferable option.
π¦: https://pypi.org/project/bulk-ner/0.25.0/
π: https://github.com/nicolay-r/bulk-ner
I noticed that the direct adaptaion of the LM for NER would result in spending signifcant amount of time on formatting your texts according to the NER-model needs.
In particular:
1. Processing CONLL format with B-I-O tags from model outputs
2. Input trimming: long input content might not be completely fitted
To cope with these problems, in version 0.25.0 I made a huge steps forward by providing:
β π Python API support: see screenshot below for a quick deployment (see screenshot below πΈ)
β πͺΆ No-string: dependencies are now clear, so it is purely Python implementation for API calls.
β π Simplified output formatting: we use lists to represent texts with inner lists that refer to annotated objects (see screenshot below πΈ)
π We have a colab for a quick start here (or screenshot for bash / Python API πΈ)
https://colab.research.google.com/github/nicolay-r/ner-service/blob/main/NER_annotation_service.ipynb
π The code for pipeline deployment is taken from the AREkit project:
https://github.com/nicolay-r/AREkit
Releasing the bulk-ner 0.25.0 which represent a tiny framework that would save you time for deploing NER with any model.
π Why is this important? In the era of GenAI the handling out textual output might be challenging. Instead, recognizing named-entities via domain-oriented systems for your donwstream LLM would be preferable option.
π¦: https://pypi.org/project/bulk-ner/0.25.0/
π: https://github.com/nicolay-r/bulk-ner
I noticed that the direct adaptaion of the LM for NER would result in spending signifcant amount of time on formatting your texts according to the NER-model needs.
In particular:
1. Processing CONLL format with B-I-O tags from model outputs
2. Input trimming: long input content might not be completely fitted
To cope with these problems, in version 0.25.0 I made a huge steps forward by providing:
β π Python API support: see screenshot below for a quick deployment (see screenshot below πΈ)
β πͺΆ No-string: dependencies are now clear, so it is purely Python implementation for API calls.
β π Simplified output formatting: we use lists to represent texts with inner lists that refer to annotated objects (see screenshot below πΈ)
π We have a colab for a quick start here (or screenshot for bash / Python API πΈ)
https://colab.research.google.com/github/nicolay-r/ner-service/blob/main/NER_annotation_service.ipynb
π The code for pipeline deployment is taken from the AREkit project:
https://github.com/nicolay-r/AREkit
reacted to
Kseniase's
post with π
9 days ago
Post
2670
**15 Agentic Systems and Frameworks of 2024**
This year, we started our βAI Agents and Agentic Workflowsβ series (https://www.turingpost.com/t/AI-Agents) to explore everything about AI agents step by step: all the vocabulary, how they work, and how to build them.
The huge interest in this series and the large number of studies conducted on agents showed that it was one of the most popular and important themes of the year. In 2025, most likely, agents will reach new highs β we will be covering that for you. Now, letβs review the agentic systems that have emerged this year.
Here is a list of 15 agentic systems and frameworks of 2024:
1. GUI Agents: A Survey (2412.13501)
2. Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level (2411.03562)
3. The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery (2408.06292)
4. MALT: Improving Reasoning with Multi-Agent LLM Training (2412.01928)
5. Agent S: An Open Agentic Framework that Uses Computers Like a Human (2410.08164)
6. Automated Design of Agentic Systems (2408.08435)
7. AgentInstruct: Toward Generative Teaching with Agentic Flows (2407.03502)
8. AgentStore: Scalable Integration of Heterogeneous Agents As Specialized Generalist Computer Assistant (2410.18603)
9. WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents (2410.07484)
10. Generative Agent Simulations of 1,000 People (2411.10109)
11. DynaSaur: Large Language Agents Beyond Predefined Actions (2411.01747)
12. PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking (2410.12375)
13. Generative World Explorer (2411.11844)
14. Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines (2412.14684)
15. AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions (2410.20424)
Thanks for reading Turing Post!
Subscribe to receive new posts straight into your inbox -> https://www.turingpost.com/subscribe
This year, we started our βAI Agents and Agentic Workflowsβ series (https://www.turingpost.com/t/AI-Agents) to explore everything about AI agents step by step: all the vocabulary, how they work, and how to build them.
The huge interest in this series and the large number of studies conducted on agents showed that it was one of the most popular and important themes of the year. In 2025, most likely, agents will reach new highs β we will be covering that for you. Now, letβs review the agentic systems that have emerged this year.
Here is a list of 15 agentic systems and frameworks of 2024:
1. GUI Agents: A Survey (2412.13501)
2. Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level (2411.03562)
3. The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery (2408.06292)
4. MALT: Improving Reasoning with Multi-Agent LLM Training (2412.01928)
5. Agent S: An Open Agentic Framework that Uses Computers Like a Human (2410.08164)
6. Automated Design of Agentic Systems (2408.08435)
7. AgentInstruct: Toward Generative Teaching with Agentic Flows (2407.03502)
8. AgentStore: Scalable Integration of Heterogeneous Agents As Specialized Generalist Computer Assistant (2410.18603)
9. WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents (2410.07484)
10. Generative Agent Simulations of 1,000 People (2411.10109)
11. DynaSaur: Large Language Agents Beyond Predefined Actions (2411.01747)
12. PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking (2410.12375)
13. Generative World Explorer (2411.11844)
14. Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines (2412.14684)
15. AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions (2410.20424)
Thanks for reading Turing Post!
Subscribe to receive new posts straight into your inbox -> https://www.turingpost.com/subscribe
reacted to
nroggendorff's
post with π
9 days ago
Post
1295
Can we please do something about this? It makes everything I do so much harder, and because my local machine is so terrible, I am forced to test in production. This makes debugging so difficult.
nroggendorff/system-exit
cc @victor
nroggendorff/system-exit
cc @victor
- 1 reply
reacted to
anton-l's
post with π₯
12 days ago
Post
2074
Introducing ππ
π’π§πππππ‘: the best public math pre-training dataset with 50B+ tokens!
HuggingFaceTB/finemath
Math remains challenging for LLMs and by training on FineMath we see considerable gains over other math datasets, especially on GSM8K and MATH.
We build the dataset by:
π οΈ carefully extracting math data from Common Crawl;
π iteratively filtering and recalling high quality math pages using a classifier trained on synthetic annotations to identify math reasoning and deduction.
We conducted a series of ablations comparing the performance of Llama-3.2-3B-Base after continued pre-training on FineMath and observe notable gains compared to the baseline model and other public math datasets.
We hope this helps advance the performance of LLMs on math and reasoning! π
Weβre also releasing all the ablation models as well as the evaluation code.
HuggingFaceTB/finemath-6763fb8f71b6439b653482c2
HuggingFaceTB/finemath
Math remains challenging for LLMs and by training on FineMath we see considerable gains over other math datasets, especially on GSM8K and MATH.
We build the dataset by:
π οΈ carefully extracting math data from Common Crawl;
π iteratively filtering and recalling high quality math pages using a classifier trained on synthetic annotations to identify math reasoning and deduction.
We conducted a series of ablations comparing the performance of Llama-3.2-3B-Base after continued pre-training on FineMath and observe notable gains compared to the baseline model and other public math datasets.
We hope this helps advance the performance of LLMs on math and reasoning! π
Weβre also releasing all the ablation models as well as the evaluation code.
HuggingFaceTB/finemath-6763fb8f71b6439b653482c2
reacted to
m-ric's
post with π₯
12 days ago
Post
1766
After 6 years, BERT, the workhorse of encoder models, finally gets a replacement: πͺπ²πΉπ°πΌπΊπ² π πΌπ±π²πΏπ»πππ₯π§! π€
We talk a lot about β¨Generative AIβ¨, meaning "Decoder version of the Transformers architecture", but this is only one of the ways to build LLMs: encoder models, that turn a sentence in a vector, are maybe even more widely used in industry than generative models.
The workhorse for this category has been BERT since its release in 2018 (that's prehistory for LLMs).
It's not a fancy 100B parameters supermodel (just a few hundred millions), but it's an excellent workhorse, kind of a Honda Civic for LLMs.
Many applications use BERT-family models - the top models in this category cumulate millions of downloads on the Hub.
β‘οΈ Now a collaboration between Answer.AI and LightOn just introduced BERT's replacement: ModernBERT.
π§π;ππ₯:
ποΈ Architecture changes:
β First, standard modernizations:
- Rotary positional embeddings (RoPE)
- Replace GeLU with GeGLU,
- Use Flash Attention 2
β¨ The team also introduced innovative techniques like alternating attention instead of full attention, and sequence packing to get rid of padding overhead.
π₯ As a result, the model tops the game of encoder models:
It beats previous standard DeBERTaV3 for 1/5th the memory footprint, and runs 4x faster!
Read the blog post π https://huggingface.co/blog/modernbert
We talk a lot about β¨Generative AIβ¨, meaning "Decoder version of the Transformers architecture", but this is only one of the ways to build LLMs: encoder models, that turn a sentence in a vector, are maybe even more widely used in industry than generative models.
The workhorse for this category has been BERT since its release in 2018 (that's prehistory for LLMs).
It's not a fancy 100B parameters supermodel (just a few hundred millions), but it's an excellent workhorse, kind of a Honda Civic for LLMs.
Many applications use BERT-family models - the top models in this category cumulate millions of downloads on the Hub.
β‘οΈ Now a collaboration between Answer.AI and LightOn just introduced BERT's replacement: ModernBERT.
π§π;ππ₯:
ποΈ Architecture changes:
β First, standard modernizations:
- Rotary positional embeddings (RoPE)
- Replace GeLU with GeGLU,
- Use Flash Attention 2
β¨ The team also introduced innovative techniques like alternating attention instead of full attention, and sequence packing to get rid of padding overhead.
π₯ As a result, the model tops the game of encoder models:
It beats previous standard DeBERTaV3 for 1/5th the memory footprint, and runs 4x faster!
Read the blog post π https://huggingface.co/blog/modernbert
- 1 reply
reacted to
akhaliq's
post with π₯
12 days ago
Post
3154
Google drops Gemini 2.0 Flash Thinking
a new experimental model that unlocks stronger reasoning capabilities and shows its thoughts. The model plans (with thoughts visible), can solve complex problems with Flash speeds, and more
now available in anychat, try it out: akhaliq/anychat
a new experimental model that unlocks stronger reasoning capabilities and shows its thoughts. The model plans (with thoughts visible), can solve complex problems with Flash speeds, and more
now available in anychat, try it out: akhaliq/anychat
reacted to
Lewdiculous's
post with β
12 days ago
Post
2293
Hello fellow LLMers, just a quick notice that some of my activity will be moved into the AetherArchitectural Commuity and split with
@Aetherarchio
.
[here] https://huggingface.co/AetherArchitectural
All activity should be visible in the left side of my profile.
[here] https://huggingface.co/AetherArchitectural
All activity should be visible in the left side of my profile.
- 1 reply
reacted to
KnutJaegersberg's
post with π
12 days ago
Post
1284
Intelligence Potentiation: An Evolutionary Perspective on AI Agent Designs
I found it useful to think of AI agent design as progressing up a ladder, through evolutionary selection.
https://huggingface.co/blog/KnutJaegersberg/intelligence-potentiation
I found it useful to think of AI agent design as progressing up a ladder, through evolutionary selection.
https://huggingface.co/blog/KnutJaegersberg/intelligence-potentiation
reacted to
FranckAbgrall's
post with π₯
13 days ago
Post
1091
π It should now be easier to identify discussions or pull requests where repository owners are participating on HF, let us know it that helps π¬π€
- 1 reply