Hugging Face's logo
Joseph Robert Turcotte's picture
1 4 40

Joseph Robert Turcotte PRO

Fishtiks
21world's profile picture Chaibo's profile picture legolasyiu's profile picture
·

AI & ML interests

Roleplaying, lorabration, abliteration, smol models, extensive filtering, unusual datasets, home usage, HPCs for AI, distributed training, and sentience. I got a new computer to put AI on from the start, and intend to develop it. Let me know how people are getting H100s. Actually wants an L4 for inference and is willing to pay for one.

Recent Activity

reacted to nicolay-r's post with 🚀 about 1 hour ago
📢 If you wish to empower LLM with IR and named entity recognition module, then I got relevant findings. Just tested Flair below is how you can start for adapting for processing your CSV / JSONL data via bulk-ner 👩‍💻 code: https://github.com/nicolay-r/nlp-thirdgate/blob/master/tutorials/ner_flair_0151.sh 🤖 models: https://huggingface.co./flair Provider: https://raw.githubusercontent.com/nicolay-r/nlp-thirdgate/refs/heads/master/ner/flair_0151.py Framework: https://github.com/nicolay-r/bulk-ner 🚀 Performance: the default ner model (Thinkpad X1 Nano) Batch-size 1 6it/sec Batch-size 10+ 12it/sec 🌌 other wrappers for bulk-ner nlp-thirdgate: https://github.com/nicolay-r/nlp-thirdgate
liked a Space about 1 hour ago
LeMaterial/materials_explorer
liked a model about 15 hours ago
EpistemeAI/ReasoningCore-3B-T1_2
View all activity

Organizations

None yet

Fishtiks's activity

reacted to nicolay-r's post with 🚀 about 1 hour ago
view post
Post
718
📢 If you wish to empower LLM with IR and named entity recognition module, then I got relevant findings.
Just tested Flair below is how you can start for adapting for processing your CSV / JSONL data via bulk-ner
👩‍💻 code: https://github.com/nicolay-r/nlp-thirdgate/blob/master/tutorials/ner_flair_0151.sh
🤖 models: https://huggingface.co./flair

Provider: https://raw.githubusercontent.com/nicolay-r/nlp-thirdgate/refs/heads/master/ner/flair_0151.py
Framework: https://github.com/nicolay-r/bulk-ner

🚀 Performance: the default ner model (Thinkpad X1 Nano)
Batch-size 1 6it/sec
Batch-size 10+ 12it/sec

🌌 other wrappers for bulk-ner nlp-thirdgate: https://github.com/nicolay-r/nlp-thirdgate
liked a Space about 1 hour ago
liked a model about 15 hours ago
liked 2 Spaces about 15 hours ago
liked a Space about 16 hours ago
reacted to MonsterMMORPG's post with 🔥❤️👍 1 day ago
view post
Post
1775
Amazing Gradio Batch Processing APP For Newest SOTA Background Remover Open Source Model BiRefNet HR (High Resolution) Published For Windows, RunPod, Massed Compute, and Kaggle

Installers and APP : https://www.patreon.com/posts/121679760

BiRefNet : Bilateral Reference for High-Resolution Dichotomous Image Segmentation

BiRefNet recently got some amazing updates and now it has high resolution model (2048x2048) and other speed and VRAM optimizations

We have upgraded our existing Gradio APP, added new model and new features

Official repo is here : https://github.com/ZhengPeng7/BiRefNet

We have published 1-Click installers for Windows, RunPod, Massed Compute and a free Kaggle Account notebook

Works great even on free Kaggle account

Check out the below attached images to learn more
replied to ngxson's post 1 day ago
reacted to ngxson's post with 🔥 1 day ago
view post
Post
2777
Check out my collection of pre-made GGUF LoRA adapters!

This allow you to use both normal + abliterated version of popular models like llama, qwen, etc, without having to double to amount of VRAM usage.

ngxson/gguf_lora_collection
·
New activity in bartowski/Llama-3.2-3B-Instruct-uncensored-GGUF 1 day ago

Good job!
#3 opened 1 day ago by
Fishtiks
liked a model 1 day ago
liked a Space 1 day ago
reacted to Xenova's post with 👍🤗🚀🔥 1 day ago
view post
Post
3247
We did it. Kokoro TTS (v1.0) can now run 100% locally in your browser w/ WebGPU acceleration. Real-time text-to-speech without a server. ⚡️

Generate 10 seconds of speech in ~1 second for $0.

What will you build? 🔥
webml-community/kokoro-webgpu

The most difficult part was getting the model running in the first place, but the next steps are simple:
✂️ Implement sentence splitting, allowing for streamed responses
🌍 Multilingual support (only phonemization left)

Who wants to help?
·
liked a model 1 day ago
reacted to KnutJaegersberg's post with 🚀 1 day ago
view post
Post
1354
Artificial general intelligence through recursive data compression and grounded reasoning: a position paper


This paper proposes a system to achieve AGI through general data compression and grounded reasoning.

General Data Compression involves creating a flexible algorithm that adapts to input data to simplify and compress it recursively, identifying simple, orthogonal features to avoid redundancy. The algorithm measures AGI progress by solving problems based on increasing complexity, and it expands its search space according to the data itself. Compression is applied not only to data but also to model parameters, and sequences are segmented based on compressibility.

Grounded Reasoning refers to forming representations with various granularities, crucial for commonsense reasoning and AGI. The system simulates the real world as its model, switching between representations and maximizing resourcefulness. Key ideas include the world as its own model for reasoning and actions aimed at maximizing entropy to test hypotheses.

The paper emphasizes simplicity, data-dependent bias, recursion, orthogonality, resourcefulness, and grounding in real-world contexts as fundamental principles in building an AGI system.

https://arxiv.org/abs/1506.04366
  • 1 reply
·