Inference for PROs
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Victor Mustar PRO
victor
AI & ML interests
Building the UX of this website
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victor's activity
reacted to
cutechicken's
post with 🔥
1 day ago
Post
2345
# Tank War: A Cool AI-Generated Game Making Waves on Hugging Face
Hey there! Let me tell you about Tank War, a super interesting HTML5 Canvas game that's currently ranked #11 on Hugging Face Trending. What's really cool about this game is that it was initially whipped up in just one minute using MOUSE-I ( VIDraft/mouse1), and then polished up with some human touches to the metadata files. Pretty neat, right?
## What Makes It Fun?
- **Stage-by-Stage Action**: You get 2 stages, each packed with 10 rounds and an epic boss battle
- **Power-Up Shopping**: Grab new tanks and upgrades with your hard-earned gold
- **Two-Gun System**: Switch between a heavy-hitting cannon and a rapid-fire machine gun
- **Air Support**: Call in BF-109 fighters and JU-87 dive bombers to rain down some extra firepower
## The Tech Behind the Magic
1. **AI-Powered Foundation**
- Quick game logic generation through MOUSE-I
- Fine-tuned with custom metadata tweaks
2. **Smooth Canvas Graphics**
- Butter-smooth animations with requestAnimationFrame
- Smart hitbox system for precise combat
3. **Smart Code Structure**
- Well-organized classes for enemies, effects, and support units
- Clever code reuse through inheritance
4. **Cool Game Features**
- Awesome sound effects and background music
- Smart enemy AI that keeps you on your toes
- Support units that know how to pick their targets
This project shows just how far we've come with AI-assisted game development, and its popularity on Hugging Face proves it's onto something good! It's a perfect example of how MOUSE-I's quick prototyping abilities and a developer's careful tweaking can create something really special. Think of it as AI and human creativity teaming up to make something awesome! 🎮✨
cutechicken/tankwar
Hey there! Let me tell you about Tank War, a super interesting HTML5 Canvas game that's currently ranked #11 on Hugging Face Trending. What's really cool about this game is that it was initially whipped up in just one minute using MOUSE-I ( VIDraft/mouse1), and then polished up with some human touches to the metadata files. Pretty neat, right?
## What Makes It Fun?
- **Stage-by-Stage Action**: You get 2 stages, each packed with 10 rounds and an epic boss battle
- **Power-Up Shopping**: Grab new tanks and upgrades with your hard-earned gold
- **Two-Gun System**: Switch between a heavy-hitting cannon and a rapid-fire machine gun
- **Air Support**: Call in BF-109 fighters and JU-87 dive bombers to rain down some extra firepower
## The Tech Behind the Magic
1. **AI-Powered Foundation**
- Quick game logic generation through MOUSE-I
- Fine-tuned with custom metadata tweaks
2. **Smooth Canvas Graphics**
- Butter-smooth animations with requestAnimationFrame
- Smart hitbox system for precise combat
3. **Smart Code Structure**
- Well-organized classes for enemies, effects, and support units
- Clever code reuse through inheritance
4. **Cool Game Features**
- Awesome sound effects and background music
- Smart enemy AI that keeps you on your toes
- Support units that know how to pick their targets
This project shows just how far we've come with AI-assisted game development, and its popularity on Hugging Face proves it's onto something good! It's a perfect example of how MOUSE-I's quick prototyping abilities and a developer's careful tweaking can create something really special. Think of it as AI and human creativity teaming up to make something awesome! 🎮✨
cutechicken/tankwar
reacted to
clem's
post with ❤️
2 days ago
Post
3765
Hugging Face is becoming the best place to share the most viral AI apps with spaces.
Kolors Virtual Try-on just crossed 6,000,000 unique visitors & is now the #5 most popular space. Congrats to the Kwai Kolors team!
Kwai-Kolors/Kolors-Virtual-Try-On
Kolors Virtual Try-on just crossed 6,000,000 unique visitors & is now the #5 most popular space. Congrats to the Kwai Kolors team!
Kwai-Kolors/Kolors-Virtual-Try-On
- 2 replies
reacted to
prithivMLmods's
post with 🤗
4 days ago
Post
2464
Fine-Textured [Polygon] Character 3D Design Renders 🙉
Adapters capable of providing better lighting control (Bn+, Bn-) and richer textures compared to previous sets require more contextual prompts for optimal performance.
The ideal settings are achieved at inference steps around 30–35, with the best dimensions being 1280 x 832 [ 3:2 ]. However, it also performs well with the default settings of 1024 x 1024 [ 1:1 ].
💢Models DLC :
+ strangerzonehf/Flux-3DXL-Partfile-0001
+ strangerzonehf/Flux-3DXL-Partfile-0002
+ strangerzonehf/Flux-3DXL-Partfile-0003
+ strangerzonehf/Flux-3DXL-Partfile-0004
+ strangerzonehf/Flux-3DXL-Partfile-C0001
💢Collections :
1] strangerzonehf/flux-3dxl-engine-674833c14a001d5b1fdb5139
2] prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
💢Space :
1] prithivMLmods/FLUX-LoRA-DLC
💢Page :
1] Stranger Zone: https://huggingface.co/strangerzonehf
.
.
.
@prithivMLmods 🤗
Adapters capable of providing better lighting control (Bn+, Bn-) and richer textures compared to previous sets require more contextual prompts for optimal performance.
The ideal settings are achieved at inference steps around 30–35, with the best dimensions being 1280 x 832 [ 3:2 ]. However, it also performs well with the default settings of 1024 x 1024 [ 1:1 ].
💢Models DLC :
+ strangerzonehf/Flux-3DXL-Partfile-0001
+ strangerzonehf/Flux-3DXL-Partfile-0002
+ strangerzonehf/Flux-3DXL-Partfile-0003
+ strangerzonehf/Flux-3DXL-Partfile-0004
+ strangerzonehf/Flux-3DXL-Partfile-C0001
💢Collections :
1] strangerzonehf/flux-3dxl-engine-674833c14a001d5b1fdb5139
2] prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
💢Space :
1] prithivMLmods/FLUX-LoRA-DLC
💢Page :
1] Stranger Zone: https://huggingface.co/strangerzonehf
.
.
.
@prithivMLmods 🤗
posted
an
update
4 days ago
Post
1490
Qwen/QwQ-32B-Preview shows us the future (and it's going to be exciting)...
I tested it against some really challenging reasoning prompts and the results are amazing 🤯.
Check this dataset for the results: victor/qwq-misguided-attention
I tested it against some really challenging reasoning prompts and the results are amazing 🤯.
Check this dataset for the results: victor/qwq-misguided-attention
- 2 replies
reacted to
andito's
post with 👀
5 days ago
Post
1651
SmolVLM speeding locally on a laptop thanks to mlx-vlm and
@Gradio ! Try it with two lines:
pip install git+https://github.com/andimarafioti/mlx-vlm.git@stream-generate-fix
python -m mlx_vlm.chat_ui --model mlx-community/SmolVLM-Instruct-8bit
Gotta love the MLX community! Big thanks to @pcuenq and @prince_canuma !
@Gradio ! Try it with two lines:
pip install git+https://github.com/andimarafioti/mlx-vlm.git@stream-generate-fix
python -m mlx_vlm.chat_ui --model mlx-community/SmolVLM-Instruct-8bit
Gotta love the MLX community! Big thanks to @pcuenq and @prince_canuma !
reacted to
MonsterMMORPG's
post with 🔥
5 days ago
Post
1784
FLUX Redux is a hidden Gem
I am still doing huge research to publish an amazing fully Public - no paywalled Tutorial, but this is generated via SwarmUI
Style Model Merge Strength : 0.5
FLUX Guidance Scale is : 6
Used base model is my FLUX fine tuned model with 256 images via Kohya SS GUI as shown in tutorial ( https://youtu.be/FvpWy1x5etM ) - 70 epoch
Prompt : anime ohwx man walking in a jungle <segment:yolo-face_yolov9c.pt-1,0.7,0.5> ohwx man, anime
I am still doing huge research to publish an amazing fully Public - no paywalled Tutorial, but this is generated via SwarmUI
Style Model Merge Strength : 0.5
FLUX Guidance Scale is : 6
Used base model is my FLUX fine tuned model with 256 images via Kohya SS GUI as shown in tutorial ( https://youtu.be/FvpWy1x5etM ) - 70 epoch
Prompt : anime ohwx man walking in a jungle <segment:yolo-face_yolov9c.pt-1,0.7,0.5> ohwx man, anime
- 2 replies
reacted to
AdinaY's
post with 🔥
5 days ago
Post
1497
🌊 The wave of reasoning models from the Chinese community has arrived!
🚀 Marco-o1 by AIDC, Alibaba
👉 AIDC-AI/Marco-o1
✨ QwQ by Qwen, Alibaba
👉 Qwen/qwq-674762b79b75eac01735070a
🌟 Skywork-o1 by Kunlun Tech
👉 Skywork/skywork-o1-open-67453df58e12f6c3934738d0
🔥 Xkev/Llama-3.2V-11B-cot by PKU Yuan group
👉 Xkev/Llama-3.2V-11B-cot
💡 DeepSeek-R1-Lite-Preview by DeepSeek AI
👉 https://chat.deepseek.com/
🔍 InternThinker Preview by Shanghai AI Lab
👉 https://sso.openxlab.org.cn/login?redirect=https://internlm-chat.intern-ai.org.cn/&clientId=ebmrvod6yo0nlzaek1yp
📘 k0-math by Moonshot AI
🚀 https://kimi.moonshot.cn/ ( coming soon! )
Who's next? 👀
zh-ai-community/reasoning-models-67409fb3aa1ed78f10087cd7
🚀 Marco-o1 by AIDC, Alibaba
👉 AIDC-AI/Marco-o1
✨ QwQ by Qwen, Alibaba
👉 Qwen/qwq-674762b79b75eac01735070a
🌟 Skywork-o1 by Kunlun Tech
👉 Skywork/skywork-o1-open-67453df58e12f6c3934738d0
🔥 Xkev/Llama-3.2V-11B-cot by PKU Yuan group
👉 Xkev/Llama-3.2V-11B-cot
💡 DeepSeek-R1-Lite-Preview by DeepSeek AI
👉 https://chat.deepseek.com/
🔍 InternThinker Preview by Shanghai AI Lab
👉 https://sso.openxlab.org.cn/login?redirect=https://internlm-chat.intern-ai.org.cn/&clientId=ebmrvod6yo0nlzaek1yp
📘 k0-math by Moonshot AI
🚀 https://kimi.moonshot.cn/ ( coming soon! )
Who's next? 👀
zh-ai-community/reasoning-models-67409fb3aa1ed78f10087cd7
reacted to
LukeNeumann's
post with 👀
6 days ago
Post
1015
I had a question about Trending datasets. Our initial dataset "Oregon Coast in 4K" was trending at #3 for video at about 700 downloads.
Over the past two days our downloads have spiked, now up to over 2,000, but the dataset has dropped down to the 3rd or 4th page of Trending.
What metrics are used to determine dataset Trending position?
Over the past two days our downloads have spiked, now up to over 2,000, but the dataset has dropped down to the 3rd or 4th page of Trending.
What metrics are used to determine dataset Trending position?
- 1 reply
reacted to
merve's
post with ❤️
7 days ago
Post
2938
your hugging face profile now has your recent activities 🤗
reacted to
jsulz's
post with 👀
7 days ago
Post
1450
Something I love about working at Hugging Face is the opportunity to design and work in public. Right now, we’re redesigning the architecture that supports uploads and downloads on the Hub.
Datasets and models are growing fast, and so are the challenges of storing and transferring them efficiently. To keep up, we're introducing a new protocol for uploads and downloads, supported by a content-addressed store (CAS).
Here’s what’s coming:
📦 Smarter uploads: Chunk-level management enables advanced deduplication, compression, and reduces redundant transfers, speeding up uploads.
⚡ Efficient downloads: High throughput and low latency ensure fast access, even during high-demand model releases.
🔒 Enhanced security: Validate uploads before storage to block malicious or invalid data.
We analyzed 24 hours of global upload activity in October (88 countries, 130TB of data!) to design a system that scales with your needs.
The result? A proposed infrastructure with CAS nodes in us-east-1, eu-west-3, and ap-southeast-1.
🔗 Read the blog post for the full details: https://huggingface.co/blog/rearchitecting-uploads-and-downloads
🌟 Check out our interactive demo to explore the data yourself!
xet-team/cas-analysis
We’d love to hear your feedback - let us know if you have questions or want to see more.
Datasets and models are growing fast, and so are the challenges of storing and transferring them efficiently. To keep up, we're introducing a new protocol for uploads and downloads, supported by a content-addressed store (CAS).
Here’s what’s coming:
📦 Smarter uploads: Chunk-level management enables advanced deduplication, compression, and reduces redundant transfers, speeding up uploads.
⚡ Efficient downloads: High throughput and low latency ensure fast access, even during high-demand model releases.
🔒 Enhanced security: Validate uploads before storage to block malicious or invalid data.
We analyzed 24 hours of global upload activity in October (88 countries, 130TB of data!) to design a system that scales with your needs.
The result? A proposed infrastructure with CAS nodes in us-east-1, eu-west-3, and ap-southeast-1.
🔗 Read the blog post for the full details: https://huggingface.co/blog/rearchitecting-uploads-and-downloads
🌟 Check out our interactive demo to explore the data yourself!
xet-team/cas-analysis
We’d love to hear your feedback - let us know if you have questions or want to see more.
- 5 replies
reacted to
gabrielchua's
post with 👀
7 days ago
Post
1091
Sharing my first paper!
==
Large Language Models (LLMs) are powerful, but they're prone to off-topic misuse, where users push them beyond their intended scope. Think harmful prompts, jailbreaks, and misuse. So how do we build better guardrails?
Traditional guardrails rely on curated examples or classifiers. The problem?
⚠️ High false-positive rates
⚠️ Poor adaptability to new misuse types
⚠️ Require real-world data, which is often unavailable during pre-production
Our method skips the need for real-world misuse examples. Instead, we:
1️⃣ Define the problem space qualitatively
2️⃣ Use an LLM to generate synthetic misuse prompts
3️⃣ Train and test guardrails on this dataset
We apply this to the off-topic prompt detection problem, and fine-tune simple bi- and cross-encoder classifiers that outperform heuristics based on cosine similarity or prompt engineering.
Additionally, framing the problem as prompt relevance allows these fine-tuned classifiers to generalise to other risk categories (e.g., jailbreak, toxic prompts).
Through this work, we also open-source our dataset (2M examples, ~50M+ tokens) and models.
paper: A Flexible Large Language Models Guardrail Development Methodology Applied to Off-Topic Prompt Detection (2411.12946)
artifacts: govtech/off-topic-guardrail-673838a62e4c661f248e81a4
==
Large Language Models (LLMs) are powerful, but they're prone to off-topic misuse, where users push them beyond their intended scope. Think harmful prompts, jailbreaks, and misuse. So how do we build better guardrails?
Traditional guardrails rely on curated examples or classifiers. The problem?
⚠️ High false-positive rates
⚠️ Poor adaptability to new misuse types
⚠️ Require real-world data, which is often unavailable during pre-production
Our method skips the need for real-world misuse examples. Instead, we:
1️⃣ Define the problem space qualitatively
2️⃣ Use an LLM to generate synthetic misuse prompts
3️⃣ Train and test guardrails on this dataset
We apply this to the off-topic prompt detection problem, and fine-tune simple bi- and cross-encoder classifiers that outperform heuristics based on cosine similarity or prompt engineering.
Additionally, framing the problem as prompt relevance allows these fine-tuned classifiers to generalise to other risk categories (e.g., jailbreak, toxic prompts).
Through this work, we also open-source our dataset (2M examples, ~50M+ tokens) and models.
paper: A Flexible Large Language Models Guardrail Development Methodology Applied to Off-Topic Prompt Detection (2411.12946)
artifacts: govtech/off-topic-guardrail-673838a62e4c661f248e81a4
reacted to
csabakecskemeti's
post with 👀
7 days ago
Post
1155
I have this small utility: no_more_typo
It is running in the background and able to call the LLM model to update the text on the clipboard. I think it would be ideal to fix typos and syntax.
I have just added the option to use custom prompt templates to perform different tasks.
Details, code and executable:
https://github.com/csabakecskemeti/no_more_typo
https://devquasar.com/no-more-typo/
It is running in the background and able to call the LLM model to update the text on the clipboard. I think it would be ideal to fix typos and syntax.
I have just added the option to use custom prompt templates to perform different tasks.
Details, code and executable:
https://github.com/csabakecskemeti/no_more_typo
https://devquasar.com/no-more-typo/
reacted to
maxiw's
post with 👍
7 days ago
Post
1919
You can now try out computer use models from the hub to automate your local machine with https://github.com/askui/vision-agent. 💻
Currently these models are integrated with Gradio Spaces API. Also planning to add local inference soon!
Currently supported:
- Qwen/Qwen2-VL-7B-Instruct
- Qwen/Qwen2-VL-2B-Instruct
- AskUI/PTA-1
- OS-Copilot/OS-Atlas-Base-7B
import time
from askui import VisionAgent
with VisionAgent() as agent:
agent.tools.webbrowser.open_new("http://www.google.com")
time.sleep(0.5)
agent.click("search field in the center of the screen", model_name="Qwen/Qwen2-VL-7B-Instruct")
agent.type("cats")
agent.keyboard("enter")
time.sleep(0.5)
agent.click("text 'Images'", model_name="AskUI/PTA-1")
time.sleep(0.5)
agent.click("second cat image", model_name="OS-Copilot/OS-Atlas-Base-7B")Currently these models are integrated with Gradio Spaces API. Also planning to add local inference soon!
Currently supported:
- Qwen/Qwen2-VL-7B-Instruct
- Qwen/Qwen2-VL-2B-Instruct
- AskUI/PTA-1
- OS-Copilot/OS-Atlas-Base-7B
- 3 replies
reacted to
KnutJaegersberg's
post with 🔥
7 days ago
Post
1072
openGPT-X/Teuken-7B-instruct-research-v0.4
New European LLM
openGPT-X/Teuken-7B-instruct-research-v0.4
New European LLM
openGPT-X/Teuken-7B-instruct-research-v0.4
reacted to
davanstrien's
post with ❤️
7 days ago
Post
2291
First dataset for the new Hugging Face Bluesky community organisation:
bluesky-community/one-million-bluesky-posts 🦋
📊 1M public posts from Bluesky's firehose API
🔍 Includes text, metadata, and language predictions
🔬 Perfect to experiment with using ML for Bluesky 🤗
Excited to see people build more open tools for a more open social media platform!
📊 1M public posts from Bluesky's firehose API
🔍 Includes text, metadata, and language predictions
🔬 Perfect to experiment with using ML for Bluesky 🤗
Excited to see people build more open tools for a more open social media platform!
reacted to
vilarin's
post with 🤗🤯
7 days ago
Post
1276
A few days ago, Blackforestlabs released FLUX.1 Tools, which has surprised everyone with its quality and effects. Now that diffusers support these features, you can easily deploy and build your own Tools.
Combined with the powerful Gradio and ZeroGPU, you can experience the Tools immediately, which is truly wonderful.
I was impressed by the Flux.1 Fill dev, so here I've built a demo for it, making it easy to use for inpainting and outpainting images.
🏄Model: black-forest-labs/FLUX.1-Fill-dev
🦖Demo: vilarin/Flux.1-Fill-dev
👏diffusers: https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/flux
Combined with the powerful Gradio and ZeroGPU, you can experience the Tools immediately, which is truly wonderful.
I was impressed by the Flux.1 Fill dev, so here I've built a demo for it, making it easy to use for inpainting and outpainting images.
🏄Model: black-forest-labs/FLUX.1-Fill-dev
🦖Demo: vilarin/Flux.1-Fill-dev
👏diffusers: https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/flux
reacted to
fdaudens's
post with 👀
8 days ago
Post
1279
🤖 93% of Gen Z workers use AI tools weekly, but nearly half of all workers aren't comfortable admitting it. The tech adoption gap isn't about usage—it's about openness. Why are we still treating AI tools like a workplace secret? 🤔
See this article: https://www.axios.com/2024/11/25/gen-z-ai-work-survey
See this article: https://www.axios.com/2024/11/25/gen-z-ai-work-survey
- 1 reply