FlameF0X (Daniel Fox)

reacted to Hari5115's post with 🔥 about 7 hours ago

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Bit addictive. Fair warning !!!
Chain combos, fever mode, daily leaderboard. Free, runs in your browser.
Beat the score if you can 🫧

🎮 Hari5115/neon-pop

#SendHelp #JustOneMoreGame #NeonPop #NotAddicted

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replied to Hari5115's post about 10 hours ago

New record 🔥🔥🔥

reacted to Reubencf's post with 🔥 1 day ago

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Shadows of Tomorrow is finally live on Hugging Face Spaces with Gradio.

It’s a browser-playable RPG built with Godot, set in a post-nuclear future where players explore Magnus Province, collect medicinal plants, craft medicine, and help cure NPCs.

Play it here: Reubencf/Shadows_of_Tomorrow

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replied to their post 12 days ago

Ah, MD doesn't work :(

posted an update 12 days ago

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My models on the Intel Low-Bit LLM Leaderboard

Figured I'd share where my quantized models landed on Intel/low_bit_open_llm_leaderboard since I hadn't posted about it yet.

FlameF0X/Qwen3-4B-Distilled-Claude-4.6 (NVFP4 and MXFP4) sit at ranks 23 and 24 with 62.68% and 61.18% average, right below the base Qwen3-4B. Not bad considering they were distilled from Claude 4.6 rather than trained from scratch.

FlameF0X/LFM2.5-1.2B-Distilled-Claude-4.6 and FlameF0X/LFM2.5-1.2B-Thinking-CodeX land around rank 47-49, competitive with MiniCPM5-1B and the Qwen3 sub-1B models despite being a larger base architecture.

The funny one is FlameF0X/Qwen2-0.2B-pt and FlameF0X/Qwen2-0.2B-it. They're not properly trained — genuinely undertrained, basically undefined — and they still beat openai/gpt-oss-20b at rank 66. The 20B model. Not sure what that says but it's something.

FlameF0X/LFM2-Research is at the bottom of my lineup but it's a research artifact, not meant to be competitive.

Chart below showing my models vs nearby competitors, with size vs performance on the left.

Chart made by Claude

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reacted to their post with 🔥 19 days ago

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MiniMax-M3 coming soon.
https://github.com/MiniMax-AI/MiniMax-M3

posted an update 20 days ago

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MiniMax-M3 coming soon.
https://github.com/MiniMax-AI/MiniMax-M3

reacted to wenhuach's post with 🔥 25 days ago

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🚀 We provide **free** hardware to quantize models at the [Intel Low Bit Open LLM Leaderboard]( Intel/low_bit_open_llm_leaderboard), currently supporting Pure RTN mode powered by AutoRound

⭐ If you find it useful, please consider starring the AutoRound project on [GitHub](https://github.com/intel/auto-round)!

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reacted to pankajpandey-dev's post with 🔥 26 days ago

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🧬 Just uploaded K-quants of Carbon-3B for llama.cpp users!
@HuggingFaceBio released the original GGUF in bf16 only — so I added the full quant ladder for CPU/edge inference:
• Q2_K → 1.4 GB
• Q3_K_M → 1.8 GB
• Q4_K_M → 2.1 GB ⭐
• Q5_K_M → 2.4 GB
• Q6_K → 2.7 GB
• Q8_0 → 3.5 GB
🔗 pankajpandey-dev/Carbon-3B-GGUF
Now you can generate DNA sequences on your laptop. Needs a llama.cpp build with PR #23410 (HybridDNATokenizer support).
Huge thanks to the HuggingFaceBio team for the original model 🙏
#GGUF #llamacpp #genomics #DNA

reacted to Crownelius's post with 🔥🔥🔥 about 1 month ago

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Howdy,
CompactAI-O is launching a tiny Model Golf, and the winner walks away with $50 in RunPod credits. Monthly. Every month. Show up, build, somebody wins.

What it is

Build the best language model you can under 100 million parameters, with at least a 1028-token context window. That's it. Any architecture, any tokenizer, any training scheme you can dream up at 3am. The only catch is it's gotta be open source (MIT, GPL, Apache, AGPL) take your pick.

It scratches the same itch as a Kaggle comp without the dataset\leaderboard nonsense. No fixed benchmark to game. No llama.cpp compatibility hoops. If you wanna train a 50M-param MoE with five experts and a tokenizer built on cookbooks, you can do that. Nothing stopping you.

The rules are listed in the discord and on the organization page if you're interested.

Why $50????

It's symbolic. It ain't gonna make anyone rich. But it's enough to cover a weekend of GPU time, enough to keep enthusiasts coming back, and not so much that it pulls in people who are just there for the money. Enthusiasts build interesting things. Interesting things move the field forward. A little incentive. I'd do it for $50 lol.

How to join

First round opens soon. Landing page is here:

→ https://huggingface.co/spaces/CompactAI-O/Tiny-model-golf

For questions or to swap ideas, the Discord's open:

→ https://discord.gg/y2jTct6Cxv

Excited to see what yall come up with. ♥

— Shane

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reacted to alvarobartt's post with 🚀 about 1 month ago

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Latest hf-mem release added a breakdown of Mixture-of-Experts (MoE) memory usage!

TL; DR MoEs can be misleading to reason about from active parameters alone, since each token only activates a subset of experts, while the serving setup still needs to account for the full resident memory footprint.

🧠 hf-mem now splits MoE memory into base model weights, routed experts, and KV cache
🏗️ Dense models usually load and use most weights every forward pass, while MoEs load many experts but only route each token to a few of them
⚡ Active params isn't the same as memory footprint, especially for sparse architectures
📦 Runtime memory is about what is used per request/token, while loading memory also includes the expert weights that need to be resident
📚 KV cache can still dominate depending on context length, batch size, and concurrency
🔀 Expert Parallelism (EP) helps shard experts across accelerators when expert weights dominate
🚀 Data Parallelism (DP) + EP is often a good fit for throughput-oriented MoE serving

Check the repository at https://github.com/alvarobartt/hf-mem

posted an update about 1 month ago

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I did some testing on the scalability of FWKV. It hits a speed bottleneck at 1B due to the T4’s bandwidth limitations. Theoretically, it should match RWKV’s inference speed if the GPU had more bandwidth. So the 1B size is not accurate.

replied to their post about 1 month ago

Not yet. I'm still experimenting.
Once I get something that I'm pleased with I'm going to write a blog.

posted an update about 1 month ago

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Greetings Hugging Face!

I started a new project called **FWKV** (Feed-forward Weighted Key Value, or Floored Weighted Key Value), a RWKV-style LM that uses FFNNs (Feed-Forward Neural Networks) instead of RNN and floor(W·K·V). I'm hoping to make it much more efficient and scalable than RWKV.

So far I have:

- FlameF0X/FWKV-29M — this one is undertrained and doesn't have a Space yet. In the attached image you can see its speed on a T4 compared to models with the same configuration.

The only model that's fully working right now is:
- FlameF0X/FWKV-TinyStories — trained on TinyStories for one epoch. The demo Space is FlameF0X/FWKV-demo.

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reacted to appvoid's post with 👀 about 1 month ago

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As promised

appvoid/palmer-005-nano

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reacted to ArtelTaleb's post with 🔥 about 1 month ago

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✈️ World Flight Arcade - Can you land in 60 seconds?

I just dropped a new browser game built entirely with Three.js: World Flight Arcade

The concept is brutally simple:
- 🕐 60 seconds of flight above a neon wireframe city
- ✈️ One single attempt to land on the runway
- 💀 No second chances. No respawn. Just you, the controls, and the clock.

The camera system is fully dynamic - it stays locked behind the plane within a ±45° pitch/yaw envelope, giving you that cinematic flight feel while keeping full spatial awareness.

Can you nail the landing on your first try?

👉 Play here: ArtelTaleb/world-flight-arcade

Built by Artel3D - handcrafted in Three.js, zero dependencies, runs directly in your browser.

Drop your score in the comments 👇

#gamedev #threejs #browserGame #webgl #artel3d #indiegame

reacted to HannesVonEssen's post with ❤️ about 2 months ago

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📣 I made a visualizer for Hugging Face models: https://hfviewer.com

✨ Simply paste a Hugging Face URL to get an interactive visualization of the architecture!

🔗 The recent Qwen3.6-27B model as an example: https://hfviewer.com/Qwen/Qwen3.6-27B

Feel free to try it out and give me feedback on how it can be improved! ❤️

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reacted to Crownelius's post with 🔥 about 2 months ago

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[DAY ONE] PROJECT CROWFEATHER 4/30/2026
...The day I forgot to attach wandb.ai
Just dropped Crowfeather-50m, the first checkpoint in a series, and yeah, no graphs.

Crowfeather/Crowfeather-50m

54.5M params. Pretrain only. 17,500 steps banked on FineWeb-edu before Thunder credits ran dry. About 2.3B tokens, no SFT yet.

Architecture: Gemma-4 alternating sliding/global attention (1024 window, last layer always global) plus DeepSeek-V4 Muon optimizer plus WSD scheduler plus Gemma-2 logit soft-cap plus PaLM z-loss. Recipe in the model card.

What it can do: writes grammatical English. Knows that France has Rhine-adjacent monasteries (it picked Rouen instead of Paris but the vocabulary is in there). Tells stories about Mr. Fabien.

What it can't do yet: facts, code, math. Base LM, no SFT, no instruction tuning.

The series:
Every additional training run becomes another model card here
Every model card gets a matching post on this profile
Continuation goes to Colab next, picking up from step 17500 out of 100k

Limited to one post a day on Hugging Face, so updates will trickle out at that pace. Follow [@Crownelius](@Crownelius ) and [@Crowfeather](

Crowfeather ) if you want to watch this thing learn in public. Next drop will either come with the finished pre-train or whatever step I land on before the bank takes my credit card away.

Graphs will be available on my NEXT model lol

-Shane

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