QEDOC ANANAB DNIM
NOOS
The Commission shall adopt delegated acts in accordance with Article 21 of this Regulation.
We are
Translated from es by facebook/nllb-200-distilled-600M
Daniel B. PRO
FlameF0X
AI & ML interests
Pre-training text generator.
(Brother, im 18)
Please don't try to contact me.
Recent Activity
new activity about 9 hours ago
bench-labs/cagliostro-v3:AGI? updated a Space about 9 hours ago
FlameF0X/Chess-AGI updated a dataset about 10 hours ago
FlameF0X/chess-agi-resultsOrganizations
replied to Banaxi-Tech's post 4 days ago
reacted to SeaWolf-AI's post with 🔥 6 days ago
Post
5615
Instead of making the fly brain play games, we measured what it is for
Since the Drosophila connectome was released, people have had the fly brain doomscroll a feed, play Beat Saber, drive in GTA. Those demos show that the brain runs. We wanted to show what it is for.
So we gave it a looming object — one of the few things a fly brain is unambiguously built to detect — then deleted a single cell type and repeated the identical stimulus. Remove LC4, 126 cells out of 173,023, and the escape signal falls from 0.840 to 0.091. Eighty-nine percent of the danger signal is gone while the other 172,897 neurons run exactly as before.
Deleting neurons does not do this on its own, which is the whole point of the controls. LC11 is the same class and larger than LC4 — 143 cells and 9,940 outgoing connections against 126 and 7,846 — and removing every one of them changes the signal by 0.000000, to six decimal places. It has to be those 126.
No server and no GPU: a looming stimulus drives fewer than one percent of neurons above threshold, so the whole thing is 40 KB gzipped and runs in your browser.
Try deleting one yourself →
VIDraft/fruitfly-brain
The wiring is the measured connectome, but synaptic strength is a uniform count-based value and the dynamics are a firing-rate model of our choosing — a total-effect measurement of a model, not a recording from a fly. Male CNS connectome, FlyEM / HHMI Janelia with Google Research, Columbia and Harvard (2026), CC BY.
Since the Drosophila connectome was released, people have had the fly brain doomscroll a feed, play Beat Saber, drive in GTA. Those demos show that the brain runs. We wanted to show what it is for.
So we gave it a looming object — one of the few things a fly brain is unambiguously built to detect — then deleted a single cell type and repeated the identical stimulus. Remove LC4, 126 cells out of 173,023, and the escape signal falls from 0.840 to 0.091. Eighty-nine percent of the danger signal is gone while the other 172,897 neurons run exactly as before.
Deleting neurons does not do this on its own, which is the whole point of the controls. LC11 is the same class and larger than LC4 — 143 cells and 9,940 outgoing connections against 126 and 7,846 — and removing every one of them changes the signal by 0.000000, to six decimal places. It has to be those 126.
No server and no GPU: a looming stimulus drives fewer than one percent of neurons above threshold, so the whole thing is 40 KB gzipped and runs in your browser.
Try deleting one yourself →
VIDraft/fruitfly-brain
The wiring is the measured connectome, but synaptic strength is a uniform count-based value and the dynamics are a firing-rate model of our choosing — a total-effect measurement of a model, not a recording from a fly. Male CNS connectome, FlyEM / HHMI Janelia with Google Research, Columbia and Harvard (2026), CC BY.
replied to Banaxi-Tech's post 10 days ago
We need to wait for ARC-AGI 4, if astra is actually agi it should max on that too, else no agi.
reacted to Hoglet-33's post with 😔 12 days ago
Post
3192
Today, we planned to release Pebble-50M and Pebble-50M-Chat to the world. Unfortunately, due to a few issues, that didn't go quite as planned.
What happened:
- Some data and benchmark results were lost or corrupted
- The models performed worse on benchmarks than our other Pebble models
Despite that, you can still find both models here:
Pebble-50M-beta: basically-experimental/Pebble-50M-beta
Pebble-50M-Chat-beta: basically-experimental/Pebble-50M-Chat-beta
There are still some interesting improvements in these models:
- Compatible with non-CUDA devices
- Vocabulary increased to 16K tokens
- Context length increased to 16K tokens
For now, there won't be any more Pebble releases for a while. We're going to take some time to experiment with other approaches and hopefully make the next generation a monumental leap over this one.
Follow for updates:
@Hoglet-33
basically-ai
basically-experimental
What happened:
- Some data and benchmark results were lost or corrupted
- The models performed worse on benchmarks than our other Pebble models
Despite that, you can still find both models here:
Pebble-50M-beta: basically-experimental/Pebble-50M-beta
Pebble-50M-Chat-beta: basically-experimental/Pebble-50M-Chat-beta
There are still some interesting improvements in these models:
- Compatible with non-CUDA devices
- Vocabulary increased to 16K tokens
- Context length increased to 16K tokens
For now, there won't be any more Pebble releases for a while. We're going to take some time to experiment with other approaches and hopefully make the next generation a monumental leap over this one.
Follow for updates:
@Hoglet-33
- 4 replies
replied to their post 12 days ago
I ran the evaluation on colab's t4
reacted to appvoid's post with 😔 14 days ago
Post
3864
We got gpt6 before gta6
- 15 replies
posted an update 14 days ago
Post
2974
Hello HuggingFace! (UPDATE)
I tested the current architect of FWKV/Myosotis-1-base (that beinng FWKV) @ different sized and sequence lengths among RWKV and Transformer architecture. I did not include Mamba since that would require a costume kernel.
Note: the evaluation might not be accurate.
CSV avalible @ FlameF0X/evals
I tested the current architect of FWKV/Myosotis-1-base (that beinng FWKV) @ different sized and sequence lengths among RWKV and Transformer architecture. I did not include Mamba since that would require a costume kernel.
Note: the evaluation might not be accurate.
CSV avalible @ FlameF0X/evals
- 11 replies
reacted to branikita's post with 🔥 16 days ago
Post
2221
SO-ARM 102 goes open source in the next few weeks.
What's new:
- A parallel gripper: the jaws stay parallel through the whole stroke instead of pivoting around the object as they close.
- PET-CF instead of PLA+ for a much stiffer frame.
- A topology-optimized structure.
- Wider joint rotation and folding range.
- STS3250 servos on the first shoulder joint.
Compared with the SO-ARM 101, that adds up to 2.5x the payload, roughly 2x better positioning accuracy, roughly 1.6x the movement speed, and about 36 mm more reach.
It runs on Hugging Face LeRobot, so the same tooling, training pipeline and tutorials for the SO-ARM 101 work on it from day one.
What's new:
- A parallel gripper: the jaws stay parallel through the whole stroke instead of pivoting around the object as they close.
- PET-CF instead of PLA+ for a much stiffer frame.
- A topology-optimized structure.
- Wider joint rotation and folding range.
- STS3250 servos on the first shoulder joint.
Compared with the SO-ARM 101, that adds up to 2.5x the payload, roughly 2x better positioning accuracy, roughly 1.6x the movement speed, and about 36 mm more reach.
It runs on Hugging Face LeRobot, so the same tooling, training pipeline and tutorials for the SO-ARM 101 work on it from day one.
- 1 reply
posted an update 16 days ago
Post
137
I was playing around with FWKV/FWKV-Image (A Transformers FWKV hybrid) trying to see if i can do some LoRA on it and it was somewhat of a success, this leading to FWKV/FWKV-Image-Hibiscus-LoRA a model trianed on HuggingEnvs/watercolour-reference-pool . The results are decent. I attach a image with a few samples.
This was more of a fun experiment than actually research. 🤗
This was more of a fun experiment than actually research. 🤗
reacted to kostakoff's post with ➕ 16 days ago
Post
3054
Canceling My Pro Subscription
I'm officially canceling my Hugging Face Pro subscription today.
I supported this platform because it stood for true openness and neutrality. This acquisition by NVIDIA fundamentally changes that.
Here’s why I’m against this deal:
- Neutrality is dead. NVIDIA is a US-based company. This means US regulations will inevitably dictate platform policies, creating direct pressure on Chinese developers and anyone building open-weight models outside the US.
- Community over bureaucracy. NVIDIA is a massive, slow-moving corporation. This acquisition will likely drown the community in corporate processes and commercial interests. Soon, uploading a simple finetune might become a bureaucratic nightmare.
- Open vs. Proprietary. Hugging Face was built on open-source ideals. NVIDIA? They are a fiercely proprietary hardware company with a minimal track record of meaningful open-source contributions. They sell chips, not freedom.
- And to add insult to injury, NVIDIA has practically abandoned consumer RTX GPUs in 2026 to chase data center profits. Why would I pay them for "openness" when they've turned their back on the very developers who built this ecosystem?
I paid for openness. Not for a corporate takeover.
🤗 was about community.
I'm officially canceling my Hugging Face Pro subscription today.
I supported this platform because it stood for true openness and neutrality. This acquisition by NVIDIA fundamentally changes that.
Here’s why I’m against this deal:
- Neutrality is dead. NVIDIA is a US-based company. This means US regulations will inevitably dictate platform policies, creating direct pressure on Chinese developers and anyone building open-weight models outside the US.
- Community over bureaucracy. NVIDIA is a massive, slow-moving corporation. This acquisition will likely drown the community in corporate processes and commercial interests. Soon, uploading a simple finetune might become a bureaucratic nightmare.
- Open vs. Proprietary. Hugging Face was built on open-source ideals. NVIDIA? They are a fiercely proprietary hardware company with a minimal track record of meaningful open-source contributions. They sell chips, not freedom.
- And to add insult to injury, NVIDIA has practically abandoned consumer RTX GPUs in 2026 to chase data center profits. Why would I pay them for "openness" when they've turned their back on the very developers who built this ecosystem?
I paid for openness. Not for a corporate takeover.
🤗 was about community.
- 12 replies
reacted to sergiopaniego's post with 🔥 17 days ago
Post
2063
Can you do RL over taste?
I've spent some time reproducing, in the open, Surya N's idea of training a model to paint with code. It's a coding model that learns to paint watercolours by writing JS code, trained with GRPO. I used TRL and OpenEnv for this, with the whole pipeline running on Hugging Face.
The interesting part is that the reward has no correct answer, unlike a math problem. In this case it's based on the artistic preferences of the person who builds the dataset.
Everything is published: the environment, the reference pool, the trained adapters, every painting of every run with the code that made it, and a write-up with all the decisions, including the ones that went wrong.
Blog post: https://huggingface.co/blog/train-to-paint-with-code
I've spent some time reproducing, in the open, Surya N's idea of training a model to paint with code. It's a coding model that learns to paint watercolours by writing JS code, trained with GRPO. I used TRL and OpenEnv for this, with the whole pipeline running on Hugging Face.
The interesting part is that the reward has no correct answer, unlike a math problem. In this case it's based on the artistic preferences of the person who builds the dataset.
Everything is published: the environment, the reference pool, the trained adapters, every painting of every run with the code that made it, and a write-up with all the decisions, including the ones that went wrong.
Blog post: https://huggingface.co/blog/train-to-paint-with-code
reacted to Hoglet-33's post with 🔥 17 days ago
Post
3442
Pebble-25M and Pebble-25M-Chat are out now!
We’re excited to release Pebble-25M and Pebble-25M-Chat!
Both models use our 3:1 Mamba2/Transformer hybrid architecture and were pretrained on 25B tokens. Pebble-25M-Chat was then further fine-tuned on an additional 250M tokens from smol-smoltalk, following the same approach used for the Pebble-10M models.
We hope you enjoy experimenting with them!
Pebble-50M is coming in a few days.
Models
Pebble-25M: basically-ai/Pebble-25M
Pebble-25M-Chat: basically-ai/Pebble-25M-Chat
Pebble-10M GGUFs
In case you missed it, our friend @ContextReq made GGUF versions of the Pebble-10M models:
https://huggingface.co/ContextReq/Pebble-10M-GGUF
https://huggingface.co/ContextReq/Pebble-10M-Chat-GGUF
Follow us if you don’t want to miss future releases and updates!
@Hoglet-33
basically-ai
basically-experimental
We’re excited to release Pebble-25M and Pebble-25M-Chat!
Both models use our 3:1 Mamba2/Transformer hybrid architecture and were pretrained on 25B tokens. Pebble-25M-Chat was then further fine-tuned on an additional 250M tokens from smol-smoltalk, following the same approach used for the Pebble-10M models.
We hope you enjoy experimenting with them!
Pebble-50M is coming in a few days.
Models
Pebble-25M: basically-ai/Pebble-25M
Pebble-25M-Chat: basically-ai/Pebble-25M-Chat
Pebble-10M GGUFs
In case you missed it, our friend @ContextReq made GGUF versions of the Pebble-10M models:
https://huggingface.co/ContextReq/Pebble-10M-GGUF
https://huggingface.co/ContextReq/Pebble-10M-Chat-GGUF
Follow us if you don’t want to miss future releases and updates!
@Hoglet-33
- 7 replies
reacted to juiceb0xc0de's post with 🔥 18 days ago
Post
3436
Hey I've updated my Hugging Face text generation model search space B-Sides. I always wanted more from HF's model search, so I built one.
I went deeper than the model card, embedding the relevant .json and .py files so you can search for models with custom kernels or exotic imports and specific architecture shapes. You can narrow it down to quantization types and training stacks. If you want to search it and it's not available just make a community post and I would gladly make each query more detailed with an update.
So far over 428,440 text generation models are catalogued with more coming weekly.
On deck: Docker images
https://huggingface.co/spaces/juiceb0xc0de/b-sides
I went deeper than the model card, embedding the relevant .json and .py files so you can search for models with custom kernels or exotic imports and specific architecture shapes. You can narrow it down to quantization types and training stacks. If you want to search it and it's not available just make a community post and I would gladly make each query more detailed with an update.
So far over 428,440 text generation models are catalogued with more coming weekly.
On deck: Docker images
https://huggingface.co/spaces/juiceb0xc0de/b-sides
- 4 replies
reacted to Hoglet-33's post with 🔥 20 days ago
Post
3305
Pebble 10M and Pebble 10M Chat are now released!
Both models use our Mamba/Transformer 3:1 hybrid architecture and were pretrained on 25 billion tokens.
Pebble 10M Chat was additionally fine-tuned on 250 million tokens of Smol-SmolTalk to improve its conversational capabilities.
You can find them here:
- basically-ai/Pebble-10M
- basically-ai/Pebble-10M-Chat
We hope you enjoy using them. The rest of the Pebble family will be released soon.
Follow for more:
@Hoglet-33
basically-ai
Both models use our Mamba/Transformer 3:1 hybrid architecture and were pretrained on 25 billion tokens.
Pebble 10M Chat was additionally fine-tuned on 250 million tokens of Smol-SmolTalk to improve its conversational capabilities.
You can find them here:
- basically-ai/Pebble-10M
- basically-ai/Pebble-10M-Chat
We hope you enjoy using them. The rest of the Pebble family will be released soon.
Follow for more:
@Hoglet-33
- 32 replies
replied to their post 20 days ago
I guess
replied to their post 20 days ago
Linear attention.
replied to their post 20 days ago
RNNs are a bit much harder to train than Transformers, but is worth the time for those sweet inference speeds 🤗
replied to their post 20 days ago
How many tokens did this use?
To train the model? around 2B.
replied to their post 20 days ago
Post wrote by a human (me), and typo corrected by AI.
posted an update 20 days ago
Post
2628
Hello HuggingFace!
I would like to share a small preview of a language model that I have been experiencing for a while. In the first attached image there is a small sample of the Myosotis-1, an attempt to make a small 100m parameter flagship model that is built on my bizarre architecture that is somewhat similar to an S4/S5 model with WKV added. I call it FWKV (Feed-Forward WKV). The model is currently still in training because of the nature of RNN-like models. It can also be seen that the model has insane prompt processing and token generation speed (evaluation done on a 2x Titan XP); even for its small size, some similar Transformer models do struggle to get the same results without custom kernels (some Transformer models can achieve this level of throughput on cheap hardware).
In the second image, you can see checkpoint 20k of the model in its next token prediction state (this means it can't chat), ranking in the top 100 on AxiomicLabs/Open_SLM_Leaderboard (the results have not been submitted since the model is not done training).
- Why not just use Transformers?
Have you seen any pure non-Transformers SLMs besides RWKV and Mamba?
- Should you expect this project to become the next LFM or another very fast language model thing on some Raspberry Pi?
No, the model is still an experiment; it's very sensible and prone to collapse (by the time of this post, it can be seen in the 1st image).
- Should you use it?
Maybe not yet; the architecture itself is still very "naive"—that's how I could call it at its current level. If you just want to play with it and see what you could do or how fast the model is on your hardware, then you can do it.
Once the training is finished and I feel satisfied with the model next token prediction (the base model) and "assistants" (the instruction-tuned model) capabilities, I will make open weights at FWKV with full support of the 🤗 Transformers.
I would like to share a small preview of a language model that I have been experiencing for a while. In the first attached image there is a small sample of the Myosotis-1, an attempt to make a small 100m parameter flagship model that is built on my bizarre architecture that is somewhat similar to an S4/S5 model with WKV added. I call it FWKV (Feed-Forward WKV). The model is currently still in training because of the nature of RNN-like models. It can also be seen that the model has insane prompt processing and token generation speed (evaluation done on a 2x Titan XP); even for its small size, some similar Transformer models do struggle to get the same results without custom kernels (some Transformer models can achieve this level of throughput on cheap hardware).
In the second image, you can see checkpoint 20k of the model in its next token prediction state (this means it can't chat), ranking in the top 100 on AxiomicLabs/Open_SLM_Leaderboard (the results have not been submitted since the model is not done training).
- Why not just use Transformers?
Have you seen any pure non-Transformers SLMs besides RWKV and Mamba?
- Should you expect this project to become the next LFM or another very fast language model thing on some Raspberry Pi?
No, the model is still an experiment; it's very sensible and prone to collapse (by the time of this post, it can be seen in the 1st image).
- Should you use it?
Maybe not yet; the architecture itself is still very "naive"—that's how I could call it at its current level. If you just want to play with it and see what you could do or how fast the model is on your hardware, then you can do it.
Once the training is finished and I feel satisfied with the model next token prediction (the base model) and "assistants" (the instruction-tuned model) capabilities, I will make open weights at
- 10 replies