Post
2568
Nobody knows what is doing, when you train a model, you are experimenting to advance the frontier, so keep failing 🫵
- 17 replies
♾️ Thinking
Wop
wop
AI & ML interests
AI research
AGI
Recent Activity
liked a model about 18 hours ago
bench-labs/cagliostro-v2 liked a model 3 days ago
Qwen/Qwen3.8-Flash-NextOrganizations
reacted to appvoid's post with 🔥 2 days ago
reacted to salma-remyx's post with 🔥🔥🔥 3 days ago
Post
2681
If you can’t explain why your AI-generated contribution belongs in the repo, don’t put it in a maintainer’s queue 🙅🏻♀️
Before asking for review, you should be able to answer:
1. What project need does it address?
2. Where does it fit, and does it duplicate existing work?
3. What evidence shows it works, and will you own it through review?
If you can’t answer those, you haven’t saved anyone time. You’ve passed the buck to the maintainer.
Outrider has made us better contributors by doing more of this work before upstream review by
* reading contribution rules, accepted PRs, and open issues
* finding needs and integration points
* drafting the code, tests, and context.
We still decide what deserves to go upstream, verify the claims, coordinate with maintainers and contributors, and stay involved through review.
On huggingface/peft, only 4 of 20 Outrider runs opened draft PRs. Two contributions have now merged:
✅ Riemannian-preconditioned LoRA: https://github.com/huggingface/peft/pull/3382
✅ Super-Tuning: https://github.com/huggingface/peft/pull/3518
We have more contributions in review and far more ideas were filtered out before they reached a maintainer.
Full case study: https://remyx.ai/case-study
Outrider: https://github.com/remyxai/outrider
Before asking for review, you should be able to answer:
1. What project need does it address?
2. Where does it fit, and does it duplicate existing work?
3. What evidence shows it works, and will you own it through review?
If you can’t answer those, you haven’t saved anyone time. You’ve passed the buck to the maintainer.
Outrider has made us better contributors by doing more of this work before upstream review by
* reading contribution rules, accepted PRs, and open issues
* finding needs and integration points
* drafting the code, tests, and context.
We still decide what deserves to go upstream, verify the claims, coordinate with maintainers and contributors, and stay involved through review.
On huggingface/peft, only 4 of 20 Outrider runs opened draft PRs. Two contributions have now merged:
✅ Riemannian-preconditioned LoRA: https://github.com/huggingface/peft/pull/3382
✅ Super-Tuning: https://github.com/huggingface/peft/pull/3518
We have more contributions in review and far more ideas were filtered out before they reached a maintainer.
Full case study: https://remyx.ai/case-study
Outrider: https://github.com/remyxai/outrider
- 3 replies
reacted to danielhanchen's post with 🔥 19 days ago
Post
5456
Introducing Unsloth Desktop 🦥
The first desktop app to run and train models locally.
• Open-source. Runs on Mac, Windows and Linux
• Supports MLX, diffusion image/video, audio, GGUF
• Connect Claude Code and Codex to local LLMs
• 50% more accurate, self-healing tool calls + sandboxed code exec
• Works for CPU + multiGPU setups - NVIDIA, AMD, Intel, Mac
• Train models 2× faster with 70% less VRAM
• Private web search, deep research, RAG, MCP and exports (NVFP4, GGUF)
• Use Unsloth’s OpenAI-compatible API and cloud models
• Securely deploy LLMs remotely and access anywhere
Unsloth Desktop is now available on http://unsloth.ai
and GitHub.
GitHub: https://github.com/unslothai/unsloth
Blog and Guide: https://unsloth.ai/docs/desktop
The first desktop app to run and train models locally.
• Open-source. Runs on Mac, Windows and Linux
• Supports MLX, diffusion image/video, audio, GGUF
• Connect Claude Code and Codex to local LLMs
• 50% more accurate, self-healing tool calls + sandboxed code exec
• Works for CPU + multiGPU setups - NVIDIA, AMD, Intel, Mac
• Train models 2× faster with 70% less VRAM
• Private web search, deep research, RAG, MCP and exports (NVFP4, GGUF)
• Use Unsloth’s OpenAI-compatible API and cloud models
• Securely deploy LLMs remotely and access anywhere
Unsloth Desktop is now available on http://unsloth.ai
and GitHub.
GitHub: https://github.com/unslothai/unsloth
Blog and Guide: https://unsloth.ai/docs/desktop
- 6 replies
reacted to their post with 🔥 20 days ago
Post
2177
bench-labs/GCTokenizer-v1 , a multilingual tokenizer which does not require a training corpus
bench-labs developed **GCTokenizer-v1**, which is a multi-lingual tokenizer
Available in four sizes: 32K, 65K, 131K and 262K tokens "S, M, L, XL"
It utilizes an encoding scheme which allows it to handle characters in any language around the world
General (multi lingual)
Consensus (from multiple model tokenizers consensus)
Tokenizer
We included an implementation script too,
built like BPE- it can encode arbitrary text, most of the time, efficiently
Available in four sizes: 32K, 65K, 131K and 262K tokens "S, M, L, XL"
It utilizes an encoding scheme which allows it to handle characters in any language around the world
General (multi lingual)
Consensus (from multiple model tokenizers consensus)
Tokenizer
We included an implementation script too,
built like BPE- it can encode arbitrary text, most of the time, efficiently
posted an update 20 days ago
Post
2177
bench-labs/GCTokenizer-v1 , a multilingual tokenizer which does not require a training corpus
bench-labs developed **GCTokenizer-v1**, which is a multi-lingual tokenizer
Available in four sizes: 32K, 65K, 131K and 262K tokens "S, M, L, XL"
It utilizes an encoding scheme which allows it to handle characters in any language around the world
General (multi lingual)
Consensus (from multiple model tokenizers consensus)
Tokenizer
We included an implementation script too,
built like BPE- it can encode arbitrary text, most of the time, efficiently
Available in four sizes: 32K, 65K, 131K and 262K tokens "S, M, L, XL"
It utilizes an encoding scheme which allows it to handle characters in any language around the world
General (multi lingual)
Consensus (from multiple model tokenizers consensus)
Tokenizer
We included an implementation script too,
built like BPE- it can encode arbitrary text, most of the time, efficiently
posted an update 21 days ago
Post
2323
all models now also run on zerogpu!
BenchLabs/Demo-FAST
Speed measurements:
image ~5s (pixelmodel v5 ~0.5s)
3D ~2s
audio ~3s +(upload)
insane.
Yes, we made a new model: bench-labs/AudioModel-v1
BIG thanks to @TobiasLogic
BenchLabs/Demo-FAST
Speed measurements:
image ~5s (pixelmodel v5 ~0.5s)
3D ~2s
audio ~3s +(upload)
insane.
Yes, we made a new model: bench-labs/AudioModel-v1
BIG thanks to @TobiasLogic
reacted to appvoid's post with 🔥 22 days ago
Post
2386
I don't know if it was us or one of you guys or maybe all of us at once but lately we have seen a finetuning/pretraining explosion of models below 200m params and we can't be more happy about it keep coming tinkerers all of this is possible because of you!
- 8 replies
replied to their post 22 days ago
Yeah, that's basically it SlopFinder isnt defining slop, its trying to measure the perceived / averaged version of what humans call slop.
Kind of like human preference data: some of it's obviously objective (broken hands, logical inconsistency, obvious asset-flips like you mentioned), some of it's just taste (what's "enjoyable" varies person to person).
The dataset is the collection of a lot of different definitions like yours.
replied to their post 22 days ago
implemented almost all of these
reacted to their post with 🔥 22 days ago
Post
2321
🧪 SlopFinder is here!!
We're building a dataset to study what humans actually consider AI slop.
SlopFinder shows you a random piece of AI-generated text and gives you one simple control: **how slop is it?**
No categories. No complicated forms. Just vote and move on.
Every vote helps build the dataset. 🧩
How does it work?
Samples are pulled from existing datasets, shown anonymously, and collected into our annotation pool. After enough votes, they're exported to Hugging Face for everyone to use.
This is an early MVP, so the dataset is small and the system is still evolving.
Vote here:
https://bench-labs.web.app/slopfinder.html
(refresh page if you want to skip)
Dataset:
bench-labs/slop-classification
@benchlabs
We're building a dataset to study what humans actually consider AI slop.
SlopFinder shows you a random piece of AI-generated text and gives you one simple control: **how slop is it?**
No categories. No complicated forms. Just vote and move on.
Every vote helps build the dataset. 🧩
How does it work?
Samples are pulled from existing datasets, shown anonymously, and collected into our annotation pool. After enough votes, they're exported to Hugging Face for everyone to use.
This is an early MVP, so the dataset is small and the system is still evolving.
Vote here:
https://bench-labs.web.app/slopfinder.html
(refresh page if you want to skip)
Dataset:
bench-labs/slop-classification
@benchlabs
- 17 replies
replied to their post 22 days ago
This is growing pretty fast,I just uploaded another batch, theres now ~115 rows (before 85) 🔥
I will soon make an upgrade for better batching + some big improvements (context & diversity) and maybe some visual upgrade, and maybe auto upload🎉
I wonder what improvements you would like to see (maybe an API?) - tho its suposted not to be automated with AI, but voted by humans🤷🏻
replied to their post 22 days ago
Update: you can now also add custom conversations and score them!
reacted to their post with 🤗 23 days ago
Post
2321
🧪 SlopFinder is here!!
We're building a dataset to study what humans actually consider AI slop.
SlopFinder shows you a random piece of AI-generated text and gives you one simple control: **how slop is it?**
No categories. No complicated forms. Just vote and move on.
Every vote helps build the dataset. 🧩
How does it work?
Samples are pulled from existing datasets, shown anonymously, and collected into our annotation pool. After enough votes, they're exported to Hugging Face for everyone to use.
This is an early MVP, so the dataset is small and the system is still evolving.
Vote here:
https://bench-labs.web.app/slopfinder.html
(refresh page if you want to skip)
Dataset:
bench-labs/slop-classification
@benchlabs
We're building a dataset to study what humans actually consider AI slop.
SlopFinder shows you a random piece of AI-generated text and gives you one simple control: **how slop is it?**
No categories. No complicated forms. Just vote and move on.
Every vote helps build the dataset. 🧩
How does it work?
Samples are pulled from existing datasets, shown anonymously, and collected into our annotation pool. After enough votes, they're exported to Hugging Face for everyone to use.
This is an early MVP, so the dataset is small and the system is still evolving.
Vote here:
https://bench-labs.web.app/slopfinder.html
(refresh page if you want to skip)
Dataset:
bench-labs/slop-classification
@benchlabs
- 17 replies
posted an update 23 days ago
Post
2321
🧪 SlopFinder is here!!
We're building a dataset to study what humans actually consider AI slop.
SlopFinder shows you a random piece of AI-generated text and gives you one simple control: **how slop is it?**
No categories. No complicated forms. Just vote and move on.
Every vote helps build the dataset. 🧩
How does it work?
Samples are pulled from existing datasets, shown anonymously, and collected into our annotation pool. After enough votes, they're exported to Hugging Face for everyone to use.
This is an early MVP, so the dataset is small and the system is still evolving.
Vote here:
https://bench-labs.web.app/slopfinder.html
(refresh page if you want to skip)
Dataset:
bench-labs/slop-classification
@benchlabs
We're building a dataset to study what humans actually consider AI slop.
SlopFinder shows you a random piece of AI-generated text and gives you one simple control: **how slop is it?**
No categories. No complicated forms. Just vote and move on.
Every vote helps build the dataset. 🧩
How does it work?
Samples are pulled from existing datasets, shown anonymously, and collected into our annotation pool. After enough votes, they're exported to Hugging Face for everyone to use.
This is an early MVP, so the dataset is small and the system is still evolving.
Vote here:
https://bench-labs.web.app/slopfinder.html
(refresh page if you want to skip)
Dataset:
bench-labs/slop-classification
@benchlabs
- 17 replies
reacted to appvoid's post with 🔥 23 days ago
Post
2904
If you want small models to be great again you should give a follow to people like @Banaxi-Tech or @Datdanboi25
These guys are rocking it with small models lately.
(They are not paying me to say that)
These guys are rocking it with small models lately.
(They are not paying me to say that)
- 22 replies
reacted to onekq's post with 👀 23 days ago
Post
2433
DeepSeek plans to raise token prices. I don't think this is because they are bleeding, but they are overwhelmed. If your price is 1/10 of your affiliate vendors, you can't leverage their resources. Markup is the only way to diverge traffic away.
Sadly I haven't found discussions on differentiators enabling DS to balance cost at such low prices. All software solutions (that we know of) are accessible by other vendors. If you attribute it to electricity or hardware, you can't explain why GLM and Kimi charge so much for their APIs.
This is where our attention should be (but distracted by things above).
Sadly I haven't found discussions on differentiators enabling DS to balance cost at such low prices. All software solutions (that we know of) are accessible by other vendors. If you attribute it to electricity or hardware, you can't explain why GLM and Kimi charge so much for their APIs.
This is where our attention should be (but distracted by things above).
- 5 replies
You should check out benchlabs, we have great text to image models
reacted to appvoid's post with 👀 23 days ago
Post
2904
If you want small models to be great again you should give a follow to people like @Banaxi-Tech or @Datdanboi25
These guys are rocking it with small models lately.
(They are not paying me to say that)
These guys are rocking it with small models lately.
(They are not paying me to say that)
- 22 replies