CodeSoft/slm-arena-data
270 kB
CodeSoft PRO
CodeSoft
AI & ML interests
Working on small language models
Recent Activity
reacted to Banaxi-Tech's post with 👀 about 3 hours ago
We're delaying BananaMind 2.1!
When BananaMind 2.1 Lite was almost done, we benchmarked it and the results we're worse than BananaMind 2 Mini.
We're going to spend alot more time in research on tiny models and then scaling up our techniques to the actual BananaMind 2.1 models!
We're also announcing these new models:
BananaMind 2.1 Coder: A 149M instruction tuned coder model trained on 75B tokens + 10B tokens of stack-v3-train.
BananaMind 2.1 Pico: A 1M parameter model trained on 22B tokens of data.
We also may release BananaMind 2.1 Large with around 100M parameters depending on how much compute we have.
Please give us a follow!
https://huggingface.co/BananaMind
@Banaxi-Tech
---
@vovaRL
@DedeProGames
liked a model about 21 hours ago
DALabCommunity/Haidass1.5-143M upvoted a paper about 22 hours ago
On-Policy Self-Distillation in Diffusion ModelsOrganizations
Posts 5
Post
56
Over the past week or so, I've been working on some models, those released being CodeSoft/sorbet-25m and CodeSoft/sorbet-v2-25m. In general, I'm a little confused because no matter what hyperparameters I change or datasets I add/remove, the benchmarks never move up.
In a recent project, where I attached a TN-gram block to Sorbet-v2-25M, it still stayed the same on benchmarks despite the TN-gram clearly learning (due to the perplexity being lower with the TN-gram attached). When I changed the corpus to favor higher density text (the first paragraphs of Wikipedia articles and synthetic math), the benchmarks either stayed flat or went down.
Does anyone have ideas on what I can do to improve my models? I'd really appreciate any feedback!
In a recent project, where I attached a TN-gram block to Sorbet-v2-25M, it still stayed the same on benchmarks despite the TN-gram clearly learning (due to the perplexity being lower with the TN-gram attached). When I changed the corpus to favor higher density text (the first paragraphs of Wikipedia articles and synthetic math), the benchmarks either stayed flat or went down.
Does anyone have ideas on what I can do to improve my models? I'd really appreciate any feedback!
Post
2283
Wow, SLM Arena is getting a lot of traffic! Thank you guys for showing your interest!
To handle the growing demand, I’m moving SLM Arena from a CPU Space to a ZeroGPU Space. Hopefully, this will let me add more models to SLM Arena while keeping it running fast.
I've also added a separate arena + leaderboard for base models!
If there are any models or features you’d like to see, let me know in a reply to this post or in a Community post on the Space!
To handle the growing demand, I’m moving SLM Arena from a CPU Space to a ZeroGPU Space. Hopefully, this will let me add more models to SLM Arena while keeping it running fast.
I've also added a separate arena + leaderboard for base models!
If there are any models or features you’d like to see, let me know in a reply to this post or in a Community post on the Space!
models 6
CodeSoft/sorbet-v2-25m
Text Generation • 25.2M • Updated • 1.21k
CodeSoft/sorbet-25m-gguf
Text Generation • 25.2M • Updated • 101
CodeSoft/sorbet-25m
Text Generation • 25.2M • Updated • 1.13k • 4
CodeSoft/MetaDiffusion-150M-ChatBase
Text Generation • 0.2B • Updated • 932 • 2
CodeSoft/MetaDiffusion-600M-ChatBase
Text Generation • 0.8B • Updated • 571 • 1
CodeSoft/MetaDiffusion-150M-exp
Text Generation • 0.2B • Updated • 413 • 2
datasets 0
None public yet