Entries for September 10, 2026
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Who would like to see similar RL work for training a small model to play Settlers of Catan? IMO Catan is an ideal game for an LLM to play. The trading and verbal communication aspect means that an LLM can form alliances, scheme, play byzantine games and affect game state in a way a non-language model cannot The action space is like a mix of backgammon and Diplomacy (see CICERO by Meta AI from a few years ago) I happen to have contact at a platform that could lend me millions of gameplay data, to bootstrap SFT. If this gets enough attention, they might also participate and we could shoot a series where we train and play with this live 🤩 So many possibilities to experiment with, and would be an excuse for me to to do cool RL! Let me know in the replies if you would like to see such a series! -
WAIT, THERE IS STILL HOPEImage hidden
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"Fair challenge", "Fair — that's on me", "That error is the whole bug", "Short answer: no. Not honestly" I've been hit by top 4 claudisms in the first 4 messages with DeepSeek V4.1 Flash I hate to be the bearer of bad news but this model is gonna SPREAD and it's gonna bring Claudish everywhere it goes 😭 Just when I thought the situation started to improve with OAI's newer generation of models...
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DeepSeek V4.1 Flash is NOT an LLM It is a hyper-efficient, invasive species designed to displace every other LLM, similar to how Döner displaced all other fast food in Germany As if V4 wasn't cheap and efficient enough, they made it a LOT cheaper 2.3x cheaper cached input tokens???? 1.5x cheaper uncached input and 10% cheaper output??? Are you for real? DS 4.1 Flash matches Sol in benchmark numbers It is size-wise probably comparable to Terra (I don't know how big it is...) Yet it is 33~66x cheaper than Terra and 66~133x cheaper than Sol depending on peak/off-peak hours. THAT IS TWO ORDER OF MAGNITUDE In other words, DeepSeek absolutely MOGS OpenAI in unit economics. See why in my previous post below Let's see what the vibes will say, but efficiency-wise my mind is blown
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DeepSeek-V4.1-Flash is available on Hugging Face inference providers through @novita_labs on Hugging Chat and it is FLYING at >180 tok/s I asked it my classic prompt "they say you are sota. prove it", and it created this mandelbulb. I think this demo became a sort of cliche at this point Also, it hit me with "Fair challenge" right away 😭 I thought the Chinese labs didn't need to distill Claude anymore... Could Claudish be a universal platonic feature 🤔 (lol jk) Use it through Hugging Chat:
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@NoemiTitarenco Says "the smallest model in our new architecture family" 😭
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DeepSeek V4.1 Flash weights are out! ⚡️⚡️⚡️ I have good news and bad news Don't be fooled by "V4".1, this is a different architecture V4 was 284B total, 13B active V4.1 has a 552B MoE backbone + 196B engram conditional-memory parameters with 16B active parameters Bad news first. A single DGX Spark will likely not be able to hold all those parameters 😢 You will likely need 2 Sparks, or in general, a workstation with 256 GB. Could be a good time to get a loan... (not financial advice) DeepSeek has never claimed they were building for local use, but with this architecture, they show us that their main priority is for very efficient use in datacenters, with HBM, not local Good news: Looking at active parameters, you might be bummed out that decode will be slower on this, at 13/16 ~ 81% of the speed of V4 But wait!!! KV cache is 4x smaller. So decoding on this model will be a lot faster with concurrent sessions. And with speculative decoding, it seems like it might have 2-4x the throughput at scale, compared to V4. Back of the envelope calculation, I might pull back this prediction The small KV cache will also do something good for local inference, but I need more time to calculate how much. Take these with a grain of salt. Give your agent my formulation, and let me know if it looks like I made an error somewhere: solmaz.io/llm-throughput-upper-bounds Original safetensors: huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash
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Running into out-of-memory freeze issues and having to hard-reset your DGX Spark while trying the gazillions of models you can download from Hugging Face? I had that problem as well, and built infer-guard to solve that. I did not need to hard reset my DGX Spark in months Repo:Image hidden
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This is the mental model I have of AI since years and have used this metaphor in discussions I imagine compute as barrels of gasoline, and we are all in a room, piling up more and more compute, waiting for something to light it up You can also imagine a dried up forest to be closer to Taleb's antifragility argument At some point, it might go boom (I won't say foom, to not imply fast take-off, but rather, a shock to the world system) Aiming for more frequent, smaller, controlled booms in the forms of accidents like OpenAI's would arguably let the world to adapt better to changes, than a single, delayed boom I am not yet sure what position I lean into. But the world's governments + industry are a complex system and should not be treated as something to be controlled easily