Post
What do you plan to use Muse for?
Yi Cui
onekq
AI & ML interests
Benchmark, Code Generation Model
Recent Activity
posted an update 29 minutes ago
What do you plan to use Muse for? repliedto their post 32 minutes ago
My takes on Jev
1. Very likely a small model. You can certainly pretrain, but I would grab an existing base model, say Qwen 3 class
2. The new RL method is a breakthrough, classification doesn't need to align with human preferences
3. The new output is an overstatement. It's just a new LM head. Of course autoregressive decoding can be used for classification: it takes just a few tokens to express the output. Think twice: are you sure classification doesn't need few-shot, CoT, or reasoning? All of these depend on auto-regressiveness
4. It carves out a market already existing, which is now served by oversized LLMs (hence overpaid), e.g. LLM as judge, labeling
5. Jevons effect will kick in, promoting more modeling efforts for small budget teams. It might even accelerate RSI repliedto their post 2 days ago
My takes on Jev
1. Very likely a small model. You can certainly pretrain, but I would grab an existing base model, say Qwen 3 class
2. The new RL method is a breakthrough, classification doesn't need to align with human preferences
3. The new output is an overstatement. It's just a new LM head. Of course autoregressive decoding can be used for classification: it takes just a few tokens to express the output. Think twice: are you sure classification doesn't need few-shot, CoT, or reasoning? All of these depend on auto-regressiveness
4. It carves out a market already existing, which is now served by oversized LLMs (hence overpaid), e.g. LLM as judge, labeling
5. Jevons effect will kick in, promoting more modeling efforts for small budget teams. It might even accelerate RSIOrganizations
posted an update 29 minutes ago
replied to their post 32 minutes ago
Can't fine-tune or prompt engineer this model so you have to accept its accuracy as is. Add other harnesses if the accuracy is not as good
replied to their post 2 days ago
Really no idea. Just wait for the open source ones to come out
posted an update 4 days ago
Post
3158
My takes on Jev
1. Very likely a small model. You can certainly pretrain, but I would grab an existing base model, say Qwen 3 class
2. The new RL method is a breakthrough, classification doesn't need to align with human preferences
3. The new output is an overstatement. It's just a new LM head. Of course autoregressive decoding can be used for classification: it takes just a few tokens to express the output. Think twice: are you sure classification doesn't need few-shot, CoT, or reasoning? All of these depend on auto-regressiveness
4. It carves out a market already existing, which is now served by oversized LLMs (hence overpaid), e.g. LLM as judge, labeling
5. Jevons effect will kick in, promoting more modeling efforts for small budget teams. It might even accelerate RSI
1. Very likely a small model. You can certainly pretrain, but I would grab an existing base model, say Qwen 3 class
2. The new RL method is a breakthrough, classification doesn't need to align with human preferences
3. The new output is an overstatement. It's just a new LM head. Of course autoregressive decoding can be used for classification: it takes just a few tokens to express the output. Think twice: are you sure classification doesn't need few-shot, CoT, or reasoning? All of these depend on auto-regressiveness
4. It carves out a market already existing, which is now served by oversized LLMs (hence overpaid), e.g. LLM as judge, labeling
5. Jevons effect will kick in, promoting more modeling efforts for small budget teams. It might even accelerate RSI
- 7 replies
posted an update 6 days ago
Post
66
Not your weights, not your products.
This doesn't feel true at the beginning, but suddenly becomes true after PMF
This doesn't feel true at the beginning, but suddenly becomes true after PMF
- 1 reply
replied to their post 11 days ago
posted an update 11 days ago
Post
4473
I turned off subagents in Claude Code. Am I a minority?
- 5 replies
replied to their post 23 days ago
Really appreciate everyone's knowledge sharing and critique here.
Isn't this a gloomy picture that no frontier model can be run on the edge? Smaller models are for workflows and automations.
We are all used to conversing with a frontier model and let it run things on your device. A weaker model will degrade your current experience hence no adoption.
replied to their post 24 days ago
Wow! thx for all the works here. I wish more people can see the methodology.
My Mac is only 16GB 😅 We need to find one of those people above who have big budget
https://www.ebay.com/itm/257712960417
posted an update 25 days ago
Post
997
My take on device-side inference: it's all about high bandwidth memory (thinking about it, this holds for the cloud too).
MacBooks enjoy incidental capacity of apple silicon, but per-device RAM is too low (16 to 24GB), only sufficient for a decent SLM. 512GB is the highest you can go (Kimi K2*). Counting MLX downloads of Kimi K2* on Huggingface, I estimate the user base to be <25K.
On the other hand, the newly debuted DGX station (Nvidia) has 748GB, which can fit in the latest Kimi, DS, and Qwen. Also the quantization options of CUDA is way better than MLX.
For high-end inferencing, I place my bet on workstations over Macs.
MacBooks enjoy incidental capacity of apple silicon, but per-device RAM is too low (16 to 24GB), only sufficient for a decent SLM. 512GB is the highest you can go (Kimi K2*). Counting MLX downloads of Kimi K2* on Huggingface, I estimate the user base to be <25K.
On the other hand, the newly debuted DGX station (Nvidia) has 748GB, which can fit in the latest Kimi, DS, and Qwen. Also the quantization options of CUDA is way better than MLX.
For high-end inferencing, I place my bet on workstations over Macs.
- 7 replies
posted an update 28 days ago
Post
163
My biggest takeaway from Ben Thompson interview is the legacy of this round of "bubble".
For railroad, it was the track, and dot com, fiber. Compared to these two, GPUs depreciate too fast.
The answer is power plants.
For railroad, it was the track, and dot com, fiber. Compared to these two, GPUs depreciate too fast.
The answer is power plants.
posted an update about 1 month ago
Post
2011
Back of the envelope calculation: Ox Alpha has been out for a week with 130K users and 7T tokens burned.
Method 1: Assuming Ox Alpha is the GLM 5.3 class, it has roughly the same active param count as DeepSeek V4 Pro (~40B vs 49B), derive the cost by the floor price DS has ever published.
Method 2: Assuming the users are concentrated within an 8-hour working window each day, derive number of H800 nodes needed (~700 nodes at $2/GPU-hour).
In both methods, I assume 90/10 IO split and 60% cache hit. Both methods come to $2M.
I think the ROI is awesome: (1) publicity and (2) data harvesting.
Method 1: Assuming Ox Alpha is the GLM 5.3 class, it has roughly the same active param count as DeepSeek V4 Pro (~40B vs 49B), derive the cost by the floor price DS has ever published.
Method 2: Assuming the users are concentrated within an 8-hour working window each day, derive number of H800 nodes needed (~700 nodes at $2/GPU-hour).
In both methods, I assume 90/10 IO split and 60% cache hit. Both methods come to $2M.
I think the ROI is awesome: (1) publicity and (2) data harvesting.
- 1 reply
replied to their post about 1 month ago
Whoever this is, they give away so much compute for a model launch
replied to their post about 1 month ago
Most likely
replied to their post about 1 month ago
Lots of SLMs, like the 27b one from Qwen
posted an update about 1 month ago
Post
108
I for one believe DeepMind will ship one more frontier model.
In any case GCP is irrelevant here. GDM should still have more compute than DeepSeek, Kimi, or GLM, maybe all of them combined.
In any case GCP is irrelevant here. GDM should still have more compute than DeepSeek, Kimi, or GLM, maybe all of them combined.
posted an update about 1 month ago
Post
3231
Lots of attentions are now on GPU residual value. I find car analogy to be useful.
* Both new and used cars can do the same job (you can run the latest model on 6-year-old A100)
* New cars are more efficient (higher performance per power draw)
* It's not the year, but mileage and maintenance
The last point is undeveloped. We need Carfax and KBB for GPUs.
And there will be lemon GPUs.
* Both new and used cars can do the same job (you can run the latest model on 6-year-old A100)
* New cars are more efficient (higher performance per power draw)
* It's not the year, but mileage and maintenance
The last point is undeveloped. We need Carfax and KBB for GPUs.
And there will be lemon GPUs.
- 1 reply