191–200 of 412 posts
Gotta love how Claude is always conventiently left out of all of these benchmark lists. Anthropic really is in a league of their own right now.
Gemini Pro 2.5 usually beats Sonnet 3.7 at coding.
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I similarly have a small, simple spatial reasoning problem that only reasoning models get right, and not all of them, and which Qwen3 on max reasoning still gets wrong. > I put a coin in a cup and slam it upside-down on a glass table. I can't see the coin because the cup is over it. I slide a mirror under the table and see heads. What will I see if I take the cup (and the mirror) away?
Yup, it flunked that one. I also have a question that LLMs always got wrong until ChatGPT o3, and even then it has a hard time (I just tried it again and it needed to run code to work it out). Qwen3 failed, and every time I asked it to look again at its solution it would notice the error and try to solve it again, failing again: > A man wants to cross a river, and he has a cabbage, a goat, a wolf and a lion. If he le…
I have a small physics-based problem I pose to LLMs. It's tricky for humans as well, and all LLMs I've tried (GPT o3, Claude 3.7, Gemini 2.5 Pro) fail to answer correctly. If I ask them to explain their answer, they do get it eventually, but none get it right the first time. Qwen3 with max thinking got it even more wrong than the rest, for what it's worth.
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This 61GB one: https://ollama.com/library/qwen3:30b-a3b-fp16 You can see it's roughly the same size as the one in the official repo (16 files of 4GB each): https://huggingface.co/Qwen/Qwen3-30B-A3B/tree/main
fp16 is overkill though. 8-bit is the sweet spot before perf degradation starts getting noticeable.
The large MoE could be the DeepSeek V3 for people with just 128gb of (V)RAM.
The smallest quantized version of the large MoE model on ollama is 143GB: https://ollama.com/library/qwen3:235b-a22b-q4_K_M Is there a smaller one?
The large MoE could be the DeepSeek V3 for people with just 128gb of (V)RAM.
The smallest quantized version of the large MoE model on ollama is 143GB: https://ollama.com/library/qwen3:235b-a22b-q4_K_M Is there a smaller one?
[1] - https://huggingface.co/unsloth/Qwen3-235B-A22B-GGUF/tree/mai...
With all the different open-weight models appearing, is there some way of figuring out what model would work with sensible speed (> X tok/s) on a standard desktop GPU ? I.e. I have Quadro RTX 4000 with 8G vram and seeing all the models https://ollama.com/search here with all the different sizes, I am absolutely at loss which models with which sizes would be fast enough. I.e. there is no point of me downloading the la…
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these 0.5 and 0.6B models etc. are _fantastic_ for using as a draft model in speculative decoding. lm studio makes this super easy to do - i have it on like every model i play with now my concern on these models though unfortunately is it seems like architectures very a bit so idk how it'll work
Spec decoding only depends on the tokenizer used. It's transfering either the draft token sequence or at most draft logits to the main model.
It's interesting that the release happened at 5am in China. Quite unusual.
I have a small physics-based problem I pose to LLMs. It's tricky for humans as well, and all LLMs I've tried (GPT o3, Claude 3.7, Gemini 2.5 Pro) fail to answer correctly. If I ask them to explain their answer, they do get it eventually, but none get it right the first time. Qwen3 with max thinking got it even more wrong than the rest, for what it's worth.
I similarly have a small, simple spatial reasoning problem that only reasoning models get right, and not all of them, and which Qwen3 on max reasoning still gets wrong. > I put a coin in a cup and slam it upside-down on a glass table. I can't see the coin because the cup is over it. I slide a mirror under the table and see heads. What will I see if I take the cup (and the mirror) away?
I'll think this through step by step. When you place a coin in a cup and slam it upside-down on a glass table, the coin will be between the table surface and the cup. When you look at the reflection in the mirror beneath the table, you're seeing the bottom side of the coin through the glass. Since the mirror shows heads, you're seeing the heads side of the coin reflected in the mirror. In a mirror, the image is reversed left-to-right, but the "heads" and "tails" sides of a coin would not be flipped in this reflection - you'd still recognize heads as heads. If you take away both the cup and the mirror, you'll see the opposite side of the coin compared to what you saw in the mirror. Since the mirror showed heads, you'll see tails when looking directly at the coin from above the table.