bartowski/Qwen2.5-14B-Instruct-1M-GGUF
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rtuuuuuuuur
urtuuuu
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Recent Activity
new activity
4 days ago
mistralai/Mistral-Small-24B-Instruct-2501:Optimal settings for running Small-24b using Ollama?
new activity
4 days ago
deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B:Using the Model
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7 days ago
Qwen/Qwen2.5-14B-Instruct-1M:Better than distilled version
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Optimal settings for running Small-24b using Ollama?
9
#14 opened 9 days ago
by
AaronFeng753
Using the Model
1
#14 opened 8 days ago
by
joel1610-hon
Better than distilled version
4
#5 opened 7 days ago
by
urtuuuu
liked
a
model
7 days ago
R1 32b is much worse than QwQ ...
22
#6 opened 18 days ago
by
mirek190
Poor performance in the leaderboard?
7
#17 opened 15 days ago
by
L29Ah
System Prompt
13
#2 opened 19 days ago
by
Wanfq
liked
a
model
18 days ago
System Prompt
7
#2 opened 19 days ago
by
Wanfq
Not impressed?
2
#2 opened 19 days ago
by
urtuuuu
liked
2
models
about 1 month ago
How can we thank you enough, whale bros?
10
#1 opened about 1 month ago
by
KrishnaKaasyap
liked
a
model
about 1 month ago
GGUFs eventually ?
6
#1 opened about 2 months ago
by
HMasaki
A bit annoying, isn't it? Some time ago I asked you for arm version of gemma-2-9b-it-abliterated. So now it won't work again. I guess there is no Q4_0 ?
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bartowski's
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about 2 months ago
Post
42168
Looks like Q4_0_N_M file types are going away
Before you panic, there's a new "preferred" method which is online (I prefer the term on-the-fly) repacking, so if you download Q4_0 and your setup can benefit from repacking the weights into interleaved rows (what Q4_0_4_4 was doing), it will do that automatically and give you similar performance (minor losses I think due to using intrinsics instead of assembly, but intrinsics are more maintainable)
You can see the reference PR here:
https://github.com/ggerganov/llama.cpp/pull/10446
So if you update your llama.cpp past that point, you won't be able to run Q4_0_4_4 (unless they add backwards compatibility back), but Q4_0 should be the same speeds (though it may currently be bugged on some platforms)
As such, I'll stop making those newer model formats soon, probably end of this week unless something changes, but you should be safe to download and Q4_0 quants and use those !
Also IQ4_NL supports repacking though not in as many shapes yet, but should get a respectable speed up on ARM chips, PR for that can be found here: https://github.com/ggerganov/llama.cpp/pull/10541
Remember, these are not meant for Apple silicon since those use the GPU and don't benefit from the repacking of weights
Before you panic, there's a new "preferred" method which is online (I prefer the term on-the-fly) repacking, so if you download Q4_0 and your setup can benefit from repacking the weights into interleaved rows (what Q4_0_4_4 was doing), it will do that automatically and give you similar performance (minor losses I think due to using intrinsics instead of assembly, but intrinsics are more maintainable)
You can see the reference PR here:
https://github.com/ggerganov/llama.cpp/pull/10446
So if you update your llama.cpp past that point, you won't be able to run Q4_0_4_4 (unless they add backwards compatibility back), but Q4_0 should be the same speeds (though it may currently be bugged on some platforms)
As such, I'll stop making those newer model formats soon, probably end of this week unless something changes, but you should be safe to download and Q4_0 quants and use those !
Also IQ4_NL supports repacking though not in as many shapes yet, but should get a respectable speed up on ARM chips, PR for that can be found here: https://github.com/ggerganov/llama.cpp/pull/10541
Remember, these are not meant for Apple silicon since those use the GPU and don't benefit from the repacking of weights
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- 16 replies
Notably better than Phi3.5 in many ways, but something is wrong.
8
#5 opened about 2 months ago
by
phil111
llama.cpp...
1
#1 opened 2 months ago
by
urtuuuu
liked
a
model
2 months ago