SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
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Models quantized and uploaded by the LM Studio community, for the LM Studio community. Discord: https://discord.gg/aPQfnNkxGC
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reach-vb
authored
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paper
about 19 hours ago
getting error
8
#1 opened 8 days ago
by
ryg81
bartowski
published
a
model
7 days ago
bartowski
updated
a
model
7 days ago
I have an error when i chat
2
#1 opened 8 days ago
by
joacohuara
lol that was quick
4
#1 opened 8 days ago
by
SerialKicked
bartowski
published
a
model
8 days ago
bartowski
updated
a
model
8 days ago
bartowski
published
a
model
9 days ago
bartowski
updated
a
model
9 days ago
yagilb
published
2
models
12 days ago
bartowski
updated
2
models
13 days ago
needs deepseek 14b qwen model
1
#2 opened 13 days ago
by
joebnb
bartowski
updated
a
model
14 days ago
bartowski
updated
a
model
15 days ago
Post
29473
Switching to
I posted a poll on twitter, and others have mentioned the interest in me using the convention of including the author name in the model path when I upload.
It has a couple advantages, first and foremost of course is ensuring clarity of who uploaded the original model (did Qwen upload Qwen2.6? Or did someone fine tune Qwen2.5 and named it 2.6 for fun?)
The second thing is that it avoids collisions, so if multiple people upload the same model and I try to quant them both, I would normally end up colliding and being unable to upload both
I'll be implementing the change next week, there are just two final details I'm unsure about:
First, should the files also inherit the author's name?
Second, what to do in the case that the author name + model name pushes us past the character limit?
Haven't yet decided how to handle either case, so feedback is welcome, but also just providing this as a "heads up"
author_model-nameI posted a poll on twitter, and others have mentioned the interest in me using the convention of including the author name in the model path when I upload.
It has a couple advantages, first and foremost of course is ensuring clarity of who uploaded the original model (did Qwen upload Qwen2.6? Or did someone fine tune Qwen2.5 and named it 2.6 for fun?)
The second thing is that it avoids collisions, so if multiple people upload the same model and I try to quant them both, I would normally end up colliding and being unable to upload both
I'll be implementing the change next week, there are just two final details I'm unsure about:
First, should the files also inherit the author's name?
Second, what to do in the case that the author name + model name pushes us past the character limit?
Haven't yet decided how to handle either case, so feedback is welcome, but also just providing this as a "heads up"
-
- 3 replies
Post
40464
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
Post
4779
VLMs are going through quite an open revolution AND on-device friendly sizes:
1. Google DeepMind w/ PaliGemma2 - 3B, 10B & 28B: google/paligemma-2-release-67500e1e1dbfdd4dee27ba48
2. OpenGVLabs w/ InternVL 2.5 - 1B, 2B, 4B, 8B, 26B, 38B & 78B: https://huggingface.co/collections/OpenGVLab/internvl-25-673e1019b66e2218f68d7c1c
3. Qwen w/ Qwen 2 VL - 2B, 7B & 72B: Qwen/qwen2-vl-66cee7455501d7126940800d
4. Microsoft w/ FlorenceVL - 3B & 8B: https://huggingface.co/jiuhai
5. Moondream2 w/ 0.5B: https://huggingface.co/vikhyatk/
What a time to be alive! 🔥
1. Google DeepMind w/ PaliGemma2 - 3B, 10B & 28B: google/paligemma-2-release-67500e1e1dbfdd4dee27ba48
2. OpenGVLabs w/ InternVL 2.5 - 1B, 2B, 4B, 8B, 26B, 38B & 78B: https://huggingface.co/collections/OpenGVLab/internvl-25-673e1019b66e2218f68d7c1c
3. Qwen w/ Qwen 2 VL - 2B, 7B & 72B: Qwen/qwen2-vl-66cee7455501d7126940800d
4. Microsoft w/ FlorenceVL - 3B & 8B: https://huggingface.co/jiuhai
5. Moondream2 w/ 0.5B: https://huggingface.co/vikhyatk/
What a time to be alive! 🔥