Qwen2.5-VL
Collection
Vision-language model series based on Qwen2.5
•
3 items
•
Updated
•
337
Mitko Vasilev
mitkox
AI & ML interests
Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it.
Recent Activity
upvoted
a
collection
15 days ago
Qwen2.5-VL
upvoted
a
collection
16 days ago
Qwen2.5-1M
liked
a model
18 days ago
mobiuslabsgmbh/DeepSeek-R1-ReDistill-Qwen-1.5B-v1.0
Organizations
mitkox's activity
upvoted
a
collection
15 days ago
upvoted
a
collection
16 days ago
liked
a
model
18 days ago
updated
a
model
18 days ago
published
a
model
18 days ago
posted
an
update
18 days ago
Post
2258
llama.cpp is 26.8% faster than ollama.
I have upgraded both, and using the same settings, I am running the same DeepSeek R1 Distill 1.5B on the same hardware. It's an Apples to Apples comparison.
Total duration:
llama.cpp 6.85 sec <- 26.8% faster
ollama 8.69 sec
Breakdown by phase:
Model loading
llama.cpp 241 ms <- 2x faster
ollama 553 ms
Prompt processing
llama.cpp 416.04 tokens/s with an eval time 45.67 ms <- 10x faster
ollama 42.17 tokens/s with an eval time of 498 ms
Token generation
llama.cpp 137.79 tokens/s with an eval time 6.62 sec <- 13% faster
ollama 122.07 tokens/s with an eval time 7.64 sec
llama.cpp is LLM inference in C/C++; ollama adds abstraction layers and marketing.
Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it.
I have upgraded both, and using the same settings, I am running the same DeepSeek R1 Distill 1.5B on the same hardware. It's an Apples to Apples comparison.
Total duration:
llama.cpp 6.85 sec <- 26.8% faster
ollama 8.69 sec
Breakdown by phase:
Model loading
llama.cpp 241 ms <- 2x faster
ollama 553 ms
Prompt processing
llama.cpp 416.04 tokens/s with an eval time 45.67 ms <- 10x faster
ollama 42.17 tokens/s with an eval time of 498 ms
Token generation
llama.cpp 137.79 tokens/s with an eval time 6.62 sec <- 13% faster
ollama 122.07 tokens/s with an eval time 7.64 sec
llama.cpp is LLM inference in C/C++; ollama adds abstraction layers and marketing.
Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it.
- 7 replies
liked
a
dataset
19 days ago
posted
an
update
20 days ago
Post
464
Stargate to the west of me
DeepSeek to the east
Here I am
Stuck in the middle with the EU
It will likely be a matter of sparkle to get export control on frontier research and models on both sides, leaving us in a vacuum.
Decentralized training infrastructure and on device inferencing are the future.
DeepSeek to the east
Here I am
Stuck in the middle with the EU
It will likely be a matter of sparkle to get export control on frontier research and models on both sides, leaving us in a vacuum.
Decentralized training infrastructure and on device inferencing are the future.
posted
an
update
22 days ago
Post
510
On device AI reasoning ODA-R using speculative decoding with draft model DeepSeek-R1-Distill-Qwen-1.5B and DeepSeek-R1-Distill-Qwen-32B. DSPy compiler for reasoning prompts in math, engineering, code...
updated
a
model
22 days ago
published
a
model
22 days ago
liked
2
models
22 days ago
posted
an
update
26 days ago
Post
1411
Training a model to reason in the continuous latent space based on Meta's Coconut.
If it all works will apply it on the MiniCPM-o SVD-LR.
Endgame is a multimodal, adaptive, and efficient foundational on device AI model.
If it all works will apply it on the MiniCPM-o SVD-LR.
Endgame is a multimodal, adaptive, and efficient foundational on device AI model.
- 2 replies
replied to
their
post
about 1 month ago
DDR5 on HP Z8 G5
replied to
their
post
about 1 month ago
exactly Q2 med with ~190GB RAM
posted
an
update
about 1 month ago
Post
2470
Can it run DeepSeek V3 671B is the new 'can it run Doom'.
How minimalistic can I go with on device AI with behemoth models - here I'm running DeepSeek V3 MoE on a single A6000 GPU.
Not great, not terrible, for this minimalistic setup. I love the Mixture of Experts architectures. Typically I'm running my core LLM distributed over the 4 GPUs.
Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it.
How minimalistic can I go with on device AI with behemoth models - here I'm running DeepSeek V3 MoE on a single A6000 GPU.
Not great, not terrible, for this minimalistic setup. I love the Mixture of Experts architectures. Typically I'm running my core LLM distributed over the 4 GPUs.
Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it.
-
- 5 replies
liked
a
Space
about 2 months ago
Bye Apple and hi NVIDIA
4
#6 opened 3 months ago
by
mitkox
liked
a
model
4 months ago
