open-thoughts/OpenThinker-7B
Text Generation
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t1u1
t1u1
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about 16 hours ago
open-thoughts/OpenThinker-7B
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1 day ago
unsloth/DeepSeek-R1-GGUF
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t1u1's activity
liked
a
model
about 16 hours ago
liked
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model
1 day ago
reacted to
mitkox's
post with 👍
3 days ago
Post
2028
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
reacted to
lewtun's
post with 🚀
3 days ago
Post
9283
We are reproducing the full DeepSeek R1 data and training pipeline so everybody can use their recipe. Instead of doing it in secret we can do it together in the open!
🧪 Step 1: replicate the R1-Distill models by distilling a high-quality reasoning corpus from DeepSeek-R1.
🧠 Step 2: replicate the pure RL pipeline that DeepSeek used to create R1-Zero. This will involve curating new, large-scale datasets for math, reasoning, and code.
🔥 Step 3: show we can go from base model -> SFT -> RL via multi-stage training.
Follow along: https://github.com/huggingface/open-r1
🧪 Step 1: replicate the R1-Distill models by distilling a high-quality reasoning corpus from DeepSeek-R1.
🧠 Step 2: replicate the pure RL pipeline that DeepSeek used to create R1-Zero. This will involve curating new, large-scale datasets for math, reasoning, and code.
🔥 Step 3: show we can go from base model -> SFT -> RL via multi-stage training.
Follow along: https://github.com/huggingface/open-r1
- 5 replies
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models
3 days ago
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4 days ago
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article
5 days ago
Article
FuseO1-Preview: System-II Reasoning Fusion of LLMs
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upvoted
a
paper
7 days ago
Can you please create weighted/imatrix quant versions?
#1 opened 8 days ago
by
t1u1
liked
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11 days ago
upvoted
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paper
19 days ago
liked
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Space
4 months ago
reacted to
cbensimon's
post with 🚀
5 months ago
Post
4416
Hello everybody,
We've rolled out a major update to ZeroGPU! All the Spaces are now running on it.
Major improvements:
1. GPU cold starts about twice as fast!
2. RAM usage reduced by two-thirds, allowing more effective resource usage, meaning more GPUs for the community!
3. ZeroGPU initializations (coldstarts) can now be tracked and displayed (use
4. Improved compatibility and PyTorch integration, increasing ZeroGPU compatible spaces without requiring any modifications!
Feel free to answer in the post if you have any questions
🤗 Best regards,
Charles
We've rolled out a major update to ZeroGPU! All the Spaces are now running on it.
Major improvements:
1. GPU cold starts about twice as fast!
2. RAM usage reduced by two-thirds, allowing more effective resource usage, meaning more GPUs for the community!
3. ZeroGPU initializations (coldstarts) can now be tracked and displayed (use
progress=gr.Progress(track_tqdm=True))4. Improved compatibility and PyTorch integration, increasing ZeroGPU compatible spaces without requiring any modifications!
Feel free to answer in the post if you have any questions
🤗 Best regards,
Charles
reacted to
KingNish's
post with 🔥
5 months ago
Post
3908
I am experimenting with Flux and trying to push it to its limits without training (as I am GPU-poor 😅).
I found some flaws in the pipelines, which I resolved, and now I am able to generate an approx similar quality image as Flux Schnell 4 steps in just 1 step.
Demo Link:
KingNish/Realtime-FLUX
I found some flaws in the pipelines, which I resolved, and now I am able to generate an approx similar quality image as Flux Schnell 4 steps in just 1 step.
Demo Link:
KingNish/Realtime-FLUX
- 1 reply
liked
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Spaces
5 months ago
upvoted
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papers
6 months ago