ngxson

The dsv4 flash is 158B params in total. It is possible to run locally but will require all my system RAM.

Also, a lot of my day-to-day tasks perform the same on both small and bigger models: summarize a web page, draft a response, translations, quick web search, etc.

Ahhh no sorry I forgot that the actual code controlling this is inside llama-model.cpp ; sorry for the misinfo, the -ngl only set to max by default if you're using Metal backend

(See the code in side llama_model_default_params())

Ohhh nice to know! I was pretty sure that someone already tried to fix the chat template haha, but because we also allow users to freely create their quants via the GGUF-my-repo space, I have to fix the quants produces from that source

We also support SmolVLM series which delivers light-speed response thanks to its mini size!

This is perfect for real-time home video surveillance system. That's one of the ideas for my next hobby project!

    llama-server -hf ggml-org/SmolVLM-Instruct-GGUF
    llama-server -hf ggml-org/SmolVLM-256M-Instruct-GGUF
    llama-server -hf ggml-org/SmolVLM-500M-Instruct-GGUF
    llama-server -hf ggml-org/SmolVLM2-2.2B-Instruct-GGUF
    llama-server -hf ggml-org/SmolVLM2-256M-Video-Instruct-GGUF
    llama-server -hf ggml-org/SmolVLM2-500M-Video-Instruct-GGUF

Ah no I mean we can omit the whole "-ngl N" argument for now, as it is internally set to -1 by default in CPP code (instead of being 0 traditionally), and -1 meaning offload everything to GPU

I have no idea how to specify custom layer specs with multi GPU, but that is interesting!

And btw, -ngl is automatically set to max value now, you don't need to -ngl 99 anymore!

Edit: sorry this is only true on Metal. For CUDA or other GPU backends, you still need to manually specify -ngl

Thanks Daniel! Kudos for your great work on quantization, I use the Mistral Small IQ2_M from unsloth during development and it works very well!!

For brew users, you can specify --HEAD when installing the package. This way, brew will automatically build the latest master branch.

Btw, the brew version will be updated in the next few hours, so after that you will be able to simply "brew upgrade llama.cpp" and you will be good to go!

Two things:

1. Because the support in llama.cpp is horizontal integrated within ggml ecosystem, we can optimize it to run even faster than ollama.

For example, pixtral/mistral small 3.1 model has some 2D-RoPE trick that use less memory than ollama's implementation. Same for flash attention (which will be added very soon), it will allow vision encoder to run faster while using less memory.

2. llama.cpp simply support more models than ollama. For example, ollama does not support either pixtral or smolvlm

Hi I'm Xuan-Son,

Small correct, I'm not just asking it to convert ARM NEON to SIMD, but for the function handling q6_K_q8_K, I asked it to reinvent a new approach (without giving it any prior examples). The reason I did that was because it failed writing this function 4 times so far.

And a bit of context here, I was doing this during my Sunday and the time budget is 2 days to finish.

I wanted to optimize wllama (wasm wrapper for llama.cpp that I maintain) to run deepseek distill 1.5B faster. Wllama is totally a weekend project and I can never spend more than 2 consecutive days on it.

Between 2 choices: (1) to take time to do it myself then maybe give up, or (2) try prompting LLM to do that and maybe give up (at worst, it just give me hallucinated answer), I choose the second option since I was quite sleepy.

So yeah, turns out it was a great success in the given context. Just does it job, saves my weekend.

Some of you may ask, why not trying ChatGPT or Claude in the first place? Well, short answer is: my input is too long, these platforms straight up refuse to give me the answer :)