👍 6 🎉 2 ️ 5 Timboman commented Oct 12, 2023 • edited Will this include support for the llama.cpp GGUF compatible version of LLaVA? That frankly is what I would consider the most important overall feature add. https://old.reddit.com/r/LocalLLaMA/comments/175ejvi/quick...
192.168.0.1:2 malvolio.local:1 The above will distribute the computation across 2 processes on the first host and 1 process on the second host. Each process will use roughly an equal amount of RAM. Try to keep these numbers small, as inter-process (intra-host) communication is expensive....
python3 convert.py /app/LinkSoul/Chinese-Llama-2-7b/ --outfile /app/LinkSoul/Chinese-Llama-2-7b-ggml.bin ./quantize /app/LinkSoul/Chinese-Llama-2-7b-ggml.bin /app/LinkSoul/Chinese-Llama-2-7b-ggml-q4.bin q4_0 量化配置的定义: 转自: https://www.reddit.com/r/LocalLLaMA/comments/139...
2. **社区参与**：在Stack Overflow、Reddit等技术社区上发帖询问，高手云集，搞不好一下子就有人揭晓...
摘录:不同内存推荐的本地LLM | reddit提问:Anything LLM, LM Studio, Ollama, Open WebUI,… how and where to even start as a beginner?链接摘录一则回答,来自网友Vitesh4:不同内存推荐的本地LLMLM Studio is super easy to get started with: Just install it, download a model and run it. There...
https://www.reddit.com/r/LocalLLaMA/comments/11o6o3f/how_to_install_llama_8bit_and_4bit/ ^理论上 64GB 就能运行 LLaMA-65B gptq-w4,但是速度受限于内存带宽。 ^推理成本更低,计算量和缓存所允许的显存都显著降低。 ^https://zhuanlan.zhihu.com/p/617433844...
这篇Reddit帖子宣布了Llama 3.2模型,特别是1B和3B版本在微调和推理速度方面的重大进展。这些模型现在可以使用少于4GB的VRAM进行微调,这对于硬件资源有限的用户来说是一个显著的成就。帖子还强调了预量化模型的可用性,这些模型下载速度快4倍,并节省... 这篇...
Reddit Rate(Search and Rate Reddit topics with a weighted summation) OpenTalkGpt(Chrome Extension to manage open-source models supported by Ollama, create custom models, and chat with models from a user-friendly UI) VT(A minimal multimodal AI chat app, with dynamic conversation routing. Supports...
大语言模型(英文:Large Language Model,缩写 LLM)是当前 AI 实际的“大脑🧠”。我们可以在本地运行并使用的模型主要是Llama 2 (meta.com),其由 Meta 提供其是 Llama 的继任者,其他在网上可以看到的绝大多数模型都是基于 Llama v1/2 的 “微调”(Fine-Tune)或修改版本。
Conda reddit.com/r/LocalLLaMA UI github.com/oobabooga/te Model huggingface.co/decapoda 理论上只要有 64GB DRAM 就能运行 30B 的量化模型,但是最好还是找块 RTX 3090 及以上的显卡。如果是 Apple Silicon 建议使用 llama.cpp,目前似乎只能用 CPU 而不能充分利用 GPU 或者 Accelerate。 注意事项 如果对 Hyper...