SkyVision.cpp
Collection
12 items • Updated • 1
How to use SkyContact/Marlin-2B-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf SkyContact/Marlin-2B-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf SkyContact/Marlin-2B-GGUF:Q8_0
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf SkyContact/Marlin-2B-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf SkyContact/Marlin-2B-GGUF:Q8_0
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf SkyContact/Marlin-2B-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf SkyContact/Marlin-2B-GGUF:Q8_0
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf SkyContact/Marlin-2B-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf SkyContact/Marlin-2B-GGUF:Q8_0
docker model run hf.co/SkyContact/Marlin-2B-GGUF:Q8_0
How to use SkyContact/Marlin-2B-GGUF with Ollama:
ollama run hf.co/SkyContact/Marlin-2B-GGUF:Q8_0
How to use SkyContact/Marlin-2B-GGUF with Docker Model Runner:
docker model run hf.co/SkyContact/Marlin-2B-GGUF:Q8_0
How to use SkyContact/Marlin-2B-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull SkyContact/Marlin-2B-GGUF:Q8_0
lemonade run user.Marlin-2B-GGUF-Q8_0
lemonade list
Marlin-2B(Qwen3.5-2B 混合主干 + Qwen3-VL 式视觉塔,视频结构化信息提取)的社区 GGUF 量化版,由自研推理引擎 SkyVision.cpp(纯 ggml,无 llama.cpp 依赖)转换并逐级对拍验证。
仅供 SkyVision.cpp 使用。 GGUF 里
general.architecture = marlin, llama.cpp / ollama / LM Studio 等通用 GGUF 运行时无法加载本仓库文件;本仓库与通用 GGUF 生态不兼容, 请勿据此判断"能不能跑"。
| 文件 | 大小 | 说明 |
|---|---|---|
marlin-2b-q8_0.gguf |
2.67 GB | 12GB 显卡流畅档(视觉塔 F16 保留) |
git clone https://github.com/SkYContact/SkyVision.cpp
cd SkyVision.cpp && git clone --depth 1 https://github.com/ggml-org/ggml
scripts\build-cuda.bat
# 视频稠密字幕(Scene: + 带秒级时间戳的事件列表)
vqa-cli --model marlin-2b-q8_0.gguf --tokenizer <Marlin-2B 检查点目录> \
--video clip.mp4 --fps 2 --max-frames 16 --ask x --mode caption
# 时序定位:自然语言事件 → From X.X to Y.Y.
vqa-cli --model marlin-2b-q8_0.gguf --tokenizer <Marlin-2B 检查点目录> \
--video clip.mp4 --ask "a person enters the room" --mode find
tokenizer 直接用官方检查点目录(tokenizer.json),无需转换;检查点可从
NemoStation/Marlin-2B 或魔搭镜像
NemoStation/Marlin-2B(无需登录)获取。
性能参考(RTX 5070 Ti Laptop,Q8_0):视觉编码 112ms/300tok,prefill ~9.7k tok/s,decode ~121 tok/s。
Apache-2.0(继承基座 NemoStation/Marlin-2B)。
8-bit