Instructions to use mradermacher/SmolVLM2-256M-Video-Instruct-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/SmolVLM2-256M-Video-Instruct-i1-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/SmolVLM2-256M-Video-Instruct-i1-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/SmolVLM2-256M-Video-Instruct-i1-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mradermacher/SmolVLM2-256M-Video-Instruct-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/SmolVLM2-256M-Video-Instruct-i1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mradermacher/SmolVLM2-256M-Video-Instruct-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/SmolVLM2-256M-Video-Instruct-i1-GGUF:Q4_K_M

Use pre-built binary

# 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 mradermacher/SmolVLM2-256M-Video-Instruct-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/SmolVLM2-256M-Video-Instruct-i1-GGUF:Q4_K_M

Build from source code

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 mradermacher/SmolVLM2-256M-Video-Instruct-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/SmolVLM2-256M-Video-Instruct-i1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/SmolVLM2-256M-Video-Instruct-i1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/SmolVLM2-256M-Video-Instruct-i1-GGUF with Ollama:
```
ollama run hf.co/mradermacher/SmolVLM2-256M-Video-Instruct-i1-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use mradermacher/SmolVLM2-256M-Video-Instruct-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/SmolVLM2-256M-Video-Instruct-i1-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/SmolVLM2-256M-Video-Instruct-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/SmolVLM2-256M-Video-Instruct-i1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SmolVLM2-256M-Video-Instruct-i1-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

SmolVLM2-256M-Video-Instruct-i1-GGUF / SmolVLM2-256M-Video-Instruct.i1-IQ3_S.gguf

mradermacher

uploaded from rain

3534ae3 verified over 1 year ago

History Blame GGUF Editor new Delete

104 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 530bdfeae5398982c241ce7a5025a8d0ce47d2b60db2a70221c1df339370ef2a
Size of remote file:: 104 MB
SHA256:: 1fef244d2f41678af6714f89d1e3d13b47aac7068b4e28194f6e709a331cde11

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.