How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Steven0090/llava1.6-Mistral-7B-Instruct-v0.2-gguf:Q8_0# Run inference directly in the terminal:
llama cli -hf Steven0090/llava1.6-Mistral-7B-Instruct-v0.2-gguf:Q8_0Use 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 Steven0090/llava1.6-Mistral-7B-Instruct-v0.2-gguf:Q8_0# Run inference directly in the terminal:
./llama-cli -hf Steven0090/llava1.6-Mistral-7B-Instruct-v0.2-gguf:Q8_0Build 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 Steven0090/llava1.6-Mistral-7B-Instruct-v0.2-gguf:Q8_0# Run inference directly in the terminal:
./build/bin/llama-cli -hf Steven0090/llava1.6-Mistral-7B-Instruct-v0.2-gguf:Q8_0Use Docker
docker model run hf.co/Steven0090/llava1.6-Mistral-7B-Instruct-v0.2-gguf:Q8_0Quick Links
This is Q8_0 quantization model of Llava1.6.
Run it by llama_cpp
# !pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="Steven0090/llava1.6-Mistral-7B-Instruct-v0.2-gguf",
filename="Mistral-7B-Instruct-v0.2-Q8_0.gguf",
)
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Model tree for Steven0090/llava1.6-Mistral-7B-Instruct-v0.2-gguf
Base model
liuhaotian/llava-v1.6-mistral-7b
Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf Steven0090/llava1.6-Mistral-7B-Instruct-v0.2-gguf:Q8_0# Run inference directly in the terminal: llama cli -hf Steven0090/llava1.6-Mistral-7B-Instruct-v0.2-gguf:Q8_0