How to use from
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 abenzerps/Spark-X2.5-4B-GGUF:
# Run inference directly in the terminal:
llama cli -hf abenzerps/Spark-X2.5-4B-GGUF:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf abenzerps/Spark-X2.5-4B-GGUF:
# Run inference directly in the terminal:
llama cli -hf abenzerps/Spark-X2.5-4B-GGUF:
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 abenzerps/Spark-X2.5-4B-GGUF:
# Run inference directly in the terminal:
./llama-cli -hf abenzerps/Spark-X2.5-4B-GGUF:
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 abenzerps/Spark-X2.5-4B-GGUF:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf abenzerps/Spark-X2.5-4B-GGUF:
Use Docker
docker model run hf.co/abenzerps/Spark-X2.5-4B-GGUF:
Quick Links

Spark-X2.5-4B GGUF

GGUF quantizations of XHToken/Spark-X2.5-4B, a 4B general-purpose language model for reasoning, coding, tool use, and agentic workflows. Native context: 1,048,576 tokens (1M).

Benchmarks

Spark-X2.5-4B benchmark results

Benchmark results reported by XHToken for Spark-X2.5-4B in thinking mode.

GGUF files

Quantization File Size
Q4_0 Spark-X2.5-4B-Q4_0.gguf 2.41 GB
Q4_K_M Spark-X2.5-4B-Q4_K_M.gguf 2.60 GB
Q5_K_M Spark-X2.5-4B-Q5_K_M.gguf 2.98 GB
Q6_K Spark-X2.5-4B-Q6_K.gguf 3.38 GB
Q8_0 Spark-X2.5-4B-Q8_0.gguf 4.38 GB

Includes the upstream chat_template.jinja. Checksums: SHA256SUMS.txt.

Usage

llama-cli -m Spark-X2.5-4B-Q4_K_M.gguf -c 131072 -cnv

Source

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