Files
granite-3.1-3b-a800m-instru…/README.md
ModelHub XC b802479b00 初始化项目,由ModelHub XC社区提供模型
Model: PJRM/granite-3.1-3b-a800m-instruct-Q4_0-GGUF
Source: Original Platform
2026-06-05 16:40:16 +08:00

1.9 KiB

pipeline_tag, inference, license, library_name, tags, base_model
pipeline_tag inference license library_name tags base_model
text-generation false apache-2.0 transformers
language
granite-3.1
llama-cpp
gguf-my-repo
ibm-granite/granite-3.1-3b-a800m-instruct

PJRM/granite-3.1-3b-a800m-instruct-Q4_0-GGUF

This model was converted to GGUF format from ibm-granite/granite-3.1-3b-a800m-instruct using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo PJRM/granite-3.1-3b-a800m-instruct-Q4_0-GGUF --hf-file granite-3.1-3b-a800m-instruct-q4_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo PJRM/granite-3.1-3b-a800m-instruct-Q4_0-GGUF --hf-file granite-3.1-3b-a800m-instruct-q4_0.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo PJRM/granite-3.1-3b-a800m-instruct-Q4_0-GGUF --hf-file granite-3.1-3b-a800m-instruct-q4_0.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo PJRM/granite-3.1-3b-a800m-instruct-Q4_0-GGUF --hf-file granite-3.1-3b-a800m-instruct-q4_0.gguf -c 2048