Instructions to use farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF", dtype="auto")

llama-cpp-python

How to use farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF",
	filename="granite-3.0-1b-a400m-instruct-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-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 farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-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 farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-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 farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-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 farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF:Q4_K_M

SGLang

How to use farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF:Q4_K_M
```

Unsloth Studio

How to use farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF to start chatting

How to use farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF

File size: 4,269 Bytes

33e227f

---
pipeline_tag: text-generation
inference: false
license: apache-2.0
library_name: transformers
tags:
- language
- granite-3.0
- llama-cpp
- gguf-my-repo
base_model: ibm-granite/granite-3.0-1b-a400m-instruct
model-index:
- name: granite-3.0-2b-instruct
  results:
  - task:
      type: text-generation
    dataset:
      name: IFEval
      type: instruction-following
    metrics:
    - type: pass@1
      value: 32.39
      name: pass@1
    - type: pass@1
      value: 6.17
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: AGI-Eval
      type: human-exams
    metrics:
    - type: pass@1
      value: 20.35
      name: pass@1
    - type: pass@1
      value: 32.0
      name: pass@1
    - type: pass@1
      value: 12.21
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: OBQA
      type: commonsense
    metrics:
    - type: pass@1
      value: 38.4
      name: pass@1
    - type: pass@1
      value: 47.55
      name: pass@1
    - type: pass@1
      value: 65.59
      name: pass@1
    - type: pass@1
      value: 61.17
      name: pass@1
    - type: pass@1
      value: 49.11
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: BoolQ
      type: reading-comprehension
    metrics:
    - type: pass@1
      value: 70.12
      name: pass@1
    - type: pass@1
      value: 1.27
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: ARC-C
      type: reasoning
    metrics:
    - type: pass@1
      value: 41.21
      name: pass@1
    - type: pass@1
      value: 23.07
      name: pass@1
    - type: pass@1
      value: 31.77
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: HumanEvalSynthesis
      type: code
    metrics:
    - type: pass@1
      value: 30.18
      name: pass@1
    - type: pass@1
      value: 26.22
      name: pass@1
    - type: pass@1
      value: 21.95
      name: pass@1
    - type: pass@1
      value: 15.4
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: GSM8K
      type: math
    metrics:
    - type: pass@1
      value: 26.31
      name: pass@1
    - type: pass@1
      value: 10.88
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: PAWS-X (7 langs)
      type: multilingual
    metrics:
    - type: pass@1
      value: 45.84
      name: pass@1
    - type: pass@1
      value: 11.8
      name: pass@1
---

# farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`ibm-granite/granite-3.0-1b-a400m-instruct`](https://huggingface.co/ibm-granite/granite-3.0-1b-a400m-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/ibm-granite/granite-3.0-1b-a400m-instruct) for more details on the model.

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

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF --hf-file granite-3.0-1b-a400m-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF --hf-file granite-3.0-1b-a400m-instruct-q4_k_m.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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 farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF --hf-file granite-3.0-1b-a400m-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo farpluto/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF --hf-file granite-3.0-1b-a400m-instruct-q4_k_m.gguf -c 2048
```