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

Libraries

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

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

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

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

llama-cpp-python

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tensorblock/granite-3.0-1b-a400m-instruct-GGUF",
	filename="granite-3.0-1b-a400m-instruct-Q2_K.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 tensorblock/granite-3.0-1b-a400m-instruct-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 tensorblock/granite-3.0-1b-a400m-instruct-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/granite-3.0-1b-a400m-instruct-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tensorblock/granite-3.0-1b-a400m-instruct-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/granite-3.0-1b-a400m-instruct-GGUF:Q2_K

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 tensorblock/granite-3.0-1b-a400m-instruct-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/granite-3.0-1b-a400m-instruct-GGUF:Q2_K

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 tensorblock/granite-3.0-1b-a400m-instruct-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/granite-3.0-1b-a400m-instruct-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/granite-3.0-1b-a400m-instruct-GGUF:Q2_K

LM Studio
Jan

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tensorblock/granite-3.0-1b-a400m-instruct-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": "tensorblock/granite-3.0-1b-a400m-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tensorblock/granite-3.0-1b-a400m-instruct-GGUF:Q2_K

SGLang

How to use tensorblock/granite-3.0-1b-a400m-instruct-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 "tensorblock/granite-3.0-1b-a400m-instruct-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": "tensorblock/granite-3.0-1b-a400m-instruct-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 "tensorblock/granite-3.0-1b-a400m-instruct-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": "tensorblock/granite-3.0-1b-a400m-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use tensorblock/granite-3.0-1b-a400m-instruct-GGUF with Ollama:
```
ollama run hf.co/tensorblock/granite-3.0-1b-a400m-instruct-GGUF:Q2_K
```

Unsloth Studio

How to use tensorblock/granite-3.0-1b-a400m-instruct-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 tensorblock/granite-3.0-1b-a400m-instruct-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 tensorblock/granite-3.0-1b-a400m-instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tensorblock/granite-3.0-1b-a400m-instruct-GGUF:Q2_K

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": "tensorblock/granite-3.0-1b-a400m-instruct-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use tensorblock/granite-3.0-1b-a400m-instruct-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 tensorblock/granite-3.0-1b-a400m-instruct-GGUF:Q2_K

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 tensorblock/granite-3.0-1b-a400m-instruct-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use tensorblock/granite-3.0-1b-a400m-instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tensorblock/granite-3.0-1b-a400m-instruct-GGUF:Q2_K

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "tensorblock/granite-3.0-1b-a400m-instruct-GGUF:Q2_K" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use tensorblock/granite-3.0-1b-a400m-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/granite-3.0-1b-a400m-instruct-GGUF:Q2_K
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/granite-3.0-1b-a400m-instruct-GGUF:Q2_K

Run and chat with the model

lemonade run user.granite-3.0-1b-a400m-instruct-GGUF-Q2_K

List all available models

lemonade list

granite-3.0-1b-a400m-instruct-GGUF / README.md

morriszms

Update README.md

f661cf7 verified about 1 year ago

preview code

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	---
	pipeline_tag: text-generation
	inference: false
	license: apache-2.0
	library_name: transformers
	tags:
	- language
	- granite-3.0
	- TensorBlock
	- GGUF
	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
	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
	---

	<div style="width: auto; margin-left: auto; margin-right: auto">
	<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
	</div>

	[![Website](https://img.shields.io/badge/Website-tensorblock.co-blue?logo=google-chrome&logoColor=white)](https://tensorblock.co)
	[![Twitter](https://img.shields.io/twitter/follow/tensorblock_aoi?style=social)](https://twitter.com/tensorblock_aoi)
	[![Discord](https://img.shields.io/badge/Discord-Join%20Us-5865F2?logo=discord&logoColor=white)](https://discord.gg/Ej5NmeHFf2)
	[![GitHub](https://img.shields.io/badge/GitHub-TensorBlock-black?logo=github&logoColor=white)](https://github.com/TensorBlock)
	[![Telegram](https://img.shields.io/badge/Telegram-Group-blue?logo=telegram)](https://t.me/TensorBlock)


	## ibm-granite/granite-3.0-1b-a400m-instruct - GGUF

	This repo contains GGUF format model files for [ibm-granite/granite-3.0-1b-a400m-instruct](https://huggingface.co/ibm-granite/granite-3.0-1b-a400m-instruct).

	The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

	## Our projects
	<table border="1" cellspacing="0" cellpadding="10">
	<tr>
	<th style="font-size: 25px;">Awesome MCP Servers</th>
	<th style="font-size: 25px;">TensorBlock Studio</th>
	</tr>
	<tr>
	<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="Project A" width="450"/></th>
	<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Project B" width="450"/></th>
	</tr>
	<tr>
	<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
	<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
	</tr>
	<tr>
	<th>
	<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
	display: inline-block;
	padding: 8px 16px;
	background-color: #FF7F50;
	color: white;
	text-decoration: none;
	border-radius: 6px;
	font-weight: bold;
	font-family: sans-serif;
	">👀 See what we built 👀</a>
	</th>
	<th>
	<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
	display: inline-block;
	padding: 8px 16px;
	background-color: #FF7F50;
	color: white;
	text-decoration: none;
	border-radius: 6px;
	font-weight: bold;
	font-family: sans-serif;
	">👀 See what we built 👀</a>
	</th>
	</tr>
	</table>
	## Prompt template

	```
	<\|start_of_role\|>system<\|end_of_role\|>{system_prompt}<\|end_of_text\|>
	<\|start_of_role\|>user<\|end_of_role\|>{prompt}<\|end_of_text\|>
	<\|start_of_role\|>assistant<\|end_of_role\|>
	```

	## Model file specification

	\| Filename \| Quant type \| File Size \| Description \|
	\| -------- \| ---------- \| --------- \| ----------- \|
	\| [granite-3.0-1b-a400m-instruct-Q2_K.gguf](https://huggingface.co/tensorblock/granite-3.0-1b-a400m-instruct-GGUF/blob/main/granite-3.0-1b-a400m-instruct-Q2_K.gguf) \| Q2_K \| 0.512 GB \| smallest, significant quality loss - not recommended for most purposes \|
	\| [granite-3.0-1b-a400m-instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/granite-3.0-1b-a400m-instruct-GGUF/blob/main/granite-3.0-1b-a400m-instruct-Q3_K_S.gguf) \| Q3_K_S \| 0.598 GB \| very small, high quality loss \|
	\| [granite-3.0-1b-a400m-instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/granite-3.0-1b-a400m-instruct-GGUF/blob/main/granite-3.0-1b-a400m-instruct-Q3_K_M.gguf) \| Q3_K_M \| 0.659 GB \| very small, high quality loss \|
	\| [granite-3.0-1b-a400m-instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/granite-3.0-1b-a400m-instruct-GGUF/blob/main/granite-3.0-1b-a400m-instruct-Q3_K_L.gguf) \| Q3_K_L \| 0.711 GB \| small, substantial quality loss \|
	\| [granite-3.0-1b-a400m-instruct-Q4_0.gguf](https://huggingface.co/tensorblock/granite-3.0-1b-a400m-instruct-GGUF/blob/main/granite-3.0-1b-a400m-instruct-Q4_0.gguf) \| Q4_0 \| 0.768 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \|
	\| [granite-3.0-1b-a400m-instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/granite-3.0-1b-a400m-instruct-GGUF/blob/main/granite-3.0-1b-a400m-instruct-Q4_K_S.gguf) \| Q4_K_S \| 0.775 GB \| small, greater quality loss \|
	\| [granite-3.0-1b-a400m-instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/granite-3.0-1b-a400m-instruct-GGUF/blob/main/granite-3.0-1b-a400m-instruct-Q4_K_M.gguf) \| Q4_K_M \| 0.822 GB \| medium, balanced quality - recommended \|
	\| [granite-3.0-1b-a400m-instruct-Q5_0.gguf](https://huggingface.co/tensorblock/granite-3.0-1b-a400m-instruct-GGUF/blob/main/granite-3.0-1b-a400m-instruct-Q5_0.gguf) \| Q5_0 \| 0.929 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \|
	\| [granite-3.0-1b-a400m-instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/granite-3.0-1b-a400m-instruct-GGUF/blob/main/granite-3.0-1b-a400m-instruct-Q5_K_S.gguf) \| Q5_K_S \| 0.929 GB \| large, low quality loss - recommended \|
	\| [granite-3.0-1b-a400m-instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/granite-3.0-1b-a400m-instruct-GGUF/blob/main/granite-3.0-1b-a400m-instruct-Q5_K_M.gguf) \| Q5_K_M \| 0.956 GB \| large, very low quality loss - recommended \|
	\| [granite-3.0-1b-a400m-instruct-Q6_K.gguf](https://huggingface.co/tensorblock/granite-3.0-1b-a400m-instruct-GGUF/blob/main/granite-3.0-1b-a400m-instruct-Q6_K.gguf) \| Q6_K \| 1.099 GB \| very large, extremely low quality loss \|
	\| [granite-3.0-1b-a400m-instruct-Q8_0.gguf](https://huggingface.co/tensorblock/granite-3.0-1b-a400m-instruct-GGUF/blob/main/granite-3.0-1b-a400m-instruct-Q8_0.gguf) \| Q8_0 \| 1.422 GB \| very large, extremely low quality loss - not recommended \|


	## Downloading instruction

	### Command line

	Firstly, install Huggingface Client

	```shell
	pip install -U "huggingface_hub[cli]"
	```

	Then, downoad the individual model file the a local directory

	```shell
	huggingface-cli download tensorblock/granite-3.0-1b-a400m-instruct-GGUF --include "granite-3.0-1b-a400m-instruct-Q2_K.gguf" --local-dir MY_LOCAL_DIR
	```

	If you wanna download multiple model files with a pattern (e.g., `Q4_Kgguf`), you can try:

	```shell
	huggingface-cli download tensorblock/granite-3.0-1b-a400m-instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='Q4_Kgguf'
	```