Instructions to use Myric/granite-3.1-1b-a400m-instruct-APEX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Myric/granite-3.1-1b-a400m-instruct-APEX with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Myric/granite-3.1-1b-a400m-instruct-APEX",
	filename="granite-3.1-1b-a400m-instruct-APEX-compact.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 Myric/granite-3.1-1b-a400m-instruct-APEX 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 Myric/granite-3.1-1b-a400m-instruct-APEX
# Run inference directly in the terminal:
llama cli -hf Myric/granite-3.1-1b-a400m-instruct-APEX

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Myric/granite-3.1-1b-a400m-instruct-APEX
# Run inference directly in the terminal:
llama cli -hf Myric/granite-3.1-1b-a400m-instruct-APEX

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 Myric/granite-3.1-1b-a400m-instruct-APEX
# Run inference directly in the terminal:
./llama-cli -hf Myric/granite-3.1-1b-a400m-instruct-APEX

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 Myric/granite-3.1-1b-a400m-instruct-APEX
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Myric/granite-3.1-1b-a400m-instruct-APEX

Use Docker

docker model run hf.co/Myric/granite-3.1-1b-a400m-instruct-APEX

LM Studio
Jan

vLLM

How to use Myric/granite-3.1-1b-a400m-instruct-APEX with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Myric/granite-3.1-1b-a400m-instruct-APEX"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Myric/granite-3.1-1b-a400m-instruct-APEX",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Myric/granite-3.1-1b-a400m-instruct-APEX

Ollama
How to use Myric/granite-3.1-1b-a400m-instruct-APEX with Ollama:
```
ollama run hf.co/Myric/granite-3.1-1b-a400m-instruct-APEX
```

Unsloth Studio

How to use Myric/granite-3.1-1b-a400m-instruct-APEX 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 Myric/granite-3.1-1b-a400m-instruct-APEX 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 Myric/granite-3.1-1b-a400m-instruct-APEX to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Myric/granite-3.1-1b-a400m-instruct-APEX to start chatting

How to use Myric/granite-3.1-1b-a400m-instruct-APEX with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Myric/granite-3.1-1b-a400m-instruct-APEX

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": "Myric/granite-3.1-1b-a400m-instruct-APEX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Myric/granite-3.1-1b-a400m-instruct-APEX with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Myric/granite-3.1-1b-a400m-instruct-APEX

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 Myric/granite-3.1-1b-a400m-instruct-APEX

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Myric/granite-3.1-1b-a400m-instruct-APEX with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Myric/granite-3.1-1b-a400m-instruct-APEX

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 "Myric/granite-3.1-1b-a400m-instruct-APEX" \
  --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 Myric/granite-3.1-1b-a400m-instruct-APEX with Docker Model Runner:
```
docker model run hf.co/Myric/granite-3.1-1b-a400m-instruct-APEX
```

Lemonade

How to use Myric/granite-3.1-1b-a400m-instruct-APEX with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Myric/granite-3.1-1b-a400m-instruct-APEX

Run and chat with the model

lemonade run user.granite-3.1-1b-a400m-instruct-APEX-{{QUANT_TAG}}

List all available models

lemonade list

granite-3.1-1b-a400m-instruct-APEX / TOOL_CALLING.md

Myric

Upload folder using huggingface_hub

55a0be8 verified 1 day ago

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Tool calling with these APEX quants (reproducible)

granite's stock chat template can't emit tool calls; use the granite-3.1-tools.jinja in this repo. Below is a full round-trip you can paste and run — every setting included.

Server

llama-server --jinja --chat-template-file granite-3.1-tools.jinja --ctx-size 8192 -fa on \
  --temp 0.6 --top-p 0.9 --repeat-penalty 1.05 --model <this-quant>.gguf --host 127.0.0.1 --port 8090

System prompt

You are a helpful assistant with access to tools. When a tool is needed, call it; when it returns a result, answer the user directly using that result.

Turn 1 — request (user + tool schema)

{
    "messages": [
        {
            "role": "system",
            "content": "You are a helpful assistant with access to tools. When a tool is needed, call it; when it returns a result, answer the user directly using that result."
        },
        {
            "role": "user",
            "content": "What is the weather in Paris right now? Use celsius."
        }
    ],
    "tools": [
        {
            "type": "function",
            "function": {
                "name": "get_weather",
                "description": "Get the current weather for a location.",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "City name"
                        },
                        "unit": {
                            "type": "string",
                            "enum": [
                                "celsius",
                                "fahrenheit"
                            ]
                        }
                    },
                    "required": [
                        "location"
                    ]
                }
            }
        }
    ],
    "temperature": 0.6,
    "top_p": 0.9
}

Turn 1 — model response (the tool call)

{
    "choices": [
        {
            "finish_reason": "tool_calls",
            "index": 0,
            "message": {
                "role": "assistant",
                "content": "",
                "tool_calls": [
                    {
                        "type": "function",
                        "function": {
                            "name": "get_weather",
                            "arguments": "{\"location\": \"Paris\", \"unit\": \"celsius\"}"
                        },
                        "id": "eZB0TTBwOvxvL09vmJBizhJcTrGtTBPj"
                    }
                ]
            }
        }
    ],
    "created": 1785251947,
    "model": "granite-3.1-1b-a400m-instruct-APEX-quality.gguf",
    "system_fingerprint": "b1215-3fc4e105",
    "object": "chat.completion",
    "usage": {
        "completion_tokens": 33,
        "prompt_tokens": 197,
        "total_tokens": 230,
        "prompt_tokens_details": {
            "cached_tokens": 0
        }
    },
    "id": "chatcmpl-RESswmFpvUBlJHKzj8KhGIwxnKs5mYBJ",
    "timings": {
        "cache_n": 0,
        "prompt_n": 197,
        "prompt_ms": 185.403,
        "prompt_per_token_ms": 0.9411319796954314,
        "prompt_per_second": 1062.5502284213308,
        "predicted_n": 33,
        "predicted_ms": 88.299,
        "predicted_per_token_ms": 2.675727272727273,
        "predicted_per_second": 373.7301668195563
    }
}

Turn 2 — feed the tool result back

{
    "messages": [
        {
            "role": "system",
            "content": "You are a helpful assistant with access to tools. When a tool is needed, call it; when it returns a result, answer the user directly using that result."
        },
        {
            "role": "user",
            "content": "What is the weather in Paris right now? Use celsius."
        },
        {
            "role": "assistant",
            "content": "",
            "tool_calls": [
                {
                    "type": "function",
                    "function": {
                        "name": "get_weather",
                        "arguments": "{\"location\": \"Paris\", \"unit\": \"celsius\"}"
                    },
                    "id": "eZB0TTBwOvxvL09vmJBizhJcTrGtTBPj"
                }
            ]
        },
        {
            "role": "tool",
            "name": "get_weather",
            "content": "{\"location\":\"Paris\",\"temp_c\":14,\"conditions\":\"light rain\"}"
        }
    ]
}

Turn 2 — model final answer

{
    "choices": [
        {
            "finish_reason": "stop",
            "index": 0,
            "message": {
                "role": "assistant",
                "content": "The weather in Paris right now is light rain."
            }
        }
    ],
    "created": 1785251948,
    "model": "granite-3.1-1b-a400m-instruct-APEX-quality.gguf",
    "system_fingerprint": "b1215-3fc4e105",
    "object": "chat.completion",
    "usage": {
        "completion_tokens": 12,
        "prompt_tokens": 123,
        "total_tokens": 135,
        "prompt_tokens_details": {
            "cached_tokens": 39
        }
    },
    "id": "chatcmpl-jQgducSKaag4fO9sQpxX7xbjy3w8lqAJ",
    "timings": {
        "cache_n": 39,
        "prompt_n": 84,
        "prompt_ms": 13.654,
        "prompt_per_token_ms": 0.16254761904761905,
        "prompt_per_second": 6152.043357257946,
        "predicted_n": 12,
        "predicted_ms": 29.859,
        "predicted_per_token_ms": 2.4882500000000003,
        "predicted_per_second": 401.88887772530893
    }
}