Instructions to use Fascix/Akte-medium-granite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Fascix/Akte-medium-granite with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("Fascix/Akte-medium-granite")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Fascix/Akte-medium-granite 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 Fascix/Akte-medium-granite:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Fascix/Akte-medium-granite:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Fascix/Akte-medium-granite:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Fascix/Akte-medium-granite:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf Fascix/Akte-medium-granite:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf Fascix/Akte-medium-granite:Q4_K_M

Use Docker

docker model run hf.co/Fascix/Akte-medium-granite:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use Fascix/Akte-medium-granite with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Fascix/Akte-medium-granite:Q4_K_M

Ollama
How to use Fascix/Akte-medium-granite with Ollama:
```
ollama run hf.co/Fascix/Akte-medium-granite:Q4_K_M
```
Unsloth Desktop

How to use Fascix/Akte-medium-granite with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Fascix/Akte-medium-granite"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Fascix/Akte-medium-granite"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use Fascix/Akte-medium-granite with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Fascix/Akte-medium-granite"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Fascix/Akte-medium-granite"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Fascix/Akte-medium-granite",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use Fascix/Akte-medium-granite with Docker Model Runner:
```
docker model run hf.co/Fascix/Akte-medium-granite:Q4_K_M
```

Lemonade

How to use Fascix/Akte-medium-granite with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Fascix/Akte-medium-granite:Q4_K_M

Run and chat with the model

lemonade run user.Akte-medium-granite-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Fascix/Akte-medium-granite with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Fascix/Akte-medium-granite"

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 Fascix/Akte-medium-granite

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Fascix/Akte-medium-granite with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Fascix/Akte-medium-granite"

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 "Fascix/Akte-medium-granite" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

Akte-medium-granite

File size: 911 Bytes

2791c39

from vllm.reasoning.abs_reasoning_parsers import ReasoningParserManager
from vllm.reasoning.deepseek_r1_reasoning_parser import DeepSeekR1ReasoningParser


@ReasoningParserManager.register_module("granite_thinking_parser")
class GraniteThinkingReasoningParser(DeepSeekR1ReasoningParser):

    def extract_reasoning(self, model_output, request):
        reasoning_content, final_content = super().extract_reasoning(
            model_output, request
        )

        if final_content is not None:
            final_content = final_content.lstrip("\n")

        if (
            hasattr(request, "chat_template_kwargs")
            and request.chat_template_kwargs
            and request.chat_template_kwargs.get("enable_thinking") is False
            and final_content is None
        ):
            reasoning_content, final_content = None, reasoning_content

        return reasoning_content, final_content