Instructions to use Tekimax/granite-ml-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tekimax/granite-ml-coder with Transformers:

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

pipe = pipeline("text-generation", model="Tekimax/granite-ml-coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Tekimax/granite-ml-coder")
model = AutoModelForCausalLM.from_pretrained("Tekimax/granite-ml-coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

MLX

How to use Tekimax/granite-ml-coder 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("Tekimax/granite-ml-coder")

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
LM Studio

vLLM

How to use Tekimax/granite-ml-coder with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Tekimax/granite-ml-coder

SGLang

How to use Tekimax/granite-ml-coder 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 "Tekimax/granite-ml-coder" \
    --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": "Tekimax/granite-ml-coder",
		"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 "Tekimax/granite-ml-coder" \
        --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": "Tekimax/granite-ml-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

How to use Tekimax/granite-ml-coder with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Tekimax/granite-ml-coder"

Configure the model in Pi

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

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Tekimax/granite-ml-coder 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 "Tekimax/granite-ml-coder"

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 Tekimax/granite-ml-coder

Run Hermes

hermes

OpenClaw new

How to use Tekimax/granite-ml-coder with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Tekimax/granite-ml-coder"

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 "Tekimax/granite-ml-coder" \
  --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"

MLX LM

How to use Tekimax/granite-ml-coder with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Tekimax/granite-ml-coder"

Run an OpenAI-compatible server

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

Docker Model Runner
How to use Tekimax/granite-ml-coder with Docker Model Runner:
```
docker model run hf.co/Tekimax/granite-ml-coder
```

granite-ml-coder / chat_template.jinja

Tekimax

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	{%- if messages[0]['role'] == 'system' %}
	{%- set system_message = messages[0]['content'] %}
	{%- set loop_messages = messages[1:] %}
	{%- else %}
	{%- set system_message = "Knowledge Cutoff Date: April 2024.
	Today's Date: " + strftime_now('%B %d, %Y') + ".
	You are Granite, developed by IBM." %}
	{%- if tools and documents %}
	{%- set system_message = system_message + " You are a helpful AI assistant with access to the following tools. When a tool is required to answer the user's query, respond with <\|tool_call\|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request.

	Write the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data." %}
	{%- elif tools %}
	{%- set system_message = system_message + " You are a helpful AI assistant with access to the following tools. When a tool is required to answer the user's query, respond with <\|tool_call\|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request." %}
	{%- elif documents %}
	{%- set system_message = system_message + " Write the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data." %}
	{%- else %}
	{%- set system_message = system_message + " You are a helpful AI assistant." %}
	{%- endif %}
	{%- if 'citations' in controls and documents %}
	{%- set system_message = system_message + '

	In your response, use the symbols <co> and </co> to indicate when a fact comes from a document in the search result, e.g <co>0</co> for a fact from document 0. Afterwards, list all the citations with their corresponding documents in an ordered list.' %}
	{%- endif %}
	{%- if 'hallucinations' in controls and documents %}
	{%- set system_message = system_message + '

	Finally, after the response is written, include a numbered list of sentences from the response that are potentially hallucinated and not based in the documents.' %}
	{%- endif %}
	{%- set loop_messages = messages %}
	{%- endif %}
	{{- '<\|start_of_role\|>system<\|end_of_role\|>' + system_message + '<\|end_of_text\|>
	' }}
	{%- if tools %}
	{{- '<\|start_of_role\|>tools<\|end_of_role\|>' }}
	{{- tools \| tojson(indent=4) }}
	{{- '<\|end_of_text\|>
	' }}
	{%- endif %}
	{%- if documents %}
	{{- '<\|start_of_role\|>documents<\|end_of_role\|>' }}
	{%- for document in documents %}
	{{- 'Document ' + loop.index0 \| string + '
	' }}
	{{- document['text'] }}
	{%- if not loop.last %}
	{{- '

	'}}
	{%- endif%}
	{%- endfor %}
	{{- '<\|end_of_text\|>
	' }}
	{%- endif %}
	{%- for message in loop_messages %}
	{{- '<\|start_of_role\|>' + message['role'] + '<\|end_of_role\|>' + message['content'] + '<\|end_of_text\|>
	' }}
	{%- if loop.last and add_generation_prompt %}
	{{- '<\|start_of_role\|>assistant' }}
	{%- if controls %}
	{{- ' ' + controls \| tojson()}}
	{%- endif %}
	{{- '<\|end_of_role\|>' }}
	{%- endif %}
	{%- endfor %}