Instructions to use mlx-community/granite-8b-code-instruct-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/granite-8b-code-instruct-8bit with Transformers:

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

pipe = pipeline("text-generation", model="mlx-community/granite-8b-code-instruct-8bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mlx-community/granite-8b-code-instruct-8bit")
model = AutoModelForCausalLM.from_pretrained("mlx-community/granite-8b-code-instruct-8bit")
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 mlx-community/granite-8b-code-instruct-8bit 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("mlx-community/granite-8b-code-instruct-8bit")

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
LM Studio

vLLM

How to use mlx-community/granite-8b-code-instruct-8bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlx-community/granite-8b-code-instruct-8bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/granite-8b-code-instruct-8bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mlx-community/granite-8b-code-instruct-8bit

SGLang

How to use mlx-community/granite-8b-code-instruct-8bit 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 "mlx-community/granite-8b-code-instruct-8bit" \
    --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": "mlx-community/granite-8b-code-instruct-8bit",
		"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 "mlx-community/granite-8b-code-instruct-8bit" \
        --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": "mlx-community/granite-8b-code-instruct-8bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

MLX LM

How to use mlx-community/granite-8b-code-instruct-8bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "mlx-community/granite-8b-code-instruct-8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/granite-8b-code-instruct-8bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/granite-8b-code-instruct-8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use mlx-community/granite-8b-code-instruct-8bit with Docker Model Runner:
```
docker model run hf.co/mlx-community/granite-8b-code-instruct-8bit
```

mlx-community/granite-8b-code-instruct-8bit

The Model mlx-community/granite-8b-code-instruct-8bit was converted to MLX format from ibm-granite/granite-8b-code-instruct using mlx-lm version 0.12.0.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/granite-8b-code-instruct-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)

Downloads last month: 34

MLX

Hardware compatibility

Quantized

Model tree for mlx-community/granite-8b-code-instruct-8bit

Base model

ibm-granite/granite-8b-code-base-4k

Finetuned

(3)

this model

Datasets used to train mlx-community/granite-8b-code-instruct-8bit

Evaluation results

pass@1 on HumanEvalSynthesis(Python)
self-reported

57.900
pass@1 on HumanEvalSynthesis(Python)
self-reported

52.400
pass@1 on HumanEvalSynthesis(Python)
self-reported

58.500
pass@1 on HumanEvalSynthesis(Python)
self-reported

43.300
pass@1 on HumanEvalSynthesis(Python)
self-reported

48.200
pass@1 on HumanEvalSynthesis(Python)
self-reported

37.200
pass@1 on HumanEvalSynthesis(Python)
self-reported

53.000
pass@1 on HumanEvalSynthesis(Python)
self-reported

42.700
pass@1 on HumanEvalSynthesis(Python)
self-reported

52.400
pass@1 on HumanEvalSynthesis(Python)
self-reported

36.600
pass@1 on HumanEvalSynthesis(Python)
self-reported

43.900
pass@1 on HumanEvalSynthesis(Python)
self-reported

16.500
pass@1 on HumanEvalSynthesis(Python)
self-reported

39.600
pass@1 on HumanEvalSynthesis(Python)
self-reported

40.900
pass@1 on HumanEvalSynthesis(Python)
self-reported

48.200
pass@1 on HumanEvalSynthesis(Python)
self-reported

41.500