ibm-granite/granite-4.2-30b-bf16-mlx

Granite 4.2 models (MLX)

Granite 4.2 language models are a family of state-of-the-art open foundation models featuring dense decoder-only architectures. They natively support multilingual capabilities, a wide range of coding tasks, retrieval-augmented generation (RAG), tool usage, structured JSON output, and extended reasoning via a built-in thinking mode.

All models are publicly released under the Apache 2.0 license, allowing free use for both research and commercial purposes. The data curation and training processes were specifically designed for enterprise scenarios and customization, incorporating governance, risk, and compliance (GRC) evaluations alongside IBM's standard data clearance and document quality review procedures.

[!NOTE] This repository contains MLX variants of an IBM Granite base model, converted using mlx-lm for native inference on Apple Silicon (M-series) hardware.

Please reference the base model's full model card here: https://huggingface.co/ibm-granite/granite-4.2-30b

Requirements

• macOS with Apple Silicon (M1 / M2 / M3 / M4 or later)
• Python ≥ 3.9

Installation

mlx-lm is a Python package that provides fast LLM inference and fine-tuning on Apple Silicon using the MLX framework.

Option 1 — install into your environment (recommended for repeated use):

pip install mlx-lm

Option 2 — run ephemerally via uvx (no persistent mlx-lm install required):

Prerequisite: uvx is part of uv, a fast Python package manager. Install it first by following the uv installation guide or with:

pip install uv

# mlx-lm is downloaded into a temporary environment on first use
uvx mlx_lm generate \
  --model ibm-granite/granite-4.2-30b-bf16-mlx \
  --prompt "Why is the sky blue according to science?" \
  --temp 1.0 \
  --top-p 0.95

Available MLX Variants

Each quantization variant is published as its own Hugging Face repository:

For example, the variants for granite-4.2-30b are published at:

ibm-granite/granite-4.2-30b-bf16-mlx
ibm-granite/granite-4.2-30b-q8-mlx
ibm-granite/granite-4.2-30b-q4-mlx

Running

Run directly from a variant repository (mlx-lm will download weights on first use):

mlx_lm generate \
  --model ibm-granite/granite-4.2-30b-bf16-mlx \
  --prompt "Why is the sky blue according to science?" \
  --temp 1.0 \
  --top-p 0.95

To use a different quantization, substitute the variant suffix, for example -q8-mlx or -bf16-mlx.

You can also download the model and run from a local path:

uvx --from huggingface-hub hf download ibm-granite/granite-4.2-30b-bf16-mlx \
  --local-dir ./granite-4.2-30b-bf16-mlx

mlx_lm generate \
  --model ./granite-4.2-30b-bf16-mlx \
  --prompt "Why is the sky blue according to science?" \
  --temp 1.0 \
  --top-p 0.95

Recommended Generation Parameters

This repository includes a generation_config.json copied from the base model. It records the recommended temperature and top_p values, but mlx_lm generate does not read them automatically — only eos_token_id is consumed by mlx-lm at inference time. Pass the values explicitly on every invocation using the flags below.

The Granite 4.2 base model recommends:

Without these flags, mlx_lm generate defaults to --temp 0.0 (greedy / deterministic decoding) and --top-p 1.0.

Example with the recommended values applied:

mlx_lm generate \
  --model ibm-granite/granite-4.2-30b-bf16-mlx \
  --prompt "Explain retrieval-augmented generation in simple terms." \
  --temp 1.0 \
  --top-p 0.95

In Python, always apply the chat template and pre-tokenize before calling generate():

from mlx_lm import load, generate

model, tokenizer = load("ibm-granite/granite-4.2-30b-bf16-mlx")

messages = [{"role": "user", "content": "Explain retrieval-augmented generation in simple terms."}]

# Always pre-tokenize with add_special_tokens=False — see Thinking Mode section.
prompt_str = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,
)
prompt = tokenizer.encode(prompt_str, add_special_tokens=False)

response = generate(model, tokenizer, prompt=prompt, temp=1.0, top_p=0.95, max_tokens=256)
print(response)

Thinking Mode

Granite 4.2 supports built-in chain-of-thought reasoning. When thinking is active the model produces its reasoning inside a <think>…</think> block before its final answer.

Thinking is on by default. The chat template appends <think>\n automatically when add_generation_prompt=True, placing the model inside the open reasoning block so it generates content before closing it. Two template parameters let you adjust the behavior:

CLI — thinking is on by default

No extra flags are needed to enable thinking. The model reasons automatically:

mlx_lm generate \
  --model ibm-granite/granite-4.2-30b-bf16-mlx \
  --prompt "What is the difference between a mutex and a semaphore?" \
  --temp 1.0 \
  --top-p 0.95 \
  --max-tokens 1024

CLI — disable thinking for direct answers:

Pass enable_thinking=false via --chat-template-config to skip the reasoning block entirely:

mlx_lm generate \
  --model ibm-granite/granite-4.2-30b-bf16-mlx \
  --prompt "Summarize this paragraph in one sentence." \
  --chat-template-config '{"enable_thinking": false}' \
  --temp 1.0 \
  --top-p 0.95

CLI — request low-effort (faster) reasoning:

mlx_lm generate \
  --model ibm-granite/granite-4.2-30b-bf16-mlx \
  --prompt "What is the difference between a mutex and a semaphore?" \
  --chat-template-config '{"reasoning_effort": "low"}' \
  --temp 1.0 \
  --top-p 0.95 \
  --max-tokens 1024

Python API

Enable thinking:

Include <think> block before answer:

from mlx_lm import load, generate

model, tokenizer = load(
    "ibm-granite/granite-4.2-30b-bf16-mlx"
)

messages = [{"role": "user", "content": "What is the difference between a mutex and a semaphore?"}]

# enable_thinking=True: the chat template appends <think>\n so the model reasons
# before closing the block with </think> and producing its final answer.
# True is the default for Granite 4.2, but passing it explicitly is good practice.
#
# IMPORTANT: always pre-tokenize with add_special_tokens=False.
# Passing a plain string to generate() triggers re-encoding with add_special_tokens=True,
# which prepends a spurious BOS token and collapses the thinking block to empty.
prompt_str = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,         # show Think block
)
prompt = tokenizer.encode(prompt_str, add_special_tokens=False)

response = generate(model, tokenizer, prompt=prompt, temp=1.0, top_p=0.95, max_tokens=1024)
print(response)

Disable thinking

Direct answer with no <think> block:

from mlx_lm import load, generate

model, tokenizer = load(
    "ibm-granite/granite-4.2-30b-bf16-mlx"
)

messages = [{"role": "user", "content": "Summarize this paragraph in one sentence."}]

prompt_str = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,        # skip the <think> block entirely
)
prompt = tokenizer.encode(prompt_str, add_special_tokens=False)

response = generate(model, tokenizer, prompt=prompt, temp=1.0, top_p=0.95, max_tokens=256)
print(response)

Low-effort (faster) reasoning:

reasoning_effort controls the depth of the thinking pass. The default is "high" (full-depth reasoning). Set it to "low" for a shorter, faster reasoning pass.

# reasoning_effort="low"  → shorter, faster reasoning pass
# reasoning_effort="high" → full-depth reasoning (default; same as omitting the parameter)
prompt_str = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    reasoning_effort="low",
)
prompt = tokenizer.encode(prompt_str, add_special_tokens=False)

response = generate(model, tokenizer, prompt=prompt, temp=1.0, top_p=0.95, max_tokens=512)
print(response)

Supported Languages

Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 4.2 models for languages beyond these languages.

Intended Use

This model is designed to handle general instruction-following tasks and can be integrated into AI assistants across various domains, including business applications.

Capabilities

• Summarization
• Text classification
• Text extraction
• Question-answering
• Retrieval Augmented Generation (RAG)
• Code related tasks
• Function-calling tasks
• Multilingual dialog use cases
• Fill-In-the-Middle (FIM) code completions
• Thinking / extended reasoning (enable_thinking, reasoning_effort)

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Learn more

• Developers: Granite Team, IBM
• Website: Granite Docs
• GitHub Repository: ibm-granite/granite-language-models
• Hugging Face: Granite 4.2 Language Models
• MLX Examples: ml-explore/mlx-examples
• License: Apache 2.0