granite-4.0-h-tiny-5bit-MLX

Granite-4.0-H-Tiny — MLX 5-bit (Apple Silicon)

Maintainer / Publisher: Susant Achary

This repository provides an Apple-Silicon MLX build of IBM Granite-4.0-H-Tiny quantized to 5-bit.
If you need more faithfulness than 3/4-bit but want lower RAM than 6-bit, 5-bit is a strong middle ground—especially for document parsing, structured extraction, and long-context assistants on Mac.

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🔎 About Granite 4.0 (context)

• Architecture: Hybrid Mamba-2 + softmax attention; H tiers add Mixture-of-Experts (MoE) (sparse activation per token).
• Tier: H-Tiny (~7B total params with ~1B active via MoE), designed for efficient long-context inference.
• License: Apache-2.0 (permissive, enterprise-friendly).
• Typical uses: Instruction following, long-context assistants, RAG pipelines, structured outputs.

This card documents the MLX 5-bit conversion. See the comparison table below for when to choose 3/4-bit (lower RAM) or 6-bit (highest fidelity).

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📦 What’s in this repo (MLX format)

• config.json (MLX), mlx_model*.safetensors (5-bit shards)
• Tokenizer files: tokenizer.json, tokenizer_config.json
• Model metadata (e.g., model_index.json)

Target platform: macOS on Apple Silicon (M-series) with Metal/MPS acceleration.

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✅ Intended use

• High-quality instruction following and summarization with long context
• Document / form / table parsing and JSON extraction (schema-guided prompts)
• On-device prototyping where accuracy matters but RAM is modest

⚠️ Limitations

• Still quantized: some regressions vs FP16 can surface on intricate math/code.
• KV cache / context length can dominate RAM at very long windows—monitor budgets.
• Add your own guardrails and safety for production.

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🔢 Choosing a quantization level (MLX on Apple Silicon)

Indicative ranges for a ~7B hybrid MoE LM (actual usage varies by context length and batch size).

Rules of thumb

• Start at 5-bit for document/structured tasks on 8–16 GB Macs.
• Drop to 3/4-bit for tighter RAM / higher speed.
• Move to 6-bit if you still see omissions or slight distortions in outputs.

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🚀 Quickstart (CLI — MLX)

Deterministic generation

python -m mlx_lm.generate \
  --model <this-repo-id> \
  --prompt "Summarize the following in 5 bullet points:\n<your text>" \
  --max-tokens 256 \
  --temperature 0.0 \
  --device mps \
  --seed 0