residuals/granite-4.0-h-tiny

Instruction Residuals

This repository contains instruction residuals (delta weights) computed as the parameter-wise difference between ibm-granite/granite-4.0-h-tiny and ibm-granite/granite-4.0-h-tiny-base.

Apply these residuals to the base model to reconstruct the instruction-tuned weights without retraining.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from residuals import Residuals

base = AutoModelForCausalLM.from_pretrained("ibm-granite/granite-4.0-h-tiny-base")
tok = AutoTokenizer.from_pretrained("ibm-granite/granite-4.0-h-tiny-base")

res = Residuals.from_pretrained("residuals/granite-4.0-h-tiny")
res.apply(base, base_tokenizer=tok)

Provenance

• Created at: 2025-10-25T17:44:55.705169+00:00
• DType: float32
• Parameters: 587
• Shapes hash: 6dc91196b3c1b84ed135c5dcd5152fd5f2dcf49c5865e1cd097b1c26693cab11
• Names hash: c8c3f98304c698c0e6fad6202d193586a30c58e61d250b2972183fa48fe29a9b
• Base model: ibm-granite/granite-4.0-h-tiny-base
• Instruction model: ibm-granite/granite-4.0-h-tiny

Files

• model.safetensors: Serialized residual tensors (safetensors format).
• (optional) model.safetensors.index.json + shard files model-00001-of-000N.safetensors, ... for multi-part weights.
• config.json: Residuals metadata and provenance.
• tokenizer files: Saved tokenizer for compatibility.

About this format

These are additive residuals (task vectors). Applying them to the base model's parameters reconstructs the instruction-tuned model.

Tools

Generated with the residuals Python package. Install via: pip install residuals.

• PyPI: https://pypi.org/project/residuals/
• Source: https://github.com/omarish/residuals