qwen2.5-14b

Instruction Residuals

This repository contains instruction residuals (delta weights) computed as the parameter-wise difference between Qwen/Qwen2.5-14B-Instruct and Qwen/Qwen2.5-14B.

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("Qwen/Qwen2.5-14B")
tok = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-14B")

res = Residuals.from_pretrained("residuals/qwen2.5-14b")
res.apply(base, base_tokenizer=tok)

Provenance

• Created at: 2025-10-25T16:18:30.099054+00:00
• DType: float32
• Parameters: 579
• Shapes hash: b3fe0942a175bf28daab2610457f75a24fbeca9d5c4cad1c951e9beac71b6a53
• Names hash: 155db82de40585f183c6134b5f8843abbe195fbe1997d967cd5ab9d22d54dc2e
• Base model: Qwen/Qwen2.5-14B
• Instruction model: Qwen/Qwen2.5-14B-Instruct

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