---
library_name: residuals
base_model: Qwen/Qwen2.5-14B
base_model_relation: adapter
instruct_model: Qwen/Qwen2.5-14B-Instruct
pipeline_tag: text-generation
tags:
- residuals
- delta
- task-arithmetic
- finetune
---


# 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

```python
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