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---
library_name: residuals
base_model: Qwen/Qwen3-0.6B-Base
base_model_relation: adapter
instruct_model: Qwen/Qwen3-0.6B
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/Qwen3-0.6B` and `Qwen/Qwen3-0.6B-Base`.

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/Qwen3-0.6B-Base")
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B-Base")

res = Residuals.from_pretrained("residuals/qwen3-0.6b")
res.apply(base, base_tokenizer=tok)
```


## Provenance
- **Created at**: 2025-10-25T17:59:58.856127+00:00
- **DType**: float32
- **Parameters**: 311
- **Shapes hash**: a24a72d28c65d986a1537975e25867880c7ba603637058fceda763bcb730b2b8
- **Names hash**: 17f783d5d8f115eb5cf5c88fd284d191b841870eeffb9c845f51180afc4370ac
- **Base model**: `Qwen/Qwen3-0.6B-Base`
- **Instruction model**: `Qwen/Qwen3-0.6B`

## 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