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README.md ADDED
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+ ---
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+ library_name: residuals
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+ base_model: Qwen/Qwen2.5-7B
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+ base_model_relation: adapter
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+ instruct_model: Qwen/Qwen2.5-7B-Instruct
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+ pipeline_tag: text-generation
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+ tags:
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+ - residuals
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+ - delta
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+ - task-arithmetic
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+ - finetune
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+ ---
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+
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+
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+ # Instruction Residuals
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+
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+ This repository contains instruction residuals (delta weights) computed as the parameter-wise difference between `Qwen/Qwen2.5-7B-Instruct` and `Qwen/Qwen2.5-7B`.
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+
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+ Apply these residuals to the base model to reconstruct the instruction-tuned weights without retraining.
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+
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+
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+ ## Usage
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ from residuals import Residuals
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+
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+ base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B")
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+ tok = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B")
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+
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+ res = Residuals.from_pretrained("residuals/qwen2.5-7b")
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+ res.apply(base, base_tokenizer=tok)
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+ ```
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+
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+
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+ ## Provenance
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+ - **Created at**: 2025-10-25T16:09:23.454524+00:00
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+ - **DType**: float32
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+ - **Parameters**: 339
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+ - **Shapes hash**: cf9d6257268ea1e66311ede6da3b26edcb8a440a6952f4a8271b41db6abc493d
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+ - **Names hash**: 4d2eff4cabea80e69a6d439aa4d36bc50e1d699d0c8eec4da89ae58c02d99e5c
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+ - **Base model**: `Qwen/Qwen2.5-7B`
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+ - **Instruction model**: `Qwen/Qwen2.5-7B-Instruct`
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+
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+ ## Files
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+ - **model.safetensors**: Serialized residual tensors (safetensors format).
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+ - (optional) **model.safetensors.index.json** + shard files `model-00001-of-000N.safetensors`, ... for multi-part weights.
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+ - **config.json**: Residuals metadata and provenance.
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+ - **tokenizer files**: Saved tokenizer for compatibility.
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+
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+ ## About this format
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+ These are additive residuals (task vectors). Applying them to the base model's parameters reconstructs the instruction-tuned model.
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+
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+ ## Tools
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+ Generated with the `residuals` Python package. Install via: `pip install residuals`.
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+ - PyPI: https://pypi.org/project/residuals/
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+ - Source: https://github.com/omarish/residuals
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+ "residuals",
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