Create README.md

README.md

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+---
+license: apache-2.0
+language: en
+tags:
+- llama
+- instruction-residual
+- parameter-efficient
+- safetensors
+- transformers
+base_model:
+- meta-llama/Llama-3.1-8B
+- meta-llama/Llama-3.1-8B-Instruct
+---
+
+# Llama-3.1-8b-Instruct-Residual
+
+**Full-rank instruction residual for Llama-3.1-8B**
+
+This repository provides the **full-rank instruction residual** \(Δθ = θ_{instruct} - θ_{base}\) between the instruction-tuned Llama-3.1-8B-Instruct model and its corresponding base Llama-3.1-8B model. By adding this residual to a fresh base checkpoint, you can restore instruction-following capabilities **without** running a full fine-tuning cycle.
+
+## How it was created
+
+We follow the *instruction residual* approach introduced by Jindal et al. (2024):
+
+> “In this section, we describe the instruction residual approach to simply regain the instruction following capabilities. We compute the instruction residual between an instruction following LLM \(θ_{i,d_1,v_1}\) and its corresponding base model \(θ_{b,d_1}\) in the parametric space as
+> \[
+> Θ_{r,v_1} = θ_{i,d_1,v_1} - θ_{b,d_1}.
+> \]
+> This tensor subtraction extracts the instruction-specific information, which can then be added to any base model.”
+
+The full paper is available at: https://arxiv.org/abs/2410.10739
+
+## Files
+
+- `pytorch_model.safetensors` — full-rank FP16 residual weights (~16 GB).
+- `config.json` — configuration matching the Llama-3.1-8B architecture.
+- `README.md` — this model card.
+
+## Usage
+
+Below is a minimal example showing how to apply the residual to a base model:
+
+```python
+from transformers import AutoModelForCausalLM
+from safetensors.torch import load_file
+import torch
+
+# 1) Load base
+model = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Llama-3.1-8B",
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+# 2) Load residual
+residual_sd = load_file("pytorch_model.safetensors", device="cpu")
+
+# 3) Apply residual
+for name, delta in residual_sd.items():
+    param = dict(model.named_parameters())[name]
+    param.data += delta.to(param.device).to(param.dtype)
+
+# 4) Save or push
+model.save_pretrained("llama-3.1-8b-base-plus-instruct")
+```
+
+For full scripts, see the `examples/` folder.
+
+## Intended Use & Limitations
+
+- **Intended Use**: Add instruction-following capabilities to Llama-3.1-8B base models.
+- **Limitations**:
+  - Residual must match the exact base checkpoint.
+  - Stored in FP16 (~16 GB); dequantization needed if working in 4-bit.
+  - Applying to mismatched architectures will produce invalid weights.
+
+## License
+
+This residual is released under the **Apache License 2.0**. See the `LICENSE` file for details.
+
+## References
+As mentioned before this method was introduced by **Jindal et al., 2024**, arXiv:2410.10739.:
+
+```bibtex
+@misc{jindal2024balancingcontinuouspretraininginstruction,
+  title={Balancing Continuous Pre-Training and Instruction Fine-Tuning: Optimizing Instruction-Following in LLMs},
+  author={Ishan Jindal and Chandana Badrinath and Pranjal Bharti and Lakkidi Vinay and Sachin Dev Sharma},
+  year={2024},
+  eprint={2410.10739},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  url={https://arxiv.org/abs/2410.10739},
+}
+```
+