Update README.md

Signed-off-by: jinjieyuan <[email protected]>

README.md

@@ -5,13 +5,13 @@ license: apache-2.0
 
 # Shears Model Card: shears-llama-7b-50-commonsense-heuristic
 
-The heuristic subnetwork discovered from the super-network fine-tuned on LLaMA-7B with some commonsense reasoning datasets using Shears.
+The heuristic subnetwork discovered from the [super-network](https://huggingface.co/IntelLabs/shears-llama-7b-50-commonsense-super) fine-tuned on LLaMA-7B with some commonsense reasoning datasets using Shears.
 
 ## Model Details
 
 ### Information
 
-- **Model name:** Shears-llama-7b-50-commonsense-heuristic
+- **Model name:** shears-llama-7b-50-commonsense-heuristic
 - **Base model:** [LLaMA-7b](https://huggingface.co/yahma/llama-7b-hf)
 - **Sparsity:** 50%
 - **Domain:** Commonsense
@@ -22,14 +22,14 @@ The heuristic subnetwork discovered from the super-network fine-tuned on LLaMA-7
 
 - **LoRA rank:** 32
 - **LoRA alpha:** 64
-- **LoRA target modules:** q_proj, k_proj, v_proj, up_proj, gate_proj, down_proj
-- **LoRA rank search space:** [32, 24, 16] (for each module)
+- **LoRA target modules:** q_proj, k_proj, v_proj, up_proj, down_proj
+- **LoRA rank search space:** [32, 24, 16] (for each LoRA module)
 
 ### Training Hyperparameters
 
 - **Batch size:** 16
 - **Learning rate:** 3e-4
-- **Epoch:** 3
+- **Epoch:** 5
 
 ### Training Data
 
@@ -38,10 +38,14 @@ Unified commonsense reasoning dataset: [commonsense_170k.json](https://github.co
 ### Evaluation Data
 [BoolQ](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/dataset/boolq/test.json), [PIQA](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/dataset/piqa/test.json), [SIQA](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/dataset/social_i_qa/test.json), [HellaSwag](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/dataset/hellaswag/test.json), [WinoGrande](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/dataset/winogrande/test.json), [ARC-e](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/dataset/ARC-Easy/test.json), [ARC-c](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/dataset/ARC-Challenge/test.json), [OBQA](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/dataset/openbookqa/test.json).
 
-
-
 ## How to use
 
+Use our modified PEFT library (apply [patch](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears/patches/peft-modifications-for-shears-inference-usage.patch)):
+```bash
+git clone https://github.com/huggingface/peft.git
+pushd peft && git checkout v0.5.0 && git apply --ignore-space-change --ignore-whitespace peft-modifications-for-shears-inference-usage.patch && pip install -e . && popd
+```
+
 ```python
 import torch
 from peft import PeftModel