Update README.md

README.md

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----
-language: en
-license: apache-2.0
----
-
-# Shears Model Card: shears-llama-13b-50-math-heuristic
-
-The heuristic adapter discovered from the [super-adapter](https://huggingface.co/IntelLabs/shears-llama-13b-50-math-super) fine-tuned on sparsified LLaMA-13B with some math reasoning datasets using Shears.
-
-## Model Details
-
-### Information
-
-- **Model name:** shears-llama-13b-50-math-heuristic
-- **Base model:** [IntelLabs/Llama-1-13B-sparsity50](https://huggingface.co/IntelLabs/Llama-1-13B-sparsity50)
-- **Sparsity:** 50%
-- **Domain:** Math
-- **Subnetwork version:** Heuristic
-- **NNCF Configuration:** [nncf_shears_llama.json](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears/nncf_config/nncf_shears_llama.json)
-
-### Adapter Configuration
-
-- **LoRA rank:** 32 (24 in the heuristic subnetwork)
-- **LoRA alpha:** 64
-- **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
-
-### Training Data
-
-Unified math reasoning dataset: [math_10k.json](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/ft-training_set/math_10k.json) (collected with the training sets of GSM8K, MAWPS, and AQuA).
-
-### Evaluation Data
-[GSM8K](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/dataset/gsm8k/test.json), [AQuA](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/dataset/AQuA/test.json), [MAWPS](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/dataset/mawps/test.json), [SVAMP](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/dataset/SVAMP/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
-cd peft && git checkout v0.5.0 && git apply --ignore-space-change --ignore-whitespace peft-modifications-for-shears-inference-usage.patch && pip install -e . && cd ..
-```
-
-```python
-import torch
-from peft import PeftModel
-from transformers import AutoModelForCausalLM
-from transformers import AutoTokenizer
-
-def generate_prompt(instruction):
-    return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. 
-
-                    ### Instruction:
-                    {instruction}
-
-                    ### Response:
-                    """
-
-base_model = AutoModelForCausalLM.from_pretrained("IntelLabs/Llama-1-13B-sparsity50")
-model = PeftModel.from_pretrained(base_model, "IntelLabs/shears-llama-13b-50-math-heuristic")
-model.eval()
-
-non_zero_params = sum([(param.data != 0).sum().item() for _, param in model.named_parameters()])
-print(f"Number of all non-zero parameters: {non_zero_params}")
-
-tokenizer = AutoTokenizer.from_pretrained("IntelLabs/Llama-1-13B-sparsity50")
-
-instruction = "Edgar eats 18 pretzels a day. If his brother eats 1/2 as many, how many does his brother eat in a week?"
-prompt = generate_prompt(instruction)
-inputs = tokenizer(prompt, return_tensors="pt")
-input_ids = inputs["input_ids"].to(model.device)
-with torch.no_grad():
-    generation_output = model.generate(
-        input_ids=input_ids,
-        return_dict_in_generate=True,
-        output_scores=True,
-        max_new_tokens=256,
-        use_cache=True,
-        num_beams=4,
-    )
-    s = generation_output.sequences[0]
-    output = tokenizer.decode(s)
-print(output)
-
-```
-
-## Evaluation Results
-
-| Model                 | Sparsity    | GSM8K | AQuA  | MAWPS | SVAMP | Average |
-|-----------------------|-------------|-------|-------|-------|-------|---------|
-| LLaMA-7B-LoRA         | -           | 37.5  | 18.9  | 79.0  | 52.1  | 46.9    |
-| [**LLaMA-7B-Shears**](https://huggingface.co/IntelLabs/shears-llama-7b-50-math-heuristic)   | **50%**     | 36.1  | 22.0  | 78.6  | 44.5  | 45.3    |
-| LLaMA-13B-LoRA        | -           | 47.5  | 18.5  | 83.6  | 54.6  | 51.1    |
-| [**LLaMA-13B-Shears**](https://huggingface.co/IntelLabs/shears-llama-13b-50-math-heuristic)  | **50%**     | 45.1  | 22.0  | 83.2  | 53.3  | 50.9    |
-
-## Model Sources
-
-- **Repository:** [https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears)
-- **Paper:** [Shears: Unstructured Sparsity with Neural Low-rank Adapter Search](https://arxiv.org/abs/2404.10934)
-
-## Citation
-
-```bash
-@article{munoz2024shears,
-  title = {Shears: Unstructured Sparsity with Neural Low-rank Adapter Search},
-  author={J. Pablo Munoz and Jinjie Yuan and Nilesh Jain},
-  journal={The 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-2024)},
-  year={2024}
-}
-```
-
-## License
-
-Apache-2.0
+---
+language: en
+license: apache-2.0
+---
+
+# Shears Model Card: shears-llama-13b-50-math-heuristic-adapter
+
+The heuristic adapter discovered from the [super-adapter](https://huggingface.co/IntelLabs/shears-llama-13b-50-math-super-adapter) fine-tuned on sparsified LLaMA-13B with some math reasoning datasets using Shears.
+
+## Model Details
+
+### Information
+
+- **Model name:** shears-llama-13b-50-math-heuristic-adapter
+- **Base model:** [IntelLabs/shears-llama-13b-50-base](https://huggingface.co/IntelLabs/shears-llama-13b-50-base)
+- **Sparsity:** 50%
+- **Domain:** Math
+- **Subnetwork version:** Heuristic
+- **NNCF Configuration:** [nncf_shears_llama.json](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears/nncf_config/nncf_shears_llama.json)
+
+### Adapter Configuration
+
+- **LoRA rank:** 32 (24 in the heuristic subnetwork)
+- **LoRA alpha:** 64
+- **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
+
+### Training Data
+
+Unified math reasoning dataset: [math_10k.json](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/ft-training_set/math_10k.json) (collected with the training sets of GSM8K, MAWPS, and AQuA).
+
+### Evaluation Data
+[GSM8K](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/dataset/gsm8k/test.json), [AQuA](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/dataset/AQuA/test.json), [MAWPS](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/dataset/mawps/test.json), [SVAMP](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/dataset/SVAMP/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
+cd peft && git checkout v0.5.0 && git apply --ignore-space-change --ignore-whitespace peft-modifications-for-shears-inference-usage.patch && pip install -e . && cd ..
+```
+
+```python
+import torch
+from peft import PeftModel
+from transformers import AutoModelForCausalLM
+from transformers import AutoTokenizer
+
+def generate_prompt(instruction):
+    return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. 
+
+                    ### Instruction:
+                    {instruction}
+
+                    ### Response:
+                    """
+
+base_model = AutoModelForCausalLM.from_pretrained("IntelLabs/shears-llama-13b-50-base")
+model = PeftModel.from_pretrained(base_model, "IntelLabs/shears-llama-13b-50-math-heuristic-adapter")
+model.eval()
+
+non_zero_params = sum([(param.data != 0).sum().item() for _, param in model.named_parameters()])
+print(f"Number of all non-zero parameters: {non_zero_params}")
+
+tokenizer = AutoTokenizer.from_pretrained("IntelLabs/shears-llama-13b-50-base")
+
+instruction = "Edgar eats 18 pretzels a day. If his brother eats 1/2 as many, how many does his brother eat in a week?"
+prompt = generate_prompt(instruction)
+inputs = tokenizer(prompt, return_tensors="pt")
+input_ids = inputs["input_ids"].to(model.device)
+with torch.no_grad():
+    generation_output = model.generate(
+        input_ids=input_ids,
+        return_dict_in_generate=True,
+        output_scores=True,
+        max_new_tokens=256,
+        use_cache=True,
+        num_beams=4,
+    )
+    s = generation_output.sequences[0]
+    output = tokenizer.decode(s)
+print(output)
+
+```
+
+## Evaluation Results
+
+| Model                 | Sparsity    | GSM8K | AQuA  | MAWPS | SVAMP | Average |
+|-----------------------|-------------|-------|-------|-------|-------|---------|
+| LLaMA-7B-LoRA         | -           | 37.5  | 18.9  | 79.0  | 52.1  | 46.9    |
+| [**LLaMA-7B-Shears**](https://huggingface.co/IntelLabs/shears-llama-7b-50-math-heuristic-adapter)   | **50%**     | 36.1  | 22.0  | 78.6  | 44.5  | 45.3    |
+| LLaMA-13B-LoRA        | -           | 47.5  | 18.5  | 83.6  | 54.6  | 51.1    |
+| [**LLaMA-13B-Shears**](https://huggingface.co/IntelLabs/shears-llama-13b-50-math-heuristic-adapter)  | **50%**     | 45.1  | 22.0  | 83.2  | 53.3  | 50.9    |
+
+## Model Sources
+
+- **Repository:** [https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears)
+- **Paper:** [Shears: Unstructured Sparsity with Neural Low-rank Adapter Search](https://arxiv.org/abs/2404.10934)
+
+## Citation
+
+```bash
+@article{munoz2024shears,
+  title = {Shears: Unstructured Sparsity with Neural Low-rank Adapter Search},
+  author={J. Pablo Munoz and Jinjie Yuan and Nilesh Jain},
+  journal={The 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-2024)},
+  year={2024}
+}
+```
+
+## License
+
+Apache-2.0