Create README.md

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

README.md

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+---
+language: en
+license: apache-2.0
+---
+
+# Shears Model Card: shears-llama-13b-50-math-super
+
+The super-network fine-tuned on LLaMA-13B with some math reasoning datasets using Shears.
+
+The release of the super-network is to facilitate users to apply their own search algorithms and evaluation indicators to extract subnetworks suitable for their specific needs.
+
+## Model Details
+
+### Information
+
+- **Model name:** shears-llama-13b-50-math-super
+- **Base model:** [LLaMA-13b](https://huggingface.co/yahma/llama-13b-hf)
+- **Sparsity:** 50%
+- **Domain:** Math
+- **Subnetwork version:** Super-network
+- **NNCF Configuration:** [nncf_shears_llama_13b_sparsity50.json](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears/nncf_config/unified_math/nncf_shears_llama_13b_sparsity50.json)
+
+### Adapter Configuration
+
+- **LoRA rank:** 32
+- **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
+
+Refer to the illustrative example provided in [load_and_explore_supernet.ipynb](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears/search/load_and_explore_supernet.ipynb) for a comprehensive understanding. This notebook shows the direct loading of a Shears super-network and the extraction of diverse subnetworks from it. 
+This feature empowers users to employ their own search algorithms and evaluation metrics for the extraction of subnetworks customized to their specific requirements.
+
+Moreover, the super-network is essentially the maximal subnetwork, and it can also be directly loaded:
+
+```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_path = "shears-llama-13b-50-math-super/base_model"
+adapter_model_path = "shears-llama-13b-50-math-super/adapter_model"
+base_model = AutoModelForCausalLM.from_pretrained(base_model_path)
+model = PeftModel.from_pretrained(base_model, adapter_model_path)
+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(model_path)
+tokenizer.pad_token_id = 0
+
+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
+
+Results of the heuristic sub-network discoverd from the super-network:
+
+| 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]()
+
+## 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={},
+  year={2024}
+}
+```
+
+## License
+
+Apache-2.0