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-mpt-7b-50-gsm8k-super
+
+The super-network fine-tuned on MPT-7B with GSM8K 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-mpt-7b-50-gsm8k-super
+- **Base model:** [mpt-7b](https://huggingface.co/mosaicml/mpt-7b)
+- **Sparsity:** 50%
+- **Subnetwork version:** Super-network
+- **NNCF Configuration:** [nncf_shears_mpt_7b_sparsity50.json](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears/nncf_config/gsm8k/nncf_shears_mpt_7b_sparsity50.json)
+
+### Adapter Configuration
+
+- **LoRA rank:** 32
+- **LoRA alpha:** 64
+- **LoRA target modules:** q_proj, k_proj, v_proj, out_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:** 5
+
+### Training and Evaluation
+
+GSM8K dataset: [https://huggingface.co/datasets/gsm8k](https://huggingface.co/datasets/gsm8k)
+
+## 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-mpt-7b-50-gsm8k-super/base_model"
+adapter_model_path = "shears-mpt-7b-50-gsm8k-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 Accuracy |
+|-----------------------|-------------|-------|
+| [**MPT-7B-Shears**](https://huggingface.co/IntelLabs/shears-mpt-7b-50-gsm8k-heuristic)   | **50%**     | 33.4  |
+
+## 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