Update README.md

README.md

@@ -1,124 +1,124 @@
----
-language: en
-license: apache-2.0
----
-
-# Shears Model Card: shears-llama-7b-50-commonsense-super
-
-The super-adapter fine-tuned on sparsified LLaMA-7B with some commonsense 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-7b-50-commonsense-super
-- **Base model:** [IntelLabs/Llama-1-7B-sparsity50](https://huggingface.co/IntelLabs/Llama-1-7B-sparsity50)
-- **Sparsity:** 50%
-- **Domain:** Commonsense
-- **Subnetwork version:** Super
-- **NNCF Configuration:** [nncf_shears_llama.json](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears/nncf_config/unified_math/nncf_shears_llama.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:** 5
-
-### Training Data
-
-Unified commonsense reasoning dataset: [commonsense_170k.json](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/ft-training_set/commonsense_170k.json).
-
-### 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
-
-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 = AutoModelForCausalLM.from_pretrained("IntelLabs/Llama-1-7B-sparsity50")
-model = PeftModel.from_pretrained(base_model, "IntelLabs/shears-llama-7b-50-commonsense-super")
-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-1-7B-sparsity50")
-
-instruction = "Please choose the correct answer to the question: A cactus stem is used to store\n\nAnswer1: fruit "
-        "Answer2: liquid Answer3: food Answer4: spines\n\nAnswer format: answer1/answer2/answer3/answer4"
-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  | BoolQ   | PIQA   | SIQA   | HellaSwag  | WinoG  | ARC-e  | ARC-c   | OBQA   | Average  |
-|----------------------|-----------|---------|--------|--------|------------|--------|--------|---------|--------|----------|
-| ChatGPT              | -         | 73.1    | 85.4   | 68.5   | 78.5       | 66.1   | 89.8   | 79.9    | 74.8   | 77.0     |
-| LLaMA-7B-LoRA        | -         | 68.9    | 80.7   | 77.4   | 78.1       | 78.8   | 77.8   | 61.3    | 74.8   | 74.7     |
-| [**LLaMA-7B-Shears**](https://huggingface.co/IntelLabs/shears-llama-7b-50-commonsense-heuristic)    | **50%**   | 67.3    | 79.1   | 77.5   | 73.3       | 77.7   | 74.4   | 57.9    | 72.8   | 72.5     |
-
-## 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-7b-50-commonsense-super-adapter
+
+The super-adapter fine-tuned on sparsified LLaMA-7B with some commonsense 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-7b-50-commonsense-super-adapter
+- **Base model:** [IntelLabs/shears-llama-7b-50-base](https://huggingface.co/IntelLabs/shears-llama-7b-50-base)
+- **Sparsity:** 50%
+- **Domain:** Commonsense
+- **Subnetwork version:** Super
+- **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
+- **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:** 5
+
+### Training Data
+
+Unified commonsense reasoning dataset: [commonsense_170k.json](https://github.com/AGI-Edgerunners/LLM-Adapters/blob/main/ft-training_set/commonsense_170k.json).
+
+### 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
+
+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 = AutoModelForCausalLM.from_pretrained("IntelLabs/shears-llama-7b-50-base")
+model = PeftModel.from_pretrained(base_model, "IntelLabs/shears-llama-7b-50-commonsense-super-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-7b-50-base")
+
+instruction = "Please choose the correct answer to the question: A cactus stem is used to store\n\nAnswer1: fruit "
+        "Answer2: liquid Answer3: food Answer4: spines\n\nAnswer format: answer1/answer2/answer3/answer4"
+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  | BoolQ   | PIQA   | SIQA   | HellaSwag  | WinoG  | ARC-e  | ARC-c   | OBQA   | Average  |
+|----------------------|-----------|---------|--------|--------|------------|--------|--------|---------|--------|----------|
+| ChatGPT              | -         | 73.1    | 85.4   | 68.5   | 78.5       | 66.1   | 89.8   | 79.9    | 74.8   | 77.0     |
+| LLaMA-7B-LoRA        | -         | 68.9    | 80.7   | 77.4   | 78.1       | 78.8   | 77.8   | 61.3    | 74.8   | 74.7     |
+| [**LLaMA-7B-Shears**](https://huggingface.co/IntelLabs/shears-llama-7b-50-commonsense-heuristic-adapter)    | **50%**   | 67.3    | 79.1   | 77.5   | 73.3       | 77.7   | 74.4   | 57.9    | 72.8   | 72.5     |
+
+## 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