Remove base model

Signed-off-by: jinjieyuan <jinjie.yuan@intel.com>

README.md

@@ -5,7 +5,7 @@ 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 super-adapter fine-tuned on sparsified 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.
 
@@ -14,11 +14,11 @@ The release of the super-network is to facilitate users to apply their own searc
 ### Information
 
 - **Model name:** shears-llama-13b-50-math-super
-- **Base model:** [LLaMA-13b](https://huggingface.co/yahma/llama-13b-hf)
+- **Base model:** [IntelLabs/Llama-1-13B-sparsity50](https://huggingface.co/IntelLabs/Llama-1-13B-sparsity50)
 - **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)
+- **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
 
@@ -63,17 +63,14 @@ def generate_prompt(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)
+base_model = AutoModelForCausalLM.from_pretrained("IntelLabs/Llama-1-13B-sparsity50")
+model = PeftModel.from_pretrained(base_model, "IntelLabs/shears-llama-13b-50-math-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(model_path)
-tokenizer.pad_token_id = 0
+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)
@@ -88,8 +85,8 @@ with torch.no_grad():
         use_cache=True,
         num_beams=4,
     )
-  s = generation_output.sequences[0]
-  output = tokenizer.decode(s)
+    s = generation_output.sequences[0]
+    output = tokenizer.decode(s)
 print(output)
 
 ```
@@ -108,7 +105,7 @@ Results of the heuristic sub-network discoverd from the super-network:
 ## 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]()
+- **Paper:** [Shears: Unstructured Sparsity with Neural Low-rank Adapter Search](https://arxiv.org/abs/2404.10934)
 
 ## Citation
 
@@ -116,7 +113,7 @@ Results of the heuristic sub-network discoverd from the super-network:
 @article{munoz2024shears,
   title = {Shears: Unstructured Sparsity with Neural Low-rank Adapter Search},
   author={J. Pablo Munoz and Jinjie Yuan and Nilesh Jain},
-  journal={},
+  journal={The 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-2024)},
   year={2024}
 }
 ```

adapter_model/adapter_config.json → adapter_config.json

@@ -1,6 +1,6 @@
 {
   "auto_mapping": null,
-  "base_model_name_or_path": "shears-llama-13b-50-math-super/base_model",
+  "base_model_name_or_path": "IntelLabs/Llama-1-13B-sparsity50",
   "bias": "none",
   "fan_in_fan_out": false,
   "inference_mode": true,

base_model/config.json

@@ -1,25 +0,0 @@
-{
-  "architectures": [
-    "LlamaForCausalLM"
-  ],
-  "bos_token_id": 1,
-  "eos_token_id": 2,
-  "hidden_act": "silu",
-  "hidden_size": 5120,
-  "initializer_range": 0.02,
-  "intermediate_size": 13824,
-  "max_position_embeddings": 2048,
-  "model_type": "llama",
-  "num_attention_heads": 40,
-  "num_hidden_layers": 40,
-  "num_key_value_heads": 40,
-  "pad_token_id": 0,
-  "pretraining_tp": 1,
-  "rms_norm_eps": 1e-06,
-  "rope_scaling": null,
-  "tie_word_embeddings": false,
-  "torch_dtype": "float16",
-  "transformers_version": "4.31.0",
-  "use_cache": true,
-  "vocab_size": 32000
-}

base_model/generation_config.json

@@ -1,7 +0,0 @@
-{
-  "_from_model_config": true,
-  "bos_token_id": 1,
-  "eos_token_id": 2,
-  "pad_token_id": 0,
-  "transformers_version": "4.31.0"
-}

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base_model/special_tokens_map.json

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-{
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base_model/tokenizer.model

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base_model/tokenizer_config.json

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