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Model: RedHatAI/Llama-2-7b-pruned70-retrained
Source: Original Platform

.gitattributes

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README.md

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+---
+base_model: neuralmagic/Llama-2-7b-pruned50-retrained
+inference: true
+model_type: llama
+pipeline_tag: text-generation
+datasets:
+  - cerebras/SlimPajama-627B
+tags:
+- sparse
+---
+
+# Llama-2-7b-pruned70-retrained
+
+This repo contains model files for a [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b-hf) model that has had 50% of the parameters pruned in one-shot with [SparseGPT](https://arxiv.org/abs/2301.00774), then retrained by [Cerebras](https://huggingface.co/cerebras) with 50B tokens from SlimPajama while maintaining sparsity. It was then one-shot pruned to 70% sparsity and trained for another 100B tokens.
+
+Official model weights from [Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment](https://arxiv.org/abs/2405.03594).
+
+**Authors**: Neural Magic, Cerebras
+
+## Usage
+
+Below we share some code snippets on how to get quickly started with running the model.
+
+### Sparse Transfer
+
+By leveraging a pre-sparsified model's structure, you can efficiently fine-tune on new data, leading to reduced hyperparameter tuning, training times, and computational costs. Learn about this process [here](https://neuralmagic.github.io/docs-v2/get-started/transfer).
+
+### Running the model
+
+This model has not been fine-tuned for instruction-following but may be run with the transformers library. For accelerated inference with sparsity, deploy with [nm-vllm](https://github.com/neuralmagic/nm-vllm) or [deepsparse](https://github.com/neuralmagic/deepsparse).
+
+```python
+# pip install transformers accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("neuralmagic/Llama-2-7b-pruned70-retrained")
+model = AutoModelForCausalLM.from_pretrained("neuralmagic/Llama-2-7b-pruned70-retrained", device_map="auto")
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+## Evaluation Benchmark Results
+
+Model evaluation metrics and results. [UPDATE]
+
+| Benchmark                                      | Metric        | Llama-2-7b  | Llama-2-7b-pruned70-retrained |
+|------------------------------------------------|---------------|-------------|-------------------------------|
+| [MMLU](https://arxiv.org/abs/2009.03300)       | 5-shot        | 46.9%       | 36.5%                         |
+| [HellaSwag](https://arxiv.org/abs/1905.07830)  | 0-shot        | 78.6%       | 74.1%                         |
+| [WinoGrande](https://arxiv.org/abs/1907.10641) | 5-shot        | 74.0%       | 69.5%                         |
+| [ARC-c](https://arxiv.org/abs/1911.01547)      | 25-shot       | 53.1%       | 45.4%                         |
+| [TruthfulQA](https://arxiv.org/abs/2109.07958) | 5-shot        | 38.8%       | 36.7%                         |
+| [GSM8K](https://arxiv.org/abs/2110.14168)      | 5-shot        | 14.5%       | 8.0%                          |
+| [HumanEval](https://arxiv.org/abs/2107.03374)  | pass@1        | 13.4%       | 14.4%                         |
+
+## Model Training Details
+
+[UPDATE]
+
+## Help
+
+For further support, and discussions on these models and AI in general, join [Neural Magic's Slack Community](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ)

arc_challenge_25shot_bs16_bf16.json

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+  "results": {
+    "arc_challenge": {
+      "acc": 0.431740614334471,
+      "acc_stderr": 0.014474591427196206,
+      "acc_norm": 0.4539249146757679,
+      "acc_norm_stderr": 0.014549221105171864
+    }
+  },
+  "versions": {
+    "arc_challenge": 0
+  },
+  "config": {
+    "model": "sparseml",
+    "model_args": "pretrained=/network/alexandre/research/cerebras/llama2_7B_sparse70_retrained/checkpoint,dtype=bfloat16",
+    "num_fewshot": 25,
+    "batch_size": "16",
+    "batch_sizes": [],
+    "device": "cuda:4",
+    "no_cache": true,
+    "limit": null,
+    "bootstrap_iters": 100000,
+    "description_dict": {}
+  }
+}

config.json

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+{
+  "_name_or_path": "neuralmagic/Llama-2-7b-pruned70-retrained",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "tokenizer_class": "LlamaTokenizerFast",
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.40.0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.40.0"
+}

gsm8k_5shot_bs16_bf16.json

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+{
+  "results": {
+    "gsm8k": {
+      "acc": 0.07960576194086429,
+      "acc_stderr": 0.00745592433867626
+    }
+  },
+  "versions": {
+    "gsm8k": 0
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+  "config": {
+    "model": "sparseml",
+    "model_args": "pretrained=/network/alexandre/research/cerebras/llama2_7B_sparse70_retrained/checkpoint,dtype=bfloat16",
+    "num_fewshot": 5,
+    "batch_size": "16",
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+    "device": "cuda:6",
+    "no_cache": true,
+    "limit": null,
+    "bootstrap_iters": 100000,
+    "description_dict": {}
+  }
+}

hellaswag_10shot_bs16_bf16.json

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+  "results": {
+    "hellaswag": {
+      "acc": 0.5493925512846046,
+      "acc_stderr": 0.004965375341643134,
+      "acc_norm": 0.7405895239992033,
+      "acc_norm_stderr": 0.004374153847826759
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+  },
+  "versions": {
+    "hellaswag": 0
+  },
+  "config": {
+    "model": "sparseml",
+    "model_args": "pretrained=/network/alexandre/research/cerebras/llama2_7B_sparse70_retrained/checkpoint,dtype=bfloat16",
+    "num_fewshot": 10,
+    "batch_size": "16",
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+    "device": "cuda:2",
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+    "description_dict": {}
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+}

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special_tokens_map.json

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tokenizer_config.json

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truthfulqa_mc_0shot_bs16_bf16.json

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+      "mc1_stderr": 0.014843061507731618,
+      "mc2": 0.3668136165316701,
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winogrande_5shot_bs16_bf16.json

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