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Model: RedHatAI/SparseLLama-2-7b-ultrachat_200k-pruned_50.2of4
Source: Original Platform

.gitattributes

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README.md

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+---
+datasets:
+- HuggingFaceH4/ultrachat_200k
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# SparseLlama-2-7b-ultrachat_200k-pruned_50.2of4
+
+## Model Overview
+- **Model Architecture:** Llama-2
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Pruned:** 50% 2:4
+- **Release Date:** 6/28/2024
+- **Version:** 1.0
+- **Model Developers:** Neural Magic
+
+Compressed version of [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b-hf) specialized for text-generation.
+This model was obtained by fine-tuning the Sparse Foundational model [Sparse-Llama-2-7b-pruned_50.2of4](https://huggingface.co/nm-testing/SparseLlama-2-7b-pruned_50.2of4) on the [ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) dataset.
+It achieves a win rate of 62.1% on the [AlpacaEval](https://github.com/tatsu-lab/alpaca_eval) benchmark (version 1.0) when using [Llama-2-70b-chat](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) as evaluator, whereas the dense [Llama-2-7b-ultrachat200k](https://huggingface.co/neuralmagic/Llama-2-7b-ultrachat200k) model achieves 57.6% win rate.
+
+This model was produced as part if Neural Magic's Sparse Foundational Models initiative, and demostrates the capability of Sparse Foundational Models to transfer to the text-generation domain.
+
+**Note:** This model uses the chat template from [zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta).
+
+## Model Optimizations
+
+This model is derived from the Sparse Foundational model [Sparse-Llama-2-7b-pruned_50.2of4](https://huggingface.co/nm-testing/SparseLlama-2-7b-pruned_50.2of4), which was obtained by applying the [SparseGPT](https://arxiv.org/abs/2301.00774) algorithm to prune [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b-hf) to 50% sparsity with a 2:4 mask.
+This optimization reduces the number of parameters by 50%, reducing the disk size and FLOPs by the same level.
+
+## Evaluation
+
+This model was evaluated in the [AlpacaEval](https://github.com/tatsu-lab/alpaca_eval) benchmark using [Llama-2-70b-chat](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) as evaluator.
+
+## Accuracy
+
+| Model | Win rate | Recovery |
+| :----- | :--------: | :--------: |
+| [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b-hf) | 3.7% | -- |
+| [Llama-2-7b-ultrachat200k](https://huggingface.co/neuralmagic/Llama-2-7b-ultrachat200k) | 57.6% | -- |
+| SparseLlama-2-7b-ultrachat_200k-pruned_50.2of4 | 62.1% | 108% |

config.json

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+{
+  "_name_or_path": "training",
+  "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,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.40.2",
+  "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": "4.40.2"
+}

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special_tokens_map.json

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+    "<s>",
+    "</s>"
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