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Model: princeton-nlp/Sheared-LLaMA-2.7B-Pruned Source: Original Platform
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README.md
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README.md
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---
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license: llama2
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---
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---
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license: llama2
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---
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**Paper**: [https://arxiv.org/pdf/2310.06694.pdf](https://arxiv.org/pdf/2310.06694.pdf)
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**Code**: https://github.com/princeton-nlp/LLM-Shearing
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**Models**: [Sheared-LLaMA-1.3B](https://huggingface.co/princeton-nlp/Sheared-LLaMA-1.3B), [Sheared-LLaMA-2.7B](https://huggingface.co/princeton-nlp/Sheared-LLaMA-2.7B)
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**Pruned Models without Continued Pre-training**: [Sheared-LLaMA-1.3B-Pruned](https://huggingface.co/princeton-nlp/Sheared-LLaMA-1.3B-Pruned), [Sheared-LLaMA-2.7B-Pruned](https://huggingface.co/princeton-nlp/Sheared-LLaMA-2.7B-Pruned)
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**Instruction-tuned Models**: [Sheared-LLaMA-1.3B-ShareGPT](https://huggingface.co/princeton-nlp/Sheared-LLaMA-1.3B-ShareGPT), [Sheared-LLaMA-2.7B-ShareGPT](https://huggingface.co/princeton-nlp/Sheared-LLaMA-2.7B-ShareGPT)
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**License**: Must comply with license of Llama2 since it's a model derived from Llama2.
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Sheared-LLaMA-2.7B-Pruned is the model pruned from [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) **without continued pre-training**.
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We used roughly 0.4B tokens to perform the pruning experiment. This model could be a good use to study
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- effective data mixtures for continued pre-training
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- comparisons to other pruning techniques
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- extensive evaluations to understand how pruning affects knowledge and reasoning capabilities of LLMs
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config.json
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config.json
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{
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"_name_or_path": "Sheared-LLaMA-2.7B-Pruned",
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"architectures": [
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"LlamaForCausalLM"
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],
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 2560,
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"initializer_range": 0.02,
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"intermediate_size": 6912,
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"max_position_embeddings": 4096,
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"model_type": "llama",
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"num_attention_heads": 20,
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"num_hidden_layers": 32,
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"transformers_version": "4.33.0",
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"vocab_size": 32000
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}
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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generation_config.json
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tokenizer.json
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