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Model: hfl/chinese-llama-2-13b-16k
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+language:
+- zh
+- en
+---
+
+# Chinese-LLaMA-2-12B-16K
+
+**This is the full Chinese-LLaMA-2-13B-16K (context size 16K)，model，which can be loaded directly for inference and full-parameter training.**
+
+**Related models👇**
+* Long context base models (16K)
+  * [Chinese-LLaMA-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-7b-16k)
+  * [Chinese-LLaMA-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b-16k)
+  * [Chinese-LLaMA-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-13b-16k)
+  * [Chinese-LLaMA-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b-16k)
+* Long context Instruction/Chat models
+  * [Chinese-Alpaca-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b-16k)
+  * [Chinese-Alpaca-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b-16k)
+  * [Chinese-Alpaca-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b-16k)
+  * [Chinese-Alpaca-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b-16k)
+* Base models
+  * [Chinese-LLaMA-2-7B (full model)](https://huggingface.co/hfl/chinese-llama-2-7b)
+  * [Chinese-LLaMA-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b)
+  * [Chinese-LLaMA-2-13B (full model)](https://huggingface.co/hfl/chinese-llama-2-13b)
+  * [Chinese-LLaMA-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b)
+* Instruction/Chat models
+  * [Chinese-Alpaca-2-7B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b)
+  * [Chinese-Alpaca-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b)
+  * [Chinese-Alpaca-2-13B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b)
+  * [Chinese-Alpaca-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b)
+
+# Description of Chinese-LLaMA-Alpaca-2 
+This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.
+
+The main contents of this project include:
+
+* 🚀 New extended Chinese vocabulary beyond Llama-2, open-sourcing the Chinese LLaMA-2 and Alpaca-2 LLMs.
+* 🚀 Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data
+* 🚀 Quickly deploy and experience the quantized LLMs on CPU/GPU of personal PC
+* 🚀 Support for LLaMA ecosystems like 🤗transformers, llama.cpp, text-generation-webui, LangChain, vLLM etc.
+
+Please refer to [https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/) for details.

SHA256.md

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config.json

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+  "_name_or_path": "meta-llama/Llama-2-13b-hf",
+  "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": 16384,
+  "model_type": "llama",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 40,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 4.0,
+    "type": "linear"
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+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.31.0",
+  "use_cache": true,
+  "vocab_size": 55296
+}

configuration.json

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generation_config.json

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