初始化项目，由ModelHub XC社区提供模型

Model: aqweteddy/xwin-7b_chatvec-tulu2
Source: Original Platform

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README.md

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+---
+license: llama2
+language:
+- en
+---
+
+## Chat Vector
+
+CHAT VECTOR: A SIMPLE APPROACH TO EQUIP LLMS WITH NEW LANGUAGE CHAT CAPABILITIES
+https://arxiv.org/pdf/2310.04799.pdf
+
+
+With the advancements in conversational AI, such as ChatGPT, this paper focuses on exploring developing Large Language Models (LLMs) for non-English languages, especially emphasizing alignment with human preferences. We introduce a computationally efficient method, leveraging “chat vector,” to synergize pre-existing knowledge and behaviors in LLMs, restructuring the conventional training paradigm from continual pretrain 
+ SFT 
+ RLHF to continual pretrain + chat. Our empirical studies, primarily focused on Traditional Chinese, employ LLaMA2 as the base model and acquire the chat vector by subtracting the pre-trained weights, LLaMA2, from the weights of LLaMA2-chat. Evaluating from three distinct facets, which are toxicity, ability of instruction following and multi-turn dialogue demonstrates the chat vector's superior efficacy in “chatting”. To confirm the adaptability of our approach, we extend our experiments to include models pre-trained in both Korean and Simplified Chinese, illustrating the versatility of our methodology. Overall, we present a significant solution in aligning LLMs with human preferences efficiently across various languages, accomplished by the chat vector.
+
+## Merged LM
+
+* xwin7b
+* tulu2-ppo

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+}

config.json

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+  "architectures": [
+    "LlamaForCausalLM"
+  ],
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+  "model_type": "llama",
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+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.34.1",
+  "use_cache": false,
+  "vocab_size": 32001
+}

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+  "pad_token_id": 0,
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+  "top_p": 0.9,
+  "transformers_version": "4.34.1"
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