Llama3.1-70B-Chinese-Chat
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Llama3.1 70B Chinese Chat
Overview :
Llama3.1-70B-Chinese-Chat is an instruction-tuned language model based on the Meta-Llama-3.1-70B-Instruct model, specifically designed for Chinese and English bilingual users, with diverse capabilities such as role-playing and tool utilization. This model is fine-tuned using the ORPO algorithm, significantly reducing instances where Chinese questions receive English answers and cases of mixed language responses, particularly showing notable improvements in role-playing, function invocation, and mathematical abilities.
Target Users :
Llama3.1-70B-Chinese-Chat is designed for developers and businesses requiring bilingual conversation generation in Chinese and English, such as chatbots, language learning applications, and multilingual customer service scenarios. Its optimized capabilities for Chinese and English dialogues provide a more natural and accurate conversational experience.
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Top Region: US(17.94%)
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Use Cases
Serves as a foundational model for chatbots, providing seamless bilingual dialogue services.
Assists users in practicing Chinese and English dialogues in language learning applications.
Provides intelligent dialogue support for multilingual customer service systems.
Features
Supports bilingual dialogue generation in Chinese and English.
Optimized for role-playing and tool usage capabilities.
Fine-tuned with the ORPO algorithm to enhance conversation quality.
Offers various model versions, including BF16 and GGUF formats.
Supports deployment and usage across different platforms, such as Hugging Face and Ollama.
Suitable for applications requiring Chinese and English dialogue generation.
How to Use
1. Visit the Hugging Face model page and download the desired model version.
2. Install necessary dependencies, such as transformers and torch.
3. Use a Python script to load and configure the model, including device mapping and data types.
4. Prepare your dialogue input and process it using the tokenizer.
5. Call the model's generate method to produce dialogue output.
6. Decode the generated output to obtain the final conversation results.
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