SeaLLMs/SeaLLMs-v3-7B-Chat

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Jul 3, 2024License:seallmsArchitecture:Transformer0.1K Warm

SeaLLMs-v3-7B-Chat is a 7.6 billion parameter large language model developed by SeaLLMs, specifically fine-tuned for chat and instruction-following in Southeast Asian languages. It excels across diverse tasks including world knowledge, mathematical reasoning, and translation, while also demonstrating enhanced trustworthiness with reduced hallucination and culturally sensitive responses. The model supports a wide range of SEA languages such as English, Chinese, Indonesian, Vietnamese, Thai, Tagalog, Malay, Burmese, Khmer, Lao, Tamil, and Javanese, making it suitable for multilingual applications in the region.

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SeaLLMs-v3-7B-Chat: Multilingual LLM for Southeast Asia

SeaLLMs-v3-7B-Chat is the latest 7.6 billion parameter model from the SeaLLMs family, specifically designed and fine-tuned for chat and instruction-following in Southeast Asian languages. It demonstrates strong performance across various benchmarks, outperforming models of similar sizes in key areas.

Key Capabilities & Differentiators

  • Multilingual Proficiency: Optimized for a broad spectrum of SEA languages including English, Chinese, Indonesian, Vietnamese, Thai, Tagalog, Malay, Burmese, Khmer, Lao, Tamil, and Javanese.
  • Enhanced Instruction Following: Significantly improved capability in understanding and executing multi-turn human instructions.
  • Reduced Hallucination: Exhibits lower instances of hallucination and provides more contextually appropriate responses, particularly for Southeast Asian cultural queries.
  • Strong Performance: Achieves competitive results in multilingual world knowledge (M3Exam), instruction-following (SeaBench), and multilingual mathematics (MGSM), with a notable 73.1% average on MGSM and 36.52 chrF score for translation.
  • Improved Trustworthiness: Demonstrates high refusal F1 scores (e.g., 71.59% average) for non-existing entities and strong safety rates (e.g., 79.75% average) against harmful prompts across multiple languages.

Ideal Use Cases

This model is particularly well-suited for applications requiring robust multilingual communication and instruction-following capabilities within the Southeast Asian linguistic landscape, where cultural sensitivity and factual accuracy are paramount.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

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