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README.md
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@@ -31,7 +31,7 @@ This is quantized version of [SeaLLMs/SeaLLMs-v3-7B-Chat](https://huggingface.co
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<p align="center">
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<a href="https://damo-nlp-sg.github.io/SeaLLMs/" target="_blank" rel="noopener">Website</a>
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<a href="https://huggingface.co/SeaLLMs/
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<a href="https://huggingface.co/spaces/SeaLLMs/SeaLLM-Chat" target="_blank" rel="noopener"> 🤗 DEMO</a>
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@@ -51,7 +51,9 @@ We introduce **SeaLLMs-v3**, the latest series of the SeaLLMs (Large Language Mo
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SeaLLMs is tailored for handling a wide range of languages spoken in the SEA region, including English, Chinese, Indonesian, Vietnamese, Thai, Tagalog, Malay, Burmese, Khmer, Lao, Tamil, and Javanese.
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This page introduces the SeaLLMs-v3-7B-Chat model, specifically fine-tuned to follow human instructions effectively for task completion, making it directly applicable to your applications.
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### Get started with `Transformers`
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device = "cuda" # the device to load the model onto
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model = AutoModelForCausalLM.from_pretrained(
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"SeaLLMs/
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torch_dtype=torch.bfloat16,
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device_map=device
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)
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tokenizer = AutoTokenizer.from_pretrained("SeaLLMs/
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# prepare messages to model
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prompt = "Hiii How are you?"
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device = "cuda" # the device to load the model onto
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model = AutoModelForCausalLM.from_pretrained(
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"SeaLLMs/
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torch_dtype=torch.bfloat16,
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device_map=device
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)
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tokenizer = AutoTokenizer.from_pretrained("SeaLLMs/
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# prepare messages to model
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messages = [
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<p align="center">
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<a href="https://damo-nlp-sg.github.io/SeaLLMs/" target="_blank" rel="noopener">Website</a>
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<a href="https://huggingface.co/SeaLLMs/SeaLLMs-v3-7B-Chat" target="_blank" rel="noopener"> 🤗 Tech Memo</a>
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<a href="https://huggingface.co/spaces/SeaLLMs/SeaLLM-Chat" target="_blank" rel="noopener"> 🤗 DEMO</a>
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SeaLLMs is tailored for handling a wide range of languages spoken in the SEA region, including English, Chinese, Indonesian, Vietnamese, Thai, Tagalog, Malay, Burmese, Khmer, Lao, Tamil, and Javanese.
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This page introduces the **SeaLLMs-v3-7B-Chat** model, specifically fine-tuned to follow human instructions effectively for task completion, making it directly applicable to your applications.
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You may also refer to the [SeaLLMs-v3-1.5B-Chat](https://huggingface.co/SeaLLMs/SeaLLMs-v3-1.5B-Chat) model which requires much lower computational resources and can be easily loaded locally.
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### Get started with `Transformers`
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device = "cuda" # the device to load the model onto
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model = AutoModelForCausalLM.from_pretrained(
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"SeaLLMs/SeaLLMs-v3-7B-Chat", # can change to "SeaLLMs/SeaLLMs-v3-1.5B-Chat" if your resource is limited
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torch_dtype=torch.bfloat16,
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device_map=device
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)
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tokenizer = AutoTokenizer.from_pretrained("SeaLLMs/SeaLLMs-v3-7B-Chat")
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# prepare messages to model
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prompt = "Hiii How are you?"
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device = "cuda" # the device to load the model onto
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model = AutoModelForCausalLM.from_pretrained(
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"SeaLLMs/SeaLLMs-v3-7B-Chat", # can change to "SeaLLMs/SeaLLMs-v3-1.5B-Chat" if your resource is limited
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torch_dtype=torch.bfloat16,
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device_map=device
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)
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tokenizer = AutoTokenizer.from_pretrained("SeaLLMs/SeaLLMs-v3-7B-Chat")
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# prepare messages to model
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messages = [
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