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Update README.md
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README.md
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@@ -115,6 +115,40 @@ demo = gr.ChatInterface(
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demo.queue().launch()
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```
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# Base Model
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[tokyotech-llm/Swallow-MS-7b-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MS-7b-v0.1)
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demo.queue().launch()
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```
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### Textstreamer
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```
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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model_name = "aixsatoshi/Honyaku-7b-v2"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Define the streamer
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streamer = TextStreamer(tokenizer)
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# Define the English prompt
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english_prompt = """
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Machine translation accuracy varies greatly across languages.
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Key challenges include context understanding, idiomatic expressions, and syntactic differences.
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Advanced models leverage AI to enhance translation quality, focusing on nuances and cultural relevance.
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Continuous updates and training on diverse datasets are essential for improving accuracy and expanding language support, making global communication more accessible and effective.
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"""
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# Prepare the prompt for English to Japanese translation
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prompt = f"<english>: {english_prompt} </english>\n\n<japanese>:"
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# Tokenize the input text and move to CUDA device
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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# Generate the output using the model and streamer
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output = model.generate(**inputs, max_new_tokens=4096, do_sample=True, top_k=20, top_p=0.95, streamer=streamer)
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```
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# Base Model
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[tokyotech-llm/Swallow-MS-7b-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MS-7b-v0.1)
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