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README.md
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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gen_kwargs = {
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"max_new_tokens": 100,
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"top_k": 70,
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"top_p": 0.8,
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prepared = tokenizer.encode(inp, return_tensors='pt').to(model.device)
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out = model.generate(input_ids=prepared, **gen_kwargs)
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generated = tokenizer.decode(out[0])
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<!-- Provide a longer summary of what this model is. -->
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## Uses
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<!-- gen_kwargs = {
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"max_new_tokens": 100,
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"top_k": 70,
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"top_p": 0.8,
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prepared = tokenizer.encode(inp, return_tensors='pt').to(model.device)
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out = model.generate(input_ids=prepared, **gen_kwargs)
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generated = tokenizer.decode(out[0])
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