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Update README.md
Browse filesAdded text-generation pipeline documentation
README.md
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@@ -52,11 +52,17 @@ model_name = "Deci/DeciLM-7B"
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device = "cuda" # for GPU usage or "cpu" for CPU usage
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=
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inputs = tokenizer.encode("In a shocking finding, scientists discovered a herd of unicorns living in", return_tensors="pt").to(device)
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outputs = model.generate(inputs, max_new_tokens=100, do_sample=True, top_p=0.95)
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print(tokenizer.decode(outputs[0]))
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```
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## Evaluation
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device = "cuda" # for GPU usage or "cpu" for CPU usage
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", trust_remote_code=True).to(device)
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inputs = tokenizer.encode("In a shocking finding, scientists discovered a herd of unicorns living in", return_tensors="pt").to(device)
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outputs = model.generate(inputs, max_new_tokens=100, do_sample=True, top_p=0.95)
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print(tokenizer.decode(outputs[0]))
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# The model can also be used via the text-generation pipeline interface
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from transformers import pipeline
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generator = pipeline("text-generation", "Deci/DeciLM-7B", torch_dtype="auto", trust_remote_code=True, device=device)
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outputs = generator("In a shocking finding, scientists discovered a herd of unicorns living in", max_new_tokens=100, do_sample=True, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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## Evaluation
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