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Update README.md

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Added text-generation pipeline documentation

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  1. README.md +7 -1
README.md CHANGED
@@ -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=torch.bfloat16, 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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  ```
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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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+
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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