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Update README.md

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  1. README.md +6 -6
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@@ -45,15 +45,15 @@ For fast inference use, we would recommend the 'quantized tool' version, e.g.,
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  eos_token_id=tokenizer.eos_token_id,
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  pad_token_id=tokenizer.eos_token_id,
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  do_sample=True,
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- temperature=0.3,
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- max_new_tokens=100
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  )
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  output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True)
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  print("output only: ", output_only)
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- `{'llm_response': {'question': ['Did Telsa stock decline more than 8% yesterday?']} }`
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  # here's the fun part
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  try:
@@ -72,10 +72,10 @@ For fast inference use, we would recommend the 'quantized tool' version, e.g.,
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  <summary>Using as Function Call in LLMWare</summary>
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  from llmware.models import ModelCatalog
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- slim_model = ModelCatalog().load_model("llmware/slim-q-gen-phi-3")
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- response = slim_model.function_call(text,params=["boolean"], function="generate")
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- print("llmware - llm_response: ", response)
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  </details>
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  eos_token_id=tokenizer.eos_token_id,
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  pad_token_id=tokenizer.eos_token_id,
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  do_sample=True,
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+ temperature=0.7,
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+ max_new_tokens=200
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  )
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  output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True)
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  print("output only: ", output_only)
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+ [OUTPUT]: {'llm_response': {'question': ['Did Telsa stock decline more than 8% yesterday?']} }
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  # here's the fun part
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  try:
 
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  <summary>Using as Function Call in LLMWare</summary>
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  from llmware.models import ModelCatalog
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+ slim_model = ModelCatalog().load_model("llmware/slim-q-gen-phi-3", sample=True, temperature=0.7)
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+ response = slim_model.function_call(text,params=["boolean"], function="generate")
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+ print("llmware - llm_response: ", response)
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  </details>
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