Update README.md

README.md

@@ -82,14 +82,14 @@ Note:  this was the smallest model that we were able to train to consistently re
 
 The fastest way to get started with BLING is through direct import in transformers:
 
-from transformers import AutoTokenizer, AutoModelForCausalLM  
-tokenizer = AutoTokenizer.from_pretrained("llmware/bling-1b-0.1")  
-model = AutoModelForCausalLM.from_pretrained("llmware/bling-1b-0.1")  
+    from transformers import AutoTokenizer, AutoModelForCausalLM  
+    tokenizer = AutoTokenizer.from_pretrained("llmware/bling-1b-0.1")  
+    model = AutoModelForCausalLM.from_pretrained("llmware/bling-1b-0.1")  
 
 
 The BLING model was fine-tuned with a simple "\<human> and \<bot> wrapper", so to get the best results, wrap inference entries as:
 
-full_prompt = "\<human>\: " + my_prompt + "\n" + "\<bot>\:"
+    full_prompt = "\<human>\: " + my_prompt + "\n" + "\<bot>\:"
 
 The BLING model was fine-tuned with closed-context samples, which assume generally that the prompt consists of two sub-parts:
 
@@ -98,8 +98,31 @@ The BLING model was fine-tuned with closed-context samples, which assume general
 
 To get the best results, package "my_prompt" as follows:
 
-my_prompt = {{text_passage}} + "\n" + {{question/instruction}}
+    my_prompt = {{text_passage}} + "\n" + {{question/instruction}}  
 
+If you are using a HuggingFace generation script:
+
+    # prepare prompt packaging used in fine-tuning process
+    new_prompt = "<human>: " + entries["context"] + "\n" + entries["query"] + "\n" + "<bot>:"
+
+    inputs = tokenizer(new_prompt, return_tensors="pt")  
+    start_of_output = len(inputs.input_ids[0])
+
+    #   temperature: set at 0.3 for consistency of output
+    #   max_new_tokens:  set at 100 - may prematurely stop a few of the summaries
+
+    outputs = model.generate(
+            inputs.input_ids.to(device),
+            eos_token_id=tokenizer.eos_token_id,
+            pad_token_id=tokenizer.eos_token_id,
+            do_sample=True,
+            temperature=0.3,
+            max_new_tokens=100,
+            )
+
+    output_only = tokenizer.decode(outputs[0][start_of_output:],skip_special_tokens=True)
+
+Please also refer to two sample test scripts in the files repository for full examples.  
 
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