llmware
/

bling-1b-0.1

Text Generation

text-generation-inference

Model card Files Files and versions Community

doberst commited on Nov 12, 2023

Commit

faef3db

•

1 Parent(s): f92f7f8

Update README.md

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -86,7 +86,7 @@ The fastest way to get started with BLING is through direct import in transforme
     tokenizer = AutoTokenizer.from_pretrained("llmware/bling-1b-0.1")
     model = AutoModelForCausalLM.from_pretrained("llmware/bling-1b-0.1")
-Please refer to the generation_test .py files in the Files repository, which includes 200 samples and script to test the model.  The generation_test_llmware_script.py includes built-in capabilities for fact-checking, as well as easy integration with parsing to swap out the test set for actual RAG documents.
 The BLING model was fine-tuned with a simple "\<human> and \<bot> wrapper", so to get the best results, wrap inference entries as:

     tokenizer = AutoTokenizer.from_pretrained("llmware/bling-1b-0.1")
     model = AutoModelForCausalLM.from_pretrained("llmware/bling-1b-0.1")
+Please refer to the generation_test .py files in the Files repository, which includes 200 samples and script to test the model.  The **generation_test_llmware_script.py** includes built-in llmware capabilities for fact-checking, as well as easy integration with document parsing and actual retrieval to swap out the test set for RAG workflow consisting of business documents.
 The BLING model was fine-tuned with a simple "\<human> and \<bot> wrapper", so to get the best results, wrap inference entries as: