Update README.md

README.md

@@ -1,13 +1,13 @@
 ---
-license: cc-by-sa-4.0
+license: apache-2.0
 inference: false
 ---
 
-# SLIM-SUMMARY
+# SLIM-SUMMARY-TINY
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-**slim-summary** is a small, specialized model finetuned for summarize function-calls, generating output consisting of a python list of distinct summary points.  
+**slim-summary-tiny** is a small, specialized model finetuned for summarize function-calls, generating output consisting of a python list of distinct summary points.  
 
 As an experimental feature in the model, there is an optional list size that can be passed with the parameters in invoking the model to guide the model to a specific number of response elements.  
 
@@ -15,9 +15,9 @@ Input is a text passage, and output is a list of the form:
 
 &nbsp;&nbsp;&nbsp;&nbsp;`['summary_point1', 'summary_point2', 'summary_point3']`
 
-This model is 2.7B parameters, small enough to run on a CPU, and is fine-tuned on top of [**llmware/bling-stable-lm-3b-4e1t-v0**](https://huggingface.co/llmware/bling-stable-lm-3b-4e1t-v0), which in turn, is a fine-tune of stabilityai/stablelm-3b-4elt.
+This model is 1.1B parameters, small enough to run on a CPU, and is fine-tuned on top of a tiny-llama base.  
 
-For fast inference use of this model, we would recommend using the 'quantized tool' version, e.g.,  [**'slim-summary-tool'**](https://huggingface.co/llmware/slim-summary-tool).  
+For fast inference use of this model, we would recommend using the 'quantized tool' version, e.g.,  [**'slim-summary-tiny-tool'**](https://huggingface.co/llmware/slim-summary-tiny-tool).  
 
 ## Usage Tips  
 
@@ -41,8 +41,8 @@ For fast inference use of this model, we would recommend using the 'quantized to
 <details>
 <summary>Transformers Script </summary>
 
-    model = AutoModelForCausalLM.from_pretrained("llmware/slim-summary")
-    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-summary")
+    model = AutoModelForCausalLM.from_pretrained("llmware/slim-summary-tiny")
+    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-summary-tiny")
 
     function = "summarize"
     params = "key points (3)"
@@ -87,7 +87,7 @@ For fast inference use of this model, we would recommend using the 'quantized to
 <summary>Using as Function Call in LLMWare</summary>
 
     from llmware.models import ModelCatalog
-    slim_model = ModelCatalog().load_model("llmware/slim-summary")
+    slim_model = ModelCatalog().load_model("llmware/slim-summary-tiny")
     response = slim_model.function_call(text,params=["key points (3)], function="summarize")
 
     print("llmware - llm_response: ", response)