Update README.md

README.md

@@ -1,19 +1,19 @@
 ---
-license: cc-by-sa-4.0
+license: apache-2.0
 inference: false
 ---
 
-# SLIM-EXTRACT
+# SLIM-EXTRACT-TINY
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-**slim-extract** implements a specialized function-calling customizable 'extract' capability that takes as an input a context passage, a customized key, and outputs a python dictionary with key that corresponds to the customized key, with a value consisting of a list of items extracted from the text corresponding to that key, e.g., 
+**slim-extract-tiny** implements a specialized function-calling customizable 'extract' capability that takes as an input a context passage, a customized key, and outputs a python dictionary with key that corresponds to the customized key, with a value consisting of a list of items extracted from the text corresponding to that key, e.g., 
 
 &nbsp;&nbsp;&nbsp;&nbsp;`{'universities': ['Berkeley, Stanford, Yale, University of Florida, ...'] }`
 
-This model 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 fine-tuned on top of a tiny-llama 1b base.  
 
-For fast inference use, we would recommend the 'quantized tool' version, e.g.,  [**'slim-extract-tool'**](https://huggingface.co/llmware/slim-extract-tool).  
+For fast inference use, we would recommend the 'quantized tool' version, e.g.,  [**'slim-extract-tiny-tool'**](https://huggingface.co/llmware/slim-extract-tiny-tool).  
 
 
 ## Prompt format:
@@ -27,8 +27,8 @@ For fast inference use, we would recommend the 'quantized tool' version, e.g.,
 <details>
 <summary>Transformers Script </summary>
 
-    model = AutoModelForCausalLM.from_pretrained("llmware/slim-extract")
-    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-extract")
+    model = AutoModelForCausalLM.from_pretrained("llmware/slim-extract-tiny")
+    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-extract-tiny")
 
     function = "extract"
     params = "company"
@@ -70,7 +70,7 @@ For fast inference use, we would recommend the 'quantized tool' version, e.g.,
 <summary>Using as Function Call in LLMWare</summary>
 
     from llmware.models import ModelCatalog
-    slim_model = ModelCatalog().load_model("llmware/slim-extract")
+    slim_model = ModelCatalog().load_model("llmware/slim-extract-tiny")
     response = slim_model.function_call(text,params=["company"], function="extract")
 
     print("llmware - llm_response: ", response)