Upload README.md

README.md

@@ -1,3 +1,87 @@
----
-license: apache-2.0
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+---
+license: apache-2.0
+inference: false
+---
+
+# SLIM-Q-GEN-PHI-3  
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+**slim-q-gen-phi-3** implements a specialized function-calling question generation from a context passage, with output in the form of a python dictionary, e.g.,  
+
+&nbsp;&nbsp;&nbsp;&nbsp;`{'question': ['What were earnings per share in the most recent quarter?'] }  
+
+This model is finetuned on top of phi-3-mini-4k-instruct base.  
+
+For fast inference use, we would recommend the 'quantized tool' version, e.g.,  [**'slim-q-gen-phi-3-tool'**](https://huggingface.co/llmware/slim-q-gen-phi-3-tool).  
+
+
+## Prompt format:
+
+`function = "generate"`  
+`params = "{'question', 'boolean', or 'multiple choice'}"`  
+`prompt = "<human> " + {text} + "\n" + `  
+&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp;`"<{function}> " + {params} + "</{function}>" + "\n<bot>:"`  
+
+
+<details>
+<summary>Transformers Script </summary>
+
+    model = AutoModelForCausalLM.from_pretrained("llmware/slim-q-gen-phi-3")
+    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-q-gen-phi-3")
+
+    function = "generate"
+    params = "boolean"
+
+    text = "Tesla stock declined yesterday 8% in premarket trading after a poorly-received event in San Francisco yesterday, in which the company indicated a likely shortfall in revenue."  
+    
+    prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:"
+
+    inputs = tokenizer(prompt, return_tensors="pt")
+    start_of_input = len(inputs.input_ids[0])
+
+    outputs = model.generate(
+        inputs.input_ids.to('cpu'),
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.eos_token_id,
+        do_sample=True,
+        temperature=0.7,
+        max_new_tokens=200
+    )
+
+    output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True)
+
+    print("output only: ", output_only)  
+
+    [OUTPUT]:  {'llm_response': {'question': ['Did Telsa stock decline more than 8% yesterday?']} }  
+    
+    # here's the fun part
+    try:
+        output_only = ast.literal_eval(llm_string_output)
+        print("success - converted to python dictionary automatically")
+    except:
+        print("fail - could not convert to python dictionary automatically - ", llm_string_output)
+   
+   </details>  
+ 
+<details>  
+
+
+
+    
+<summary>Using as Function Call in LLMWare</summary>
+
+    from llmware.models import ModelCatalog
+    slim_model = ModelCatalog().load_model("llmware/slim-q-gen-phi-3", sample=True, temperature=0.7)  
+    response = slim_model.function_call(text,params=["boolean"], function="generate")  
+
+    print("llmware - llm_response: ", response)  
+
+</details>  
+
+    
+## Model Card Contact
+
+Darren Oberst & llmware team  
+
+[Join us on Discord](https://discord.gg/MhZn5Nc39h)