Upload README.md

README.md

@@ -1,3 +1,90 @@
 ---
-license: apache-2.0
+license: apache-2.0  
+inference: false  
 ---
+
+# SLIM-TOPICS
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+**slim-topics** is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") model series, consisting of small, specialized decoder-based models, fine-tuned for function-calling.  
+
+slim-sentiment has been fine-tuned for **topic analysis** function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:  
+
+&nbsp;&nbsp;&nbsp;&nbsp;`{"topics": ["..."]}`
+
+
+SLIM models are designed to generate structured outputs that can be used programmatically as part of a multi-step, multi-model LLM-based automation workflow.  
+
+Each slim model has a 'quantized tool' version, e.g.,  [**'slim-topics-tool'**](https://huggingface.co/llmware/slim-topics-tool).  
+
+
+## Prompt format:
+
+`function = "classify"`  
+`params = "topics"`  
+`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-topics")
+    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-topics")
+
+    function = "classify"
+    params = "topic"
+
+    text = "The stock market declined yesterday as investors worried increasingly about the slowing economy."  
+    
+    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=0.3,
+        max_new_tokens=100
+    )
+
+    output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True)
+
+    print("output only: ", output_only)  
+
+    # 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-topics")
+    response = slim_model.function_call(text,params=["topics"], function="classify")
+
+    print("llmware - llm_response: ", response)
+
+</details>  
+
+    
+## Model Card Contact
+
+Darren Oberst & llmware team  
+
+[Join us on Discord](https://discord.gg/MhZn5Nc39h)
+
+