Upload README.md

README.md

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 license: cc-by-sa-4.0
+inference: false
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+
+# SLIM-SA-NER-3B
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+**slim-sa-ner-3b** combines two of the most popular traditional classifier functions (**Sentiment Analysis** and **Named Entity Recognition**), and reimagines them as function calls on a specialized decoder-based LLM, generating output consisting of a python dictionary with keys corresponding to sentiment, and NER identifiers, such as people, organization, and place, e.g.:  
+
+&nbsp;&nbsp;&nbsp;&nbsp;`{'sentiment': ['positive'], people': ['..'], 'organization': ['..'],'place': ['..]}`
+
+This 'combo' model is designed to illustrate the potential power of using function calls on small, specialized models to enable a single model architecture to combine the capabilities of what were traditionally two separate model architectures on an encoder.  
+
+The intent of SLIMs is to forge a middle-ground between traditional encoder-based classifiers and open-ended API-based LLMs, providing an intuitive, flexible natural language response, without complex prompting, and with improved generalization and ability to fine-tune to a specific domain use case.  
+
+
+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.
+
+Each slim model has a 'quantized tool' version, e.g.,  [**'slim-sa-ner-3b-tool'**](https://huggingface.co/llmware/slim-sa-ner-3b-tool).  
+
+
+## Prompt format:
+
+`function = "classify"`  
+`params = "sentiment, person, organization, place"`  
+`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-sa-ner-3b")
+    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-sa-ner-3b")
+
+    function = "classify"
+    params = "topic"
+
+    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=0.3,
+        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-sa-ner-3b")
+    response = slim_model.function_call(text,params=["sentiment", "people", "organization", "place"], function="classify")
+
+    print("llmware - llm_response: ", response)
+
+</details>  
+
+    
+## Model Card Contact
+
+Darren Oberst & llmware team  
+
+[Join us on Discord](https://discord.gg/MhZn5Nc39h)