README.md

---
license: apache-2.0  
inference: false  
---

# SLIM-NLI

<!-- Provide a quick summary of what the model is/does. -->

**slim-nli** 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-nli has been fine-tuned for **natural language inference (nli)** function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:  

&nbsp;&nbsp;&nbsp;&nbsp;`{"evidence": ["contradicts"]}`


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-nli-tool'**](https://huggingface.co/llmware/slim-nli-tool).  


## Prompt format:

`function = "classify"`  
`params = "nli"`  
`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-nli")
    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-nli")

    function = "classify"
    params = "evidence"

    # expects two statements - the first is evidence, and the second is a conclusion  
    
    text1 = "The stock market declined yesterday as investors worried increasingly about the slowing economy."  
    text2 = "Investors are positive about the market."  

    # the two statements are concatenated with optional/helpful "Evidence: " and "Conclusion: " added  
    
    text = "Evidence: " + text1 + "\n" + "Conclusion: " + text2  
    
    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-nli")

    # input text - expects two statements - the first is evidence, and the second is a conclusion    
    text1 = "The stock market declined yesterday as investors worried increasingly about the slowing economy."    
    text2 = "Investors are positive about the market."  
    text = "Evidence: " + text1 + "\n" + "Conclusion: " + text2  
    
    response = slim_model.function_call(text,params=["evidence"], function="classify")

    print("llmware - llm_response: ", response)

</details>  

    
## Model Card Contact

Darren Oberst & llmware team  

[Join us on Discord](https://discord.gg/MhZn5Nc39h)