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

# SLIM-QA-GEN-TINY  

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

**slim-qa-gen-tiny** implements a specialized function-calling question generation and answer 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?'], 'answer': [$3.36]}`  

This model is finetuned on top of a tinyllama 1.1b base.  

For fast inference use, we would recommend the 'quantized tool' version, e.g.,  [**'slim-qa-gen-tiny-tool'**](https://huggingface.co/llmware/slim-qa-gen-tiny-tool).  


## Prompt format:

`function = "generate"`  
`params = "{'question, answer', '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-qa-gen-tiny")
    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-qa-gen-tiny")

    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 5% yesterday?'], 'answer': ['yes']} }  
    
    # 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-qa-gen-tiny", 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)