SLIM-NER

slim-ner is part of the SLIM ("Structured Language Instruction Model") model series, consisting of 1b parameter small, specialized decoder-based models, fine-tuned for function-calling.

slim-ner has been fine-tuned for named entity extraction function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:

    {"people": ["..."], "organization":["..."], "location": ["..."]}

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.

SLIM models can be used 'out of the box' for rapid prototyping in most general purpose use cases, and are designed to serve as a solid base that can be easily fine-tuned and adapted for specialized production use cases.

Each slim model has a 'quantized tool' version, e.g., 'slim-ner-tool'.

Prompt format:

function = "classify"
params = "people, organization, location"
prompt = "<human> " + {text} + "\n" +
                      "<{function}> " + {params} + "</{function}>" + "\n<bot>:"

Transformers Script

model = AutoModelForCausalLM.from_pretrained("llmware/slim-ner")
tokenizer = AutoTokenizer.from_pretrained("llmware/slim-ner")

function = "classify"
params = "people, organization, location"

text = "Yesterday, in Redmond, Satya Nadella announced that Microsoft would be launching a new AI strategy."  

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)

Using as Function Call in LLMWare

from llmware.models import ModelCatalog
slim_model = ModelCatalog().load_model("llmware/slim-ner")
response = slim_model.function_call(text,params=["people","organization","location"], function="classify")

print("llmware - llm_response: ", response)

Model Card Contact

Darren Oberst & llmware team

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