SLIM-NLI
slim-nli is part of the SLIM ("Structured Language Instruction Model") 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.:
{"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'.
Prompt format:
function = "classify"
params = "nli"
prompt = "<human> " + {text} + "\n" +
"<{function}> " + {params} + "</{function}>" + "\n<bot>:"
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)
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)
Model Card Contact
Darren Oberst & llmware team