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--- |
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license: apache-2.0 |
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inference: false |
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--- |
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# SLIM-Q-GEN-TINY |
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<!-- Provide a quick summary of what the model is/does. --> |
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**slim-q-gen-tiny** implements a specialized function-calling question generation from a context passage, with output in the form of a python dictionary, e.g., |
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`{'question': ['What were earnings per share in the most recent quarter?'] } |
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This model is finetuned on top of a tinyllama-1.1b base, and is intended for fast, local prototyping. |
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For fast inference use, we would recommend the 'quantized tool' version, e.g., [**slim-q-gen-tiny-tool**](https://huggingface.co/llmware/slim-q-gen-tiny-tool). |
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You also may want to checkout the finetuned phi-3 version of this model, e.g., [**slim-q-gen-phi-3-tool**](https://huggingface.co/llmware/slim-q-gen-phi-3-tool). |
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## Prompt format: |
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`function = "generate"` |
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`params = "{'question', 'boolean', or 'multiple choice'}"` |
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`prompt = "<human> " + {text} + "\n" + ` |
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`"<{function}> " + {params} + "</{function}>" + "\n<bot>:"` |
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<details> |
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<summary>Transformers Script </summary> |
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model = AutoModelForCausalLM.from_pretrained("llmware/slim-q-gen-tiny") |
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tokenizer = AutoTokenizer.from_pretrained("llmware/slim-q-gen-tiny") |
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function = "generate" |
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params = "boolean" |
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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." |
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prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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start_of_input = len(inputs.input_ids[0]) |
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outputs = model.generate( |
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inputs.input_ids.to('cpu'), |
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eos_token_id=tokenizer.eos_token_id, |
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pad_token_id=tokenizer.eos_token_id, |
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do_sample=True, |
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temperature=0.7, |
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max_new_tokens=200 |
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) |
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output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True) |
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print("output only: ", output_only) |
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[OUTPUT]: {'llm_response': {'question': ['Did Telsa stock decline more than 8% yesterday?']} } |
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# here's the fun part |
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try: |
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output_only = ast.literal_eval(llm_string_output) |
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print("success - converted to python dictionary automatically") |
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except: |
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print("fail - could not convert to python dictionary automatically - ", llm_string_output) |
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</details> |
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<details> |
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<summary>Using as Function Call in LLMWare</summary> |
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from llmware.models import ModelCatalog |
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slim_model = ModelCatalog().load_model("llmware/slim-q-gen-tiny", sample=True, temperature=0.7) |
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response = slim_model.function_call(text,params=["boolean"], function="generate") |
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print("llmware - llm_response: ", response) |
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</details> |
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## Model Card Contact |
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Darren Oberst & llmware team |
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[Join us on Discord](https://discord.gg/MhZn5Nc39h) |
