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--- |
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license: cc-by-sa-4.0 |
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inference: false |
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--- |
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# SLIM-SA-NER-3B |
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<!-- Provide a quick summary of what the model is/does. --> |
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**slim-sa-ner-3b** combines two of the most popular traditional classifier functions (**Sentiment Analysis** and **Named Entity Recognition**), and reimagines them as function calls on a specialized decoder-based LLM, generating output consisting of a python dictionary with keys corresponding to sentiment, and NER identifiers, such as people, organization, and place, e.g.: |
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`{'sentiment': ['positive'], people': ['..'], 'organization': ['..'],'place': ['..]}` |
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This 'combo' model is designed to illustrate the potential power of using function calls on small, specialized models to enable a single model architecture to combine the capabilities of what were traditionally two separate model architectures on an encoder. |
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The intent of SLIMs is to forge a middle-ground between traditional encoder-based classifiers and open-ended API-based LLMs, providing an intuitive, flexible natural language response, without complex prompting, and with improved generalization and ability to fine-tune to a specific domain use case. |
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This model is fine-tuned on top of [**llmware/bling-stable-lm-3b-4e1t-v0**](https://huggingface.co./llmware/bling-stable-lm-3b-4e1t-v0), which in turn, is a fine-tune of stabilityai/stablelm-3b-4elt. |
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Each slim model has a 'quantized tool' version, e.g., [**'slim-sa-ner-3b-tool'**](https://huggingface.co./llmware/slim-sa-ner-3b-tool). |
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## Prompt format: |
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`function = "classify"` |
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`params = "sentiment, person, organization, place"` |
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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-sa-ner-3b") |
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tokenizer = AutoTokenizer.from_pretrained("llmware/slim-sa-ner-3b") |
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function = "classify" |
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params = "topic" |
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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=0.3, |
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max_new_tokens=100 |
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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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# 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-sa-ner-3b") |
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response = slim_model.function_call(text,params=["sentiment", "people", "organization", "place"], function="classify") |
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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) |
