goldbach7/openchat-model-ospc

This model achieved 0.7600 AUROC for the Online Safety Prize Challenge organized by AI Singapore. It is fine-tuned from Openchat model. Note, this model is already quantized to 4-bit GPTQ.

Training details

This model is trained on a synthetic dataset generated by Apache 2.0 licensed LLMs (Mistral and Mixtral).

How to use

You can either use it to classify text or text + extracted visual description.

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"

model = AutoModelForCausalLM.from_pretrained(
    'billcai/openchat-model-ospc',
    torch_dtype="auto",
    device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained('billcai/openchat-model-ospc')

promptText = "<the prompt to be tested>"
# promptText = "Image: <image caption>, Caption: <text within image>"

prompt = """Check if there is unsafe content in the following message. Unsafe content is defined in the following categories.
    <BEGIN UNSAFE CONTENT CATEGORIES>
    Contains hate speech and harmful content. Harmful content centres on divisive and contentious subject matters. Typical areas include:
    - Racial disparities
    - Religious beliefs and practices
    - Sexual orientation
    - Nationalistic sentiments and xenophobia against migrants
    - Socio-economic divides and class struggles
    - Age-related biases
    - Gender discrimination
    - Discrimination based on illnesses and disabilities
    These content may be based on Singapore context, including the use of non-English languages like Mandarin, Malay and Tamil.
    <END UNSAFE CONTENT CATEGORIES>

    This is the message you are checking if it contains unsafe content.
    <BEGIN MESSAGE>
    {}
    <END MESSAGE>

    Provide your safety assessment for the message above. Your first line MUST read 'safe' or 'unsafe'.
    """.format(
            promptText
        )

messages = [
    {"role": "system", "content": "You are a helpful assistant deciding whether a piece of content is safe or unsafe based on specific criteria. The content may contain irrelevant words as it has been extracted from an image."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to('cuda')
generation_output = llm.generate(
    model_inputs.input_ids,
    max_new_tokens=10,
    temperature=0.1,
    output_logits=True,
    return_dict_in_generate=True
)
generated_sequences = generation_output['sequences']
generated_logits = generation_output['logits']
unsafeTokenId = tokenizer.encode('unsafe')[1]
safeTokenId = tokenizer.encode('safe')[1]
firstLogit = generated_logits[0].cpu().numpy()
prob = softmax([
    firstLogit[0,unsafeTokenId],
    firstLogit[0,safeTokenId],
    ])
print(prob) # first is score for unsafe token.

License

Apache 2.0