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| # This is a project of Chakra Lab LLC. All rights reserved. | |
| import gradio as gr | |
| import os | |
| import torch | |
| from peft import PeftConfig, PeftModel | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| #base_model_name = "google/gemma-2b" | |
| #base_model_name = "google/gemma-7b" | |
| base_model_name = "google/gemma-2-9b" | |
| #adapter_model_name = "samidh/cope-g2b-2c-hs.s1.5fpc.9-sx.s1.5.9o-vl.s1.5.9-hr.s5-sh.s5.l5e5-e3-d25-r8" | |
| #adapter_model_name = "samidh/cope-g7bq-2c-hs.s1.5fpc.9-sx.s1.5.9o-VL.s1.5.9-HR.s5-SH.s5-l5e5-e3-d25-r8" | |
| adapter_model_name = "cope-project/cope-g2.9b-2c-hs.s1.5fpc.9-sx.s1.5.9o-vl.s1.5.9-hr.s5-sh.s5.l5e5-e3-d0-r8" | |
| bnb_config = BitsAndBytesConfig( | |
| load_in_8bit=True, | |
| ) | |
| """ | |
| bnb_config = BitsAndBytesConfig( | |
| load_in_4bit=True, | |
| bnb_4bit_quant_type="nf4", | |
| bnb_4bit_compute_dtype=torch.bfloat16, | |
| bnb_4bit_use_double_quant=True | |
| ) | |
| """ | |
| model = AutoModelForCausalLM.from_pretrained(base_model_name, | |
| token=os.environ['HF_TOKEN'], | |
| quantization_config=bnb_config, | |
| device_map="auto") | |
| model = PeftModel.from_pretrained(model, adapter_model_name, token=os.environ['HF_TOKEN']) | |
| model.merge_and_unload() | |
| model = model.to(device) | |
| tokenizer = AutoTokenizer.from_pretrained(base_model_name) | |
| #gr.load("models/samidh/cope-gemma2b-hs-2c-skr-s1.5.9.d25", hf_token=os.environ['HF_TOKEN']).launch() | |
| PROMPT = """ | |
| INSTRUCTIONS | |
| ============ | |
| Examine the given POLICY and determine if the given CONTENT meets the criteria for ANY of the LABELS. Answer "1" if yes, and "0" if no. | |
| POLICY | |
| ====== | |
| {policy} | |
| CONTENT | |
| ======= | |
| {content} | |
| ANSWER | |
| ====== | |
| """ | |
| DEFAULT_POLICY = """# Policy | |
| ## Overview | |
| This policy guides the classification of content related to expressions that may be sensitive or controversial, aiming to foster a community where open dialogue is encouraged while maintaining respect for all individuals. | |
| ## Definition of Terms | |
| - Protected Classes: Individuals or groups of individuals defined by sharing a race, ethnicity, religion, disability, sex, gender identity, or sexual orientation. The scope is narrowed to focus on characteristics commonly recognized in anti-discrimination laws. Religions themselves, as concepts ARE NOT protected - only their members are. | |
| - Non-Protected Classes: Groups defined by other characteristics, such as age, socioeconomic status, and education, which are typically excluded partially or entirely from anti-discrimination laws. | |
| ## Interpretation of Language | |
| ## Definition of Labels | |
| ### (HS): Hate Speech | |
| #### Includes | |
| - Explicit Violence Advocacy: Direct and clear threats of or praise for violence or harm against individuals or groups based on their membership in a particular protected class. Stating an intention to defend against aggression DOES NOT qualify | |
| - Intentional Dehumanization: Statements that clearly depict individuals or groups as inherently ""other"", alien, animalistic, unintelligent, immoral, unclean, or less-than-fully-human based on their membership in a particular protected class in a way that justifies harm or discrimination. | |
| - Targeted Use of Derogatory Slurs: Targeting another person or group of people using a one-word name for a particular protected class that has an inherent negative connotation (e.g. Nigger, Kike, Cunt, Retard). Multi-word terms are never slurs. | |
| - Explicit Discrimination Advocacy: Direct and clear calls for exclusion, segregation, or discrimination against individuals or groups based on their membership in a particular protected class, with a clear intent to promote inequality. | |
| - Direct Hateful Insults: Content that directly addresses another person or group of people the second person (e.g. ""You over there"") and insults them based on their membership in a particular protected class | |
| #### Excludes | |
| - Artistic and Educational Content: Expressions intended for artistic, educational, or documentary purposes that discuss sensitive topics but do not advocate for violence or discrimination against individuals or groups based on their membership in a particular protected class. | |
| - Political and Social Commentary: Commentary on political issues, social issues, and political ideologies that does not directly incite violence or discrimination against individuals or groups based on their membership in a particular protected class. | |
| - Rebutting Hateful Language: Content that rebuts, condemns, questions, criticizes, or mocks a different person's hateful language or ideas OR that insults the person advocating those hateful | |
| - Quoting Hateful Language: Content in which the author quotes someone else's hateful language or ideas while discussing, explaining, or neutrally factually presenting those ideas. | |
| - Describing Sectarian Violence: Content that describes, but does not endorse or praise, violent physical injury against a specifically named race, ethnicity, nationality, sexual orientation, or religious community by another specifically named race, ethnicity, nationality, sexual orientation, or religious community | |
| """ | |
| DEFAULT_CONTENT = "LLMs steal our jobs." | |
| # Function to make predictions | |
| def predict(content, policy): | |
| input_text = PROMPT.format(policy=policy, content=content) | |
| input_ids = tokenizer.encode(input_text, return_tensors="pt").to(model.device) | |
| with torch.inference_mode(): | |
| outputs = model(input_ids) | |
| logits = outputs.logits[:, -1, :] # Get logits for the last token | |
| predicted_token_id = torch.argmax(logits, dim=-1).item() | |
| decoded_output = tokenizer.decode([predicted_token_id]) | |
| if decoded_output == '0': | |
| return f'NON-Violating ({decoded_output})' | |
| else: | |
| return f'VIOLATING ({decoded_output})' | |
| """ | |
| outputs = model.generate(inputs, max_new_tokens=1) | |
| decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| if int(decoded_output[-1]) == 0: | |
| return f'NON-Violating ({decoded_output[-1]})' | |
| else: | |
| return f'VIOLATING ({decoded_output[-1]})' | |
| """ | |
| # Create Gradio interface | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs=[gr.Textbox(label="Content", lines=2, value=DEFAULT_CONTENT), | |
| gr.Textbox(label="Policy", lines=10, value=DEFAULT_POLICY)], | |
| outputs="label", | |
| title="CoPE Dev (Unstable)", | |
| description="See if the given content violates your given policy." | |
| ) | |
| # Launch the app | |
| iface.launch() | |