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--- |
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license: apache-2.0 |
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metrics: |
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- accuracy |
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pipeline_tag: text2text-generation |
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tags: |
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- legal |
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- text-generation-inference |
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datasets: |
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- lgaalves/camel-ai-physics |
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--- |
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### Model Description |
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This model is trained on a large physics dataset, specifically “Special Relativity,” “Dark Matter,” “Black Holes,” “Quantum Mechanics,” “Plasma Physics,” “Particle Physics,” “Specific Theory,” “Nuclear Physics.” Provides expertise in "Atomic Physics," "Quantum Field Theory," "Gravitational Waves," "Electromagnetism," and "Chaotic Theory." It has the ability to provide information on a wide range of subjects, with the ability to answer physics questions under these headings. |
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``` python |
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from transformers import AutoTokenizer, GenerationConfig, T5ForConditionalGeneration |
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import torch |
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from peft import PeftModel |
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import time |
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import sys |
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repo_id = "sabankara/einstein" |
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model_name = 'google/flan-t5-small' |
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tokenizer = AutoTokenizer.from_pretrained(repo_id,device=0) |
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peft_model_base = T5ForConditionalGeneration.from_pretrained(model_name, torch_dtype=torch.bfloat16, local_files_only=True, device_map= {"":0}) |
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peft_model = PeftModel.from_pretrained(peft_model_base, |
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repo_id, |
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torch_dtype=torch.bfloat16, |
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is_trainable=False) |
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def print_character_by_character(text, delay=0.005, color_code="\033[36m"): |
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for char in text: |
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sys.stdout.write(f"{color_code}{char}\033[0m") |
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sys.stdout.flush() |
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time.sleep(delay) |
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def add_newline_after_punctuation(text): |
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n=17 |
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words = text.split() |
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updated_text = "" |
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for i, word in enumerate(words): |
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updated_text += word + " " |
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if (i + 1) % n == 0: |
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updated_text += "\n" |
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return updated_text.strip() |
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#example |
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prompt ="what is the black hole?" |
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda(0) |
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model_outputs = peft_model.generate(input_ids=input_ids, generation_config=GenerationConfig(min_new_tokens= 100, max_new_tokens=512, num_beams=1, do_sample=True, top_p=0.6, top_k=0, temperature=0.4,repetition_penalty=2.5)) |
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model_text_output = tokenizer.decode(model_outputs[0], skip_special_tokens=True) |
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albert_response = add_newline_after_punctuation(model_text_output) |
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print("\033[32mEintein:\033[0m") |
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print_character_by_character(albert_response) |
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sys.stdout.write("\n") |
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``` |
