fineinstructions/query_templatizer_s3_full

Model Card

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Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, Conversation

tokenizer = AutoTokenizer.from_pretrained('fineinstructions/query_templatizer_s3_full', revision=None) # Load tokenizer
tokenizer.padding_side = 'left'
model = AutoModelForCausalLM.from_pretrained('fineinstructions/query_templatizer_s3_full', revision=None) # Load model
pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, pad_token_id=tokenizer.pad_token_id, return_full_text=False)

inputs = ['ok now can you give 3 very speculative ideas on how to achieve unidirectional movement that results in more energy than input using magnets and/or ZPF extraction, as simple setups?']
prompts = [tokenizer.apply_chat_template([{'role': 'user', 'content': i}], tokenize=False, add_generation_prompt=True) for i in inputs]
print(pipe(prompts, max_length=131072, do_sample=False))

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This model was trained with a synthetic dataset with DataDreamer 🤖💤. The synthetic dataset card and model card can be found here. The training arguments can be found here.