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--- |
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base_model: meta-llama/Llama-3.2-1B-Instruct |
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datasets: |
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- fineinstructions/template_instantiator_training |
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tags: |
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- datadreamer |
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- datadreamer-0.46.0 |
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- synthetic |
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- text-generation |
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pipeline_tag: text-generation |
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--- |
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This model will take a instruction template in the format of [FineTemplates](https://huggingface.co/datasets/fineinstructions/finetemplates) and a document and return an instantiated instruction and answer pair. |
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The output will be a JSON object. |
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## Simple Usage Example |
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```python |
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import json |
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import re |
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
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# Helper to expand excerpts in the answer |
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def expand(document, text): |
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excerpt_pattern = r"<excerpt>(.*?)<\.\.\.>(.*?)</excerpt>" |
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matches = re.findall(excerpt_pattern, text, flags=re.DOTALL) |
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replacements = {} |
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for prefix, suffix in matches: |
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match = re.search( |
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re.escape(prefix) + r" (.*?) " + re.escape(suffix), |
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document, |
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flags=re.DOTALL, |
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) |
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try: |
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if match: |
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replacements[f"<excerpt>{prefix}<...>{suffix}</excerpt>"] = match.group( |
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0 |
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) |
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else: |
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return None |
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except Exception: |
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return None |
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for old, new in replacements.items(): |
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text = text.replace(old, new) |
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return text |
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# Load tokenizer and model |
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tokenizer = AutoTokenizer.from_pretrained('fineinstructions/template_instantiator', revision=None) |
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tokenizer.padding_side = 'left' |
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model = AutoModelForCausalLM.from_pretrained('fineinstructions/template_instantiator', revision=None) |
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pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, pad_token_id=tokenizer.pad_token_id, return_full_text=False) |
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# Run inference to instantiate the instruction template and generate an answer |
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inputs = [json.dumps({ |
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"instruction_template": "...", |
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"document": "..." |
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}, indent=2)] |
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prompts = [tokenizer.apply_chat_template([{'role': 'user', 'content': i}], tokenize=False, add_generation_prompt=True) for i in inputs] |
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generations = pipe(prompts, max_length=131072, truncation=True, temperature=None, top_p=None, do_sample=False) |
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output = generations[0][0]['generated_text'] |
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output_json = json.loads() |
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# Expand the answer |
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output_json["answer"] = expand(document=inputs[0]["document"], text=output_json["answer"]) |
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# Print the output JSON |
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print(output_json) |
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##### Output JSON: |
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# { |
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# .. |
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# } |
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# |
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``` |
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--- |
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This model was trained with a synthetic dataset with [DataDreamer 🤖💤](https://datadreamer.dev). The synthetic dataset card and model card can be found [here](datadreamer.json). The training arguments can be found [here](training_args.json). |
