query_templatizer / README.md
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metadata
base_model:
  - meta-llama/Llama-3.2-1B-Instruct
datasets:
  - fineinstructions/templates_raw_subsample
tags:
  - datadreamer
  - datadreamer-0.39.0
  - synthetic
  - text-generation
widget:
  - text: >+
      <|start_header_id|>system<|end_header_id|>


      Cutting Knowledge Date: December 2023

      Today Date: 31 Jan 2025


      <|eot_id|><|start_header_id|>user<|end_header_id|>


      What volleyball exercises should I do I'm almost in high school and i do
      volleyball excellence five times a week (basically an advanced class in
      school with experienced volleyball coaches) , we have 2-3 skill training
      sessions a week which i feel like isn't enough for me as I would like to
      improve my skills almost every day.


      &amp;#x200B;


      What i wanted to know was what setting, digging, serving and spiking
      exercises could i do that would help me improve all of my skills (I have a
      large area to practice all these things so space isn't an
      issue).<|eot_id|><|start_header_id|>assistant<|end_header_id|>

    example_title: Example 1
  - text: >+
      <|start_header_id|>system<|end_header_id|>


      Cutting Knowledge Date: December 2023

      Today Date: 31 Jan 2025


      <|eot_id|><|start_header_id|>user<|end_header_id|>


      ![enter image description here](https://i.stack.imgur.com/4ckzp.png)


      Hi i am trying to make java desktop application where i am using `JLabel`
      I wrote some test on `JLabel` I want to set text from the top and I am
      using multiple line in `JLabel` I want to set different different color
      for every line.


      Here is my code:


      ```

      JLabel label = new JLabel("<html>Case
      &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Item&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
      CaseNum<br>Party1<br>Party2</html>");


      ```


      How can I achieve
      this?<|eot_id|><|start_header_id|>assistant<|end_header_id|>

    example_title: Example 2
  - text: >+
      <|start_header_id|>system<|end_header_id|>


      Cutting Knowledge Date: December 2023

      Today Date: 31 Jan 2025


      <|eot_id|><|start_header_id|>user<|end_header_id|>


      Is there a cheaper substitute for the knipex pliers? I have wanted a pair
      of the knipex crimpers/pliers/cutters for a while, but I can't afford $70
      for a pair.  Is there a pair that is as good, but
      cheaper?<|eot_id|><|start_header_id|>assistant<|end_header_id|>

    example_title: Example 3
pipeline_tag: text-generation

Model Card

Add more information here

Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, Conversation

tokenizer = AutoTokenizer.from_pretrained('fineinstructions/query_templatizer', revision=None) # Load tokenizer
tokenizer.padding_side = 'left'
model = AutoModelForCausalLM.from_pretrained('fineinstructions/query_templatizer_full_end_to_end_s2', revision=None) # Load model
pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, pad_token_id=tokenizer.pad_token_id, return_full_text=False)

inputs = ["What volleyball exercises should I do I'm almost in high school and i do volleyball excellence five times a week (basically an advanced class in school with experienced volleyball coaches) , we have 2-3 skill training sessions a week which i feel like isn't enough for me as I would like to improve my skills almost every day.\n\n&amp;#x200B;\n\nWhat i wanted to know was what setting, digging, serving and spiking exercises could i do that would help me improve all of my skills (I have a large area to practice all these things so space isn't an issue)."]
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))

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.