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SOLAR-10B-OrcaDPO-Jawade

Overview

This model card is instruction finetuned version of upstage/SOLAR-10.7B-Instruct-v1.0 model. Trained on the Intel DPO Orca dataset using LoRA. Though it should be noted SOLAR-10.7B paper states that the original model for alignment was trained on Intel ORCA DPO pairs. Retraining using DPO and LoRA shows slight (<1%) improvement on OpenLLM Leaderboard benchmarks against SOLAR 10.7B-Instruct and significant over SOLAR 10.7B

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How to Use This Model

To use the model bhavinjawade/SOLAR-10B-OrcaDPO-Jawade, follow these steps:

  1. Import and Load the Model and Tokenizer Begin by importing the model and tokenizer. Load them using the from_pretrained method.

    from transformers import AutoModelForCausalLM, AutoTokenizer
    model = AutoModelForCausalLM.from_pretrained("bhavinjawade/SOLAR-10B-OrcaDPO-Jawade")
    tokenizer = AutoTokenizer.from_pretrained("bhavinjawade/SOLAR-10B-OrcaDPO-Jawade")
    
  2. Format the Prompt Format the chat input as a list of messages, each with a role ('system' or 'user') and content.

    message = [
        {"role": "system", "content": "You are a helpful assistant chatbot."},
        {"role": "user", "content": "Is the universe real? or is it a simulation? whats your opinion?"}
    ]
    prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
    
  3. Create a Pipeline Set up a pipeline for text generation with the loaded model and tokenizer.

    pipeline = transformers.pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer
    )
    
  4. Generate Text Use the pipeline to generate a sequence of text based on the prompt. You can adjust parameters like temperature and top_p for different styles of responses.

    sequences = pipeline(
          prompt,
          do_sample=True,
        temperature=0.7,
           top_p=0.9,
           num_return_sequences=1,
           max_length=200,
       )
     print(sequences[0]['generated_text'])
    

This setup allows you to utilize the capabilities of the bhavinjawade/SOLAR-10B-OrcaDPO-Jawade model for generating responses to chat inputs.

License

  • Type: MIT License
  • Details: This license permits reuse, modification, and distribution for both private and commercial purposes under the terms of the MIT License.

Model Details

  • Model Name: SOLAR-10.7B-Instruct-v1.0
  • Organization: Upstage
  • Training Dataset: Intel/orca_dpo_pairs
  • Technique Used: LoRA (Low-Rank Adaptation)

Contact Information

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Dataset used to train bhavinjawade/SOLAR-10B-OrcaDPO-Jawade