nectororca-solar10b-jawade

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

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

• https://bhavinjawade.github.io