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SOLAR-10B-OrcaDPO-Jawade

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metadata
license: mit
datasets:
  - Intel/orca_dpo_pairs

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)

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