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:

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

python
1from transformers import AutoModelForCausalLM, AutoTokenizer 2model = AutoModelForCausalLM.from_pretrained("bhavinjawade/SOLAR-10B-OrcaDPO-Jawade") 3tokenizer = AutoTokenizer.from_pretrained("bhavinjawade/SOLAR-10B-OrcaDPO-Jawade")
Format the Prompt Format the chat input as a list of messages, each with a role ('system' or 'user') and content.

python
1message = [ 2 {"role": "system", "content": "You are a helpful assistant chatbot."}, 3 {"role": "user", "content": "Is the universe real? or is it a simulation? whats your opinion?"} 4] 5prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
Create a Pipeline Set up a pipeline for text generation with the loaded model and tokenizer.

python
1pipeline = transformers.pipeline( 2 "text-generation", 3 model=model, 4 tokenizer=tokenizer 5)
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.

python
1sequences = pipeline( 2 prompt, 3 do_sample=True, 4 temperature=0.7, 5 top_p=0.9, 6 num_return_sequences=1, 7 max_length=200, 8 ) 9 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