---
license: mit
datasets:
- Intel/orca_dpo_pairs
model-index:
- name: SOLAR-10B-Nector-DPO-Jawade
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 71.33
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SOLAR-10B-Nector-DPO-Jawade
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 88.62
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SOLAR-10B-Nector-DPO-Jawade
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 66.22
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SOLAR-10B-Nector-DPO-Jawade
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 70.92
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SOLAR-10B-Nector-DPO-Jawade
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 83.43
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SOLAR-10B-Nector-DPO-Jawade
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 64.59
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SOLAR-10B-Nector-DPO-Jawade
      name: Open LLM Leaderboard
---

## SOLAR-10B-Nectar-Orca-DPO-LoRA-Jawade

### Overview
This model is DPO optimized and aligned version of `upstage/SOLAR-10.7B-Instruct-v1.0` model. Trained on a mixture of Berkeley-nest Nectar dataset and Intel DPO Orca dataset using LoRA.

![model_card_image](SOLAR_ORCA.png)

## 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.

   ```python
   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.

    ```python
    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.

    ```python
    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.

   ```python
   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
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co./datasets/open-llm-leaderboard/details_bhavinjawade__SOLAR-10B-Nector-DPO-Jawade)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |74.19|
|AI2 Reasoning Challenge (25-Shot)|71.33|
|HellaSwag (10-Shot)              |88.62|
|MMLU (5-Shot)                    |66.22|
|TruthfulQA (0-shot)              |70.92|
|Winogrande (5-shot)              |83.43|
|GSM8k (5-shot)                   |64.59|