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--- |
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license: mit |
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datasets: |
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- Intel/orca_dpo_pairs |
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model-index: |
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- name: SOLAR-10B-OrcaDPO-Jawade |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: AI2 Reasoning Challenge (25-Shot) |
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type: ai2_arc |
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config: ARC-Challenge |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: acc_norm |
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value: 71.16 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SOLAR-10B-OrcaDPO-Jawade |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: HellaSwag (10-Shot) |
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type: hellaswag |
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split: validation |
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args: |
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num_few_shot: 10 |
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metrics: |
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- type: acc_norm |
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value: 88.27 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SOLAR-10B-OrcaDPO-Jawade |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU (5-Shot) |
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type: cais/mmlu |
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config: all |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 66.12 |
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name: accuracy |
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source: |
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url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SOLAR-10B-OrcaDPO-Jawade |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: TruthfulQA (0-shot) |
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type: truthful_qa |
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config: multiple_choice |
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split: validation |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: mc2 |
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value: 71.57 |
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source: |
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url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SOLAR-10B-OrcaDPO-Jawade |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Winogrande (5-shot) |
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type: winogrande |
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config: winogrande_xl |
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split: validation |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 83.66 |
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name: accuracy |
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source: |
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url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SOLAR-10B-OrcaDPO-Jawade |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GSM8k (5-shot) |
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type: gsm8k |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 64.82 |
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name: accuracy |
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source: |
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url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SOLAR-10B-OrcaDPO-Jawade |
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name: Open LLM Leaderboard |
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--- |
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## SOLAR-10B-OrcaDPO-Jawade |
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### Overview |
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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 |
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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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![model_card_image](SOLAR_ORCA.png) |
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## How to Use This Model |
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To use the model `bhavinjawade/SOLAR-10B-OrcaDPO-Jawade`, follow these steps: |
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1. **Import and Load the Model and Tokenizer** |
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Begin by importing the model and tokenizer. Load them using the `from_pretrained` method. |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("bhavinjawade/SOLAR-10B-OrcaDPO-Jawade") |
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tokenizer = AutoTokenizer.from_pretrained("bhavinjawade/SOLAR-10B-OrcaDPO-Jawade") |
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``` |
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2. **Format the Prompt** |
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Format the chat input as a list of messages, each with a role ('system' or 'user') and content. |
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```python |
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message = [ |
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{"role": "system", "content": "You are a helpful assistant chatbot."}, |
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{"role": "user", "content": "Is the universe real? or is it a simulation? whats your opinion?"} |
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] |
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prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False) |
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``` |
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3. **Create a Pipeline** |
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Set up a pipeline for text generation with the loaded model and tokenizer. |
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```python |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model, |
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tokenizer=tokenizer |
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) |
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``` |
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4. **Generate Text** |
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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. |
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```python |
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sequences = pipeline( |
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prompt, |
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do_sample=True, |
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temperature=0.7, |
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top_p=0.9, |
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num_return_sequences=1, |
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max_length=200, |
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) |
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print(sequences[0]['generated_text']) |
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``` |
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This setup allows you to utilize the capabilities of the **bhavinjawade/SOLAR-10B-OrcaDPO-Jawade** model for generating responses to chat inputs. |
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### License |
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- **Type**: MIT License |
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- **Details**: This license permits reuse, modification, and distribution for both private and commercial purposes under the terms of the MIT License. |
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### Model Details |
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- **Model Name**: SOLAR-10.7B-Instruct-v1.0 |
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- **Organization**: Upstage |
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- **Training Dataset**: Intel/orca_dpo_pairs |
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- **Technique Used**: LoRA (Low-Rank Adaptation) |
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### Contact Information |
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- https://bhavinjawade.github.io |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co./datasets/open-llm-leaderboard/details_bhavinjawade__SOLAR-10B-OrcaDPO-Jawade) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |74.27| |
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|AI2 Reasoning Challenge (25-Shot)|71.16| |
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|HellaSwag (10-Shot) |88.27| |
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|MMLU (5-Shot) |66.12| |
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|TruthfulQA (0-shot) |71.57| |
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|Winogrande (5-shot) |83.66| |
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|GSM8k (5-shot) |64.82| |
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