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---
license: mit
datasets:
- Intel/orca_dpo_pairs
model-index:
- name: SuperAligned-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: 71.59
name: normalized accuracy
source:
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SuperAligned-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: 90.58
name: normalized accuracy
source:
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SuperAligned-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: 60.81
name: accuracy
source:
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SuperAligned-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: 69.17
source:
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SuperAligned-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: 83.82
name: accuracy
source:
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SuperAligned-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: 49.2
name: accuracy
source:
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SuperAligned-Jawade
name: Open LLM Leaderboard
---
## 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.
```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__SuperAligned-Jawade)
| Metric |Value|
|---------------------------------|----:|
|Avg. |70.86|
|AI2 Reasoning Challenge (25-Shot)|71.59|
|HellaSwag (10-Shot) |90.58|
|MMLU (5-Shot) |60.81|
|TruthfulQA (0-shot) |69.17|
|Winogrande (5-shot) |83.82|
|GSM8k (5-shot) |49.20|
|