File size: 6,129 Bytes
b0dc553 cb90fa6 02a4971 65efbe3 02a4971 d22aa6e 5e91a78 c73eb76 5e91a78 c73eb76 5e91a78 c73eb76 5e91a78 c73eb76 f71d0bd c73eb76 5e91a78 02a4971 cb90fa6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 |
---
license: mit
datasets:
- Intel/orca_dpo_pairs
model-index:
- name: SOLAR-10B-OrcaDPO-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: 71.16
name: normalized accuracy
source:
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SOLAR-10B-OrcaDPO-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: 88.27
name: normalized accuracy
source:
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SOLAR-10B-OrcaDPO-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: 66.12
name: accuracy
source:
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SOLAR-10B-OrcaDPO-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: 71.57
source:
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SOLAR-10B-OrcaDPO-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: 83.66
name: accuracy
source:
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SOLAR-10B-OrcaDPO-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: 64.82
name: accuracy
source:
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=bhavinjawade/SOLAR-10B-OrcaDPO-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__SOLAR-10B-OrcaDPO-Jawade)
| Metric |Value|
|---------------------------------|----:|
|Avg. |74.27|
|AI2 Reasoning Challenge (25-Shot)|71.16|
|HellaSwag (10-Shot) |88.27|
|MMLU (5-Shot) |66.12|
|TruthfulQA (0-shot) |71.57|
|Winogrande (5-shot) |83.66|
|GSM8k (5-shot) |64.82|
|