🚀 koOpenChat-sft🐧
koOpenChat-sft is a project that offers specific model capabilities. It provides a way to interact with the model through a given implementation code and has evaluation results on the Open LLM Leaderboard.
🚀 Quick Start
The following code example shows how to use the koOpenChat-sft model:
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
model = AutoModelForCausalLM.from_pretrained("maywell/koOpenChat-sft")
tokenizer = AutoTokenizer.from_pretrained("maywell/koOpenChat-sft")
messages = [
{"role": "user", "content": "바나나는 원래 하얀색이야?"},
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
✨ Features
- Instruction Formats: It follows ChatML format and Alpaca(No-Input) format.
- Open LLM Leaderboard Results: It has evaluation results on the Open LLM Leaderboard, with detailed results available here.
📦 Installation
No specific installation steps are provided in the original document.
💻 Usage Examples
Basic Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
model = AutoModelForCausalLM.from_pretrained("maywell/koOpenChat-sft")
tokenizer = AutoTokenizer.from_pretrained("maywell/koOpenChat-sft")
messages = [
{"role": "user", "content": "바나나는 원래 하얀색이야?"},
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
📚 Documentation
Model Details
| Property |
Details |
| Base Model |
OpenChat3.5 |
| Trained On |
A100 80GB * 1 |
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric |
Value |
| Avg. |
51.36 |
| ARC (25-shot) |
59.81 |
| HellaSwag (10-shot) |
78.73 |
| MMLU (5-shot) |
61.32 |
| TruthfulQA (0-shot) |
51.24 |
| Winogrande (5-shot) |
76.4 |
| GSM8K (5-shot) |
24.18 |
| DROP (3-shot) |
7.82 |
🔧 Technical Details
No technical implementation details are provided in the original document.
📄 License
This project is licensed under the CC BY-SA 4.0 license.
Support Me
This is a personal project developed by one person. If you like the model, would you consider supporting a little research fee?
Wanna be a sponsor? (Please) Contact me on Telegram AlzarTakkarsen
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