File size: 1,487 Bytes
7741103 a277f38 7741103 a277f38 3ce9834 a277f38 7741103 a277f38 7741103 a277f38 7741103 04015a3 7741103 a277f38 7741103 a277f38 7741103 a277f38 7741103 a277f38 7741103 a277f38 7741103 a277f38 7741103 a277f38 7741103 a277f38 7741103 a277f38 7741103 a277f38 b6138cc |
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 |
---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- orpo
base_model:
- meta-llama/Meta-Llama-3-8B
datasets:
- mlabonne/orpo-dpo-mix-40k
---
# OrpoLlama3-8B
This is an ORPO fine-tune of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on 15k steps of [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k).
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Muhammad2003/OrpoLlama3-8B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
## 📈 Training curves
Wandb Report
## 🏆 Evaluation
|