Instructions to use OPTML-Group/TOFU-origin-Llama-2-7b-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OPTML-Group/TOFU-origin-Llama-2-7b-chat with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="OPTML-Group/TOFU-origin-Llama-2-7b-chat")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OPTML-Group/TOFU-origin-Llama-2-7b-chat")
model = AutoModelForCausalLM.from_pretrained("OPTML-Group/TOFU-origin-Llama-2-7b-chat", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OPTML-Group/TOFU-origin-Llama-2-7b-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OPTML-Group/TOFU-origin-Llama-2-7b-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OPTML-Group/TOFU-origin-Llama-2-7b-chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OPTML-Group/TOFU-origin-Llama-2-7b-chat

SGLang

How to use OPTML-Group/TOFU-origin-Llama-2-7b-chat with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "OPTML-Group/TOFU-origin-Llama-2-7b-chat" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OPTML-Group/TOFU-origin-Llama-2-7b-chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "OPTML-Group/TOFU-origin-Llama-2-7b-chat" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OPTML-Group/TOFU-origin-Llama-2-7b-chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OPTML-Group/TOFU-origin-Llama-2-7b-chat with Docker Model Runner:
```
docker model run hf.co/OPTML-Group/TOFU-origin-Llama-2-7b-chat
```

a-F1 commited on Oct 28, 2024

Commit

bcf7a74

verified ·

1 Parent(s): 5a1cc18

Update README.md

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README.md +38 -3

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@@ -1,3 +1,38 @@
----
-license: mit
----

+---
+license: mit
+---
+# LLaMA-2-chat 7B fine tunes on TOFU (Origin Model)
+## Model Details
+- **Base Model**: LLaMA-2-chat 7B
+- **Training**: Fine-tuned on TOFU dataset
+## Loading the Model
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("OPTML-Group/TOFU-origin-Llama-2-7b-chat", use_flash_attention_2=True, torch_dtype=torch.bfloat16, trust_remote_code=True)
+```
+## Citation
+If you use this model in your research, please cite:
+```
+@misc{fan2024simplicityprevailsrethinkingnegative,
+      title={Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning},
+      author={Chongyu Fan and Jiancheng Liu and Licong Lin and Jinghan Jia and Ruiqi Zhang and Song Mei and Sijia Liu},
+      year={2024},
+      eprint={2410.07163},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2410.07163},
+}
+```
+## Contact
+For questions or issues regarding this model, please contact chongyu.fan93@gmail.com.