Instructions to use selfrag/selfrag_llama2_7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use selfrag/selfrag_llama2_7b with Transformers:

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

pipe = pipeline("text-generation", model="selfrag/selfrag_llama2_7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("selfrag/selfrag_llama2_7b")
model = AutoModelForCausalLM.from_pretrained("selfrag/selfrag_llama2_7b")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use selfrag/selfrag_llama2_7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "selfrag/selfrag_llama2_7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "selfrag/selfrag_llama2_7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/selfrag/selfrag_llama2_7b

SGLang

How to use selfrag/selfrag_llama2_7b 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 "selfrag/selfrag_llama2_7b" \
    --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": "selfrag/selfrag_llama2_7b",
		"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 "selfrag/selfrag_llama2_7b" \
        --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": "selfrag/selfrag_llama2_7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use selfrag/selfrag_llama2_7b with Docker Model Runner:
```
docker model run hf.co/selfrag/selfrag_llama2_7b
```

Sefika commited on Oct 22, 2023

Commit

369adc9

1 Parent(s): f05576d

Fix the link of requirements.txt

Browse files

The link of the requirements.txt was not accessible, so changed it with https://github.com/AkariAsai/self-rag/blob/main/requirementd.txt. It works with this link.

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -8,7 +8,7 @@ At inference, we leverage reflection tokens covering diverse aspects of generati
 See full descriptions in See full descriptions in [our paper](hhttps://arxiv.org/abs/2310.11511).
 ## Usage
-Here, we show an easy way to quickly download our model from HuggingFace and run with `vllm` with pre-given passages. Make sure to install dependencies listed at [self-rag/requirements.txt](https://github.com/AkariAsai/self-rag/requirements.txt).
 To run our full inference pipeline with a retrieval system and fine-grained tree decoding, please use [our code](https://github.com/AkariAsai/self-rag).
 ```py

 See full descriptions in See full descriptions in [our paper](hhttps://arxiv.org/abs/2310.11511).
 ## Usage
+Here, we show an easy way to quickly download our model from HuggingFace and run with `vllm` with pre-given passages. Make sure to install dependencies listed at [self-rag/requirements.txt](https://github.com/AkariAsai/self-rag/blob/main/requirementd.txt).
 To run our full inference pipeline with a retrieval system and fine-grained tree decoding, please use [our code](https://github.com/AkariAsai/self-rag).
 ```py