Instructions to use LearnItAnyway/YOLO_LLaMa_7B_VisNav with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LearnItAnyway/YOLO_LLaMa_7B_VisNav with Transformers:

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

pipe = pipeline("text-generation", model="LearnItAnyway/YOLO_LLaMa_7B_VisNav")

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("LearnItAnyway/YOLO_LLaMa_7B_VisNav")
model = AutoModelForCausalLM.from_pretrained("LearnItAnyway/YOLO_LLaMa_7B_VisNav", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LearnItAnyway/YOLO_LLaMa_7B_VisNav with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LearnItAnyway/YOLO_LLaMa_7B_VisNav"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LearnItAnyway/YOLO_LLaMa_7B_VisNav",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LearnItAnyway/YOLO_LLaMa_7B_VisNav

SGLang

How to use LearnItAnyway/YOLO_LLaMa_7B_VisNav 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 "LearnItAnyway/YOLO_LLaMa_7B_VisNav" \
    --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": "LearnItAnyway/YOLO_LLaMa_7B_VisNav",
		"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 "LearnItAnyway/YOLO_LLaMa_7B_VisNav" \
        --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": "LearnItAnyway/YOLO_LLaMa_7B_VisNav",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LearnItAnyway/YOLO_LLaMa_7B_VisNav with Docker Model Runner:
```
docker model run hf.co/LearnItAnyway/YOLO_LLaMa_7B_VisNav
```

LearnItAnyway commited on Jul 26, 2023

Commit

be97739

1 Parent(s): a979a2e

Update README.md

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@@ -1,3 +1,16 @@
 ---
 license: other
 ---

 ---
 license: other
 ---
+# Overview
+This project aims to support visually impaired individuals in their daily navigation.
+This project combines the [YOLO](https://ultralytics.com/yolov8) model and [LLaMa 2 7b](https://huggingface.co/meta-llama/Llama-2-7b) for the navigation.
+YOLO is trained on the bounding box data from the [AI Hub](https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=189),
+Output of YOLO (bbox data) is converted as lists like `[[class_of_obj_1, xmin, xmax, ymin, ymax, size], [class_of...] ...]` then added to the input of question.
+The LLM is trained to navigate using [LearnItAnyway/Visual-Navigation-21k](https://huggingface.co/datasets/LearnItAnyway/Visual-Navigation-21k) multi-turn dataset
+## Usage
+We show how to use the model in [yolo_llama_visnav_test.ipynb](https://huggingface.co/LearnItAnyway/YOLO_LLaMa_7B_VisNav/blob/main/yolo_llama_visnav_test.ipynb)