Instructions to use OpenBuddyEA/openbuddy-falcon-180b-v13-preview1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenBuddyEA/openbuddy-falcon-180b-v13-preview1 with Transformers:

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

pipe = pipeline("text-generation", model="OpenBuddyEA/openbuddy-falcon-180b-v13-preview1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenBuddyEA/openbuddy-falcon-180b-v13-preview1")
model = AutoModelForCausalLM.from_pretrained("OpenBuddyEA/openbuddy-falcon-180b-v13-preview1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenBuddyEA/openbuddy-falcon-180b-v13-preview1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenBuddyEA/openbuddy-falcon-180b-v13-preview1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenBuddyEA/openbuddy-falcon-180b-v13-preview1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenBuddyEA/openbuddy-falcon-180b-v13-preview1

SGLang

How to use OpenBuddyEA/openbuddy-falcon-180b-v13-preview1 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 "OpenBuddyEA/openbuddy-falcon-180b-v13-preview1" \
    --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": "OpenBuddyEA/openbuddy-falcon-180b-v13-preview1",
		"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 "OpenBuddyEA/openbuddy-falcon-180b-v13-preview1" \
        --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": "OpenBuddyEA/openbuddy-falcon-180b-v13-preview1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenBuddyEA/openbuddy-falcon-180b-v13-preview1 with Docker Model Runner:
```
docker model run hf.co/OpenBuddyEA/openbuddy-falcon-180b-v13-preview1
```

openbuddy-falcon-180b-v13-preview1 / pytorch_model-00018-of-00041.bin

ff670

Upload folder using huggingface_hub

82d6f0f almost 3 years ago

Download

History Blame

8.88 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 3a40da4c82114daa5ec6a592242254c51867f9d9408840c401936f4a7d32b322
Size of remote file:: 8.88 GB
SHA256:: c09247b11139c9f1f25ef538b97a4b9fc388b6da91146aecf2bfb9d0dd47f366

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.