Instructions to use jondurbin/airoboros-180b-2.2.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jondurbin/airoboros-180b-2.2.1 with Transformers:

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

pipe = pipeline("text-generation", model="jondurbin/airoboros-180b-2.2.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jondurbin/airoboros-180b-2.2.1")
model = AutoModelForCausalLM.from_pretrained("jondurbin/airoboros-180b-2.2.1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jondurbin/airoboros-180b-2.2.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jondurbin/airoboros-180b-2.2.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jondurbin/airoboros-180b-2.2.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jondurbin/airoboros-180b-2.2.1

SGLang

How to use jondurbin/airoboros-180b-2.2.1 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 "jondurbin/airoboros-180b-2.2.1" \
    --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": "jondurbin/airoboros-180b-2.2.1",
		"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 "jondurbin/airoboros-180b-2.2.1" \
        --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": "jondurbin/airoboros-180b-2.2.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jondurbin/airoboros-180b-2.2.1 with Docker Model Runner:
```
docker model run hf.co/jondurbin/airoboros-180b-2.2.1
```

Question about being able to load the model

by vmajor - opened Nov 3, 2023

Discussion

vmajor

Nov 3, 2023

Since the GGUF/llama.cpp option is broken (for now?) I want to see if transformers has 'advanced' enough to allow loading models in 8bit when using CPUs for inference.

I have a woeful amount of VRAM, but with 256 GB of RAM, a quantized version of the model should fit... but since transformers have not been relevant to me for a while due to their VRAM focus for development, I am out of touch regarding transformers ability to use the CPU with quantization methods, so I am interested in seeing if you or someone else that haunts your page could chime in and offer some advice.

Ransom

Nov 5, 2023

Someone has posted a quant that works with llama.cpp here: https://huggingface.co/imi2/airoboros-180b-2.2.1-gguf
Just make sure you're running the latest version of llama.cpp and follow the instructions for merging the files.
Here's the command I use to run it:
./server --model models/airoboros-180b-2.2.1-Q5_K_M.gguf --n-gpu-layers 128 --ctx-size 4090 --port 5005 --host 0.0.0.0 --parallel 1 --cont-batching --threads 24

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