Instructions to use bartowski/airoboros-2.2.1-y34b-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bartowski/airoboros-2.2.1-y34b-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="bartowski/airoboros-2.2.1-y34b-exl2")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bartowski/airoboros-2.2.1-y34b-exl2", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use bartowski/airoboros-2.2.1-y34b-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/airoboros-2.2.1-y34b-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bartowski/airoboros-2.2.1-y34b-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bartowski/airoboros-2.2.1-y34b-exl2

SGLang

How to use bartowski/airoboros-2.2.1-y34b-exl2 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 "bartowski/airoboros-2.2.1-y34b-exl2" \
    --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": "bartowski/airoboros-2.2.1-y34b-exl2",
		"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 "bartowski/airoboros-2.2.1-y34b-exl2" \
        --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": "bartowski/airoboros-2.2.1-y34b-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bartowski/airoboros-2.2.1-y34b-exl2 with Docker Model Runner:
```
docker model run hf.co/bartowski/airoboros-2.2.1-y34b-exl2
```

Exllama v2 Quantizations of airoboros-2.2.1-y34b

Using turboderp's ExLlamaV2 v0.0.7 for quantization.

Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.

Conversion was done using wikitext-103-raw-v1-test.parquet as calibration dataset.

Original model: https://huggingface.co/Doctor-Shotgun/airoboros-2.2.1-y34b

Download instructions

With git:

git clone --single-branch --branch 4.0 https://huggingface.co/bartowski/airoboros-2.2.1-y34b-exl2

With huggingface hub (credit to TheBloke for instructions):

pip3 install huggingface-hub

To download the main (only useful if you only care about measurement.json) branch to a folder called airoboros-2.2.1-y34b-exl2:

mkdir airoboros-2.2.1-y34b-exl2
huggingface-cli download bartowski/airoboros-2.2.1-y34b-exl2 --local-dir airoboros-2.2.1-y34b-exl2 --local-dir-use-symlinks False

To download from a different branch, add the --revision parameter:

mkdir airoboros-2.2.1-y34b-exl2
huggingface-cli download bartowski/airoboros-2.2.1-y34b-exl2 --revision 4.0 --local-dir airoboros-2.2.1-y34b-exl2 --local-dir-use-symlinks False

Downloads last month: -; Downloads are not tracked for this model. How to track

bartowski
/

airoboros-2.2.1-y34b-exl2

Exllama v2 Quantizations of airoboros-2.2.1-y34b

Download instructions

Dataset used to train bartowski/airoboros-2.2.1-y34b-exl2