Instructions to use bartowski/Starling-LM-7B-alpha-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bartowski/Starling-LM-7B-alpha-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="bartowski/Starling-LM-7B-alpha-exl2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bartowski/Starling-LM-7B-alpha-exl2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bartowski/Starling-LM-7B-alpha-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/Starling-LM-7B-alpha-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/Starling-LM-7B-alpha-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bartowski/Starling-LM-7B-alpha-exl2

SGLang

How to use bartowski/Starling-LM-7B-alpha-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/Starling-LM-7B-alpha-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/Starling-LM-7B-alpha-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/Starling-LM-7B-alpha-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/Starling-LM-7B-alpha-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bartowski/Starling-LM-7B-alpha-exl2 with Docker Model Runner:
```
docker model run hf.co/bartowski/Starling-LM-7B-alpha-exl2
```

Starling-LM-7B-alpha-exl2 / README.md

bartowski

Quant for 6.0

c267283 almost 3 years ago

preview code

Raw History Blame

885 Bytes

metadata

quantized_by: bartowski

Exllama v2 Quantizations of Starling-LM-7B-alpha at 6.0 bits per weight

Using turboderp's ExLlamaV2 v0.0.11 for quantization.

Conversion was done using the default calibration dataset.

Original model: https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha

Download instructions

With git:

git clone --single-branch --branch 6.0 https://huggingface.co/bartowski/Starling-LM-7B-alpha-exl2

With huggingface hub (credit to TheBloke for instructions):

pip3 install huggingface-hub

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

mkdir Starling-LM-7B-alpha-exl2
huggingface-cli download bartowski/Starling-LM-7B-alpha-exl2 --revision 6_0 --local-dir Starling-LM-7B-alpha-exl2 --local-dir-use-symlinks False