Instructions to use ek826/dbrx-instruct-4.25bpw-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ek826/dbrx-instruct-4.25bpw-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="ek826/dbrx-instruct-4.25bpw-exl2", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ek826/dbrx-instruct-4.25bpw-exl2", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("ek826/dbrx-instruct-4.25bpw-exl2", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ek826/dbrx-instruct-4.25bpw-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ek826/dbrx-instruct-4.25bpw-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ek826/dbrx-instruct-4.25bpw-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ek826/dbrx-instruct-4.25bpw-exl2

SGLang

How to use ek826/dbrx-instruct-4.25bpw-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 "ek826/dbrx-instruct-4.25bpw-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": "ek826/dbrx-instruct-4.25bpw-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 "ek826/dbrx-instruct-4.25bpw-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": "ek826/dbrx-instruct-4.25bpw-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ek826/dbrx-instruct-4.25bpw-exl2 with Docker Model Runner:
```
docker model run hf.co/ek826/dbrx-instruct-4.25bpw-exl2
```

dbrx-instruct-4.25bpw-exl2 / output-00004-of-00009.safetensors

Edward Kim

created 4.25bpw exl2 quant

181cf4e over 2 years ago

Download

History Blame Contribute Delete

8.59 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:: 2ccc2b4867be7d097f60fff3b62d41654e717ba8077929d887a1bd78d2633684
Size of remote file:: 8.59 GB
SHA256:: 97180c4d78a30653addb28e013b9effe4d0a450db9d43d37d4df237811685614

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