Instructions to use SinclairSchneider/dbrx-instruct-quantization-fixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SinclairSchneider/dbrx-instruct-quantization-fixed with Transformers:

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

pipe = pipeline("text-generation", model="SinclairSchneider/dbrx-instruct-quantization-fixed", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SinclairSchneider/dbrx-instruct-quantization-fixed", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("SinclairSchneider/dbrx-instruct-quantization-fixed", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SinclairSchneider/dbrx-instruct-quantization-fixed with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SinclairSchneider/dbrx-instruct-quantization-fixed

SGLang

How to use SinclairSchneider/dbrx-instruct-quantization-fixed 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 "SinclairSchneider/dbrx-instruct-quantization-fixed" \
    --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": "SinclairSchneider/dbrx-instruct-quantization-fixed",
		"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 "SinclairSchneider/dbrx-instruct-quantization-fixed" \
        --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": "SinclairSchneider/dbrx-instruct-quantization-fixed",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SinclairSchneider/dbrx-instruct-quantization-fixed with Docker Model Runner:
```
docker model run hf.co/SinclairSchneider/dbrx-instruct-quantization-fixed
```

Some error when running

by Williams07 - opened Mar 29, 2024

Discussion

Williams07

Mar 29, 2024

hi, thanks for your work! When I use this quantization model, I got a strange error:
TypeError: LayerNorm.init() got an unexpected keyword argument 'bias'
Is this my version problem or something else?

SinclairSchneider

Owner Mar 29, 2024

hi, thanks for your work! When I use this quantization model, I got a strange error:
TypeError: LayerNorm.init() got an unexpected keyword argument 'bias'
Is this my version problem or something else?

Would you mind sharing the full error trace. I guess this is due to an old package version.
If we see the trace we find out which package causes the error and I can have a look at the version installed on my machine.

dagelf

Mar 31, 2024

I think you have to update your torch. Because that error implied that nn.LayerNorm has no bias parameter in its init method. But, from torch documentation here, clearly bias exists there.

From: https://huggingface.co/databricks/dbrx-instruct/discussions/10

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