Instructions to use LnL-AI/dbrx-base-converted-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LnL-AI/dbrx-base-converted-v2 with Transformers:

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

pipe = pipeline("text-generation", model="LnL-AI/dbrx-base-converted-v2", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LnL-AI/dbrx-base-converted-v2", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("LnL-AI/dbrx-base-converted-v2", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LnL-AI/dbrx-base-converted-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LnL-AI/dbrx-base-converted-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LnL-AI/dbrx-base-converted-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LnL-AI/dbrx-base-converted-v2

SGLang

How to use LnL-AI/dbrx-base-converted-v2 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 "LnL-AI/dbrx-base-converted-v2" \
    --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": "LnL-AI/dbrx-base-converted-v2",
		"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 "LnL-AI/dbrx-base-converted-v2" \
        --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": "LnL-AI/dbrx-base-converted-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LnL-AI/dbrx-base-converted-v2 with Docker Model Runner:
```
docker model run hf.co/LnL-AI/dbrx-base-converted-v2
```

dbrx-base-converted-v2 / model-00048-of-00061.safetensors

Qubitium

Upload model-00048-of-00061.safetensors with huggingface_hub

2a6ce95 verified over 2 years ago

Download

History Blame

4.23 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:: a1ca88bb050710b53a37c049d880dbbf8d14ea416b816005be658529a8aa2449
Size of remote file:: 4.23 GB
SHA256:: b853bf4a8ea724a73be9120383db0dd1592e2a12f3ce5e5b147c9ef77670c846

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