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)

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
```

Qubitium commited on Apr 4, 2024

Commit

e7a9661

verified ·

1 Parent(s): 83c9f59

Note to use dbrx-base-tokenizer

Browse files

Files changed (1) hide show

README.md +3 -2

README.md CHANGED Viewed

@@ -16,9 +16,10 @@ Training Notes/Observations:
 # start with this as reference point and move up or down based on eval/train loss
 learning_rate = 1.5e-5
 ```
-2. Due to nature of BPE (tiktoken), tokenizer expansion/resize is not very friendly to training. Use text based special tokens if you need/use extra tokens to avoid bad train/eval losses
-Quants:
 1. 4bit gptq/marlin: https://huggingface.co/LnL-AI/dbrx-base-converted-v2-4bit-gptq-marlin
 2. 4bit gptq/gptq: https://huggingface.co/LnL-AI/dbrx-base-converted-v2-4bit-gptq-gptq

 # start with this as reference point and move up or down based on eval/train loss
 learning_rate = 1.5e-5
 ```
+2. Highly recommend to train this model with `dbrx-base-tokenizer` tokenizer (fully-compatible): https://huggingface.co/LnL-AI/dbrx-base-tokenizer
+# Quants:
 1. 4bit gptq/marlin: https://huggingface.co/LnL-AI/dbrx-base-converted-v2-4bit-gptq-marlin
 2. 4bit gptq/gptq: https://huggingface.co/LnL-AI/dbrx-base-converted-v2-4bit-gptq-gptq