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 3, 2024

Commit

83c9f59

verified ·

1 Parent(s): 5135d75

update convert so non-tensor files are copied over to output

Browse files

Files changed (1) hide show

convert_v2.py +10 -1

convert_v2.py CHANGED Viewed

@@ -87,4 +87,13 @@ sorted_map = sorted(weight_map['weight_map'].items())
 weight_map['weight_map'] = dict(sorted_map)
 with open(output_dir / 'model.safetensors.index.json', 'w') as f:
-    json.dump(weight_map, f, indent=4)

 weight_map['weight_map'] = dict(sorted_map)
 with open(output_dir / 'model.safetensors.index.json', 'w') as f:
+    json.dump(weight_map, f, indent=4)
+# copy rest of model non-tensor files
+for filename in os.listdir(model_dir):
+    if filename.endswith(".safetensors") or filename == "model.safetensors.index.json":
+        continue
+    src = os.path.join(model_dir, filename)
+    dst = os.path.join(output_dir, filename)
+    if os.path.isfile(src):
+        shutil.copy2(src, dst)