Instructions to use NeuraCraft/Lance-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NeuraCraft/Lance-AI with Transformers:

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

pipe = pipeline("text-generation", model="NeuraCraft/Lance-AI", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("NeuraCraft/Lance-AI", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NeuraCraft/Lance-AI with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NeuraCraft/Lance-AI"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NeuraCraft/Lance-AI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NeuraCraft/Lance-AI

SGLang

How to use NeuraCraft/Lance-AI 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 "NeuraCraft/Lance-AI" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NeuraCraft/Lance-AI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "NeuraCraft/Lance-AI" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NeuraCraft/Lance-AI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NeuraCraft/Lance-AI with Docker Model Runner:
```
docker model run hf.co/NeuraCraft/Lance-AI
```

NeuraCraft commited on Apr 25, 2025

Commit

679d9f0

1 Parent(s): 5ce9c67

Update lance_ai_model.py

Browse files

Files changed (1) hide show

lance_ai_model.py +1 -1

lance_ai_model.py CHANGED Viewed

@@ -36,7 +36,7 @@ class LanceAI(PreTrainedModel, GenerationMixin):
         self.init_weights()
-    def forward(self, input_ids=None, attention_mask=None, labels=None, inputs_embeds=None, return_dict=True):
         embeddings = self.embedding(input_ids) if inputs_embeds is None else inputs_embeds
         encoder_output = self.encoder(embeddings)
         decoder_output = self.decoder(embeddings, encoder_output)

         self.init_weights()
+    def forward(self, input_ids=None, attention_mask=None, labels=None, inputs_embeds=None, return_dict=True, use_cache=False):
         embeddings = self.embedding(input_ids) if inputs_embeds is None else inputs_embeds
         encoder_output = self.encoder(embeddings)
         decoder_output = self.decoder(embeddings, encoder_output)