Instructions to use kasohrab/gemma-spatial-bbox-fixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kasohrab/gemma-spatial-bbox-fixed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="kasohrab/gemma-spatial-bbox-fixed")# Load model directly from transformers import AutoModelForImageTextToText model = AutoModelForImageTextToText.from_pretrained("kasohrab/gemma-spatial-bbox-fixed", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use kasohrab/gemma-spatial-bbox-fixed with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kasohrab/gemma-spatial-bbox-fixed" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kasohrab/gemma-spatial-bbox-fixed", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/kasohrab/gemma-spatial-bbox-fixed
- SGLang
How to use kasohrab/gemma-spatial-bbox-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 "kasohrab/gemma-spatial-bbox-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": "kasohrab/gemma-spatial-bbox-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 "kasohrab/gemma-spatial-bbox-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": "kasohrab/gemma-spatial-bbox-fixed", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use kasohrab/gemma-spatial-bbox-fixed with Docker Model Runner:
docker model run hf.co/kasohrab/gemma-spatial-bbox-fixed
Training in progress, step 99000
Browse files
logs/events.out.tfevents.1762816700.atl1-1-03-012-18-0.pace.gatech.edu.1101255.0
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a3f61e67a92152759ab9115dd12f2fd04c59b5c65b00c0f0899e031e9e593955
|
| 3 |
+
size 110299
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 8600280616
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:51e150da0496973713ef230c811e1e2b7203ff7f3ff09aafdf83aa797791fe60
|
| 3 |
size 8600280616
|