Instructions to use ihorbilyk/donut-base-checks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ihorbilyk/donut-base-checks with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ihorbilyk/donut-base-checks")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("ihorbilyk/donut-base-checks") model = AutoModelForImageTextToText.from_pretrained("ihorbilyk/donut-base-checks") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ihorbilyk/donut-base-checks with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ihorbilyk/donut-base-checks" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ihorbilyk/donut-base-checks", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ihorbilyk/donut-base-checks
- SGLang
How to use ihorbilyk/donut-base-checks 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 "ihorbilyk/donut-base-checks" \ --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": "ihorbilyk/donut-base-checks", "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 "ihorbilyk/donut-base-checks" \ --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": "ihorbilyk/donut-base-checks", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ihorbilyk/donut-base-checks with Docker Model Runner:
docker model run hf.co/ihorbilyk/donut-base-checks
Training in progress, epoch 5
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 809203481
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0caf3f243d5f8fea9c860b4eef6e48125fb97496be575bcf145309e3a890f4a2
|
| 3 |
size 809203481
|
runs/Feb26_16-17-17_a840de3e14cb/events.out.tfevents.1677428457.a840de3e14cb.6619.0
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 sha256:168ebf877382030553ae584aeaf63d3ce08ae2791c7bbba7da306943954e27c8
|
| 3 |
+
size 11309
|