Image-Text-to-Text
Transformers
TensorBoard
Safetensors
vision-encoder-decoder
Generated from Trainer
Instructions to use PeanutCoding/Donuttest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PeanutCoding/Donuttest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="PeanutCoding/Donuttest")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("PeanutCoding/Donuttest") model = AutoModelForMultimodalLM.from_pretrained("PeanutCoding/Donuttest") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use PeanutCoding/Donuttest with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PeanutCoding/Donuttest" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PeanutCoding/Donuttest", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/PeanutCoding/Donuttest
- SGLang
How to use PeanutCoding/Donuttest 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 "PeanutCoding/Donuttest" \ --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": "PeanutCoding/Donuttest", "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 "PeanutCoding/Donuttest" \ --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": "PeanutCoding/Donuttest", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use PeanutCoding/Donuttest with Docker Model Runner:
docker model run hf.co/PeanutCoding/Donuttest
Training in progress, step 820
Browse files
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 809070744
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:fed428c4d3ca8463b1d8d4218b4f8cdae29d32ef96d230d38ac158ff27de4766
|
| 3 |
size 809070744
|
runs/Jun06_21-20-29_phi-ThinkPad-T490/events.out.tfevents.1749237629.phi-ThinkPad-T490
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:a35b633383dc5e833ad82702279a44eb0b624108ff4bbe16e4dc5adba9283495
|
| 3 |
+
size 8211
|