Instructions to use senga-ml/dnote-body with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use senga-ml/dnote-body with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="senga-ml/dnote-body")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("senga-ml/dnote-body") model = AutoModelForImageTextToText.from_pretrained("senga-ml/dnote-body") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use senga-ml/dnote-body with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "senga-ml/dnote-body" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "senga-ml/dnote-body", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/senga-ml/dnote-body
- SGLang
How to use senga-ml/dnote-body 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 "senga-ml/dnote-body" \ --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": "senga-ml/dnote-body", "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 "senga-ml/dnote-body" \ --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": "senga-ml/dnote-body", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use senga-ml/dnote-body with Docker Model Runner:
docker model run hf.co/senga-ml/dnote-body
Training in progress, epoch 0
Browse files- config.json +4 -4
- model.safetensors +1 -1
config.json
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
{
|
| 2 |
-
"_name_or_path": "senga-ml/dnote-
|
| 3 |
"architectures": [
|
| 4 |
"VisionEncoderDecoderModel"
|
| 5 |
],
|
|
@@ -49,7 +49,7 @@
|
|
| 49 |
"LABEL_1": 1
|
| 50 |
},
|
| 51 |
"length_penalty": 1.0,
|
| 52 |
-
"max_length":
|
| 53 |
"max_position_embeddings": 1536,
|
| 54 |
"min_length": 0,
|
| 55 |
"model_type": "mbart",
|
|
@@ -124,8 +124,8 @@
|
|
| 124 |
"1": "LABEL_1"
|
| 125 |
},
|
| 126 |
"image_size": [
|
| 127 |
-
|
| 128 |
-
|
| 129 |
],
|
| 130 |
"initializer_range": 0.02,
|
| 131 |
"is_decoder": false,
|
|
|
|
| 1 |
{
|
| 2 |
+
"_name_or_path": "senga-ml/dnote-body",
|
| 3 |
"architectures": [
|
| 4 |
"VisionEncoderDecoderModel"
|
| 5 |
],
|
|
|
|
| 49 |
"LABEL_1": 1
|
| 50 |
},
|
| 51 |
"length_penalty": 1.0,
|
| 52 |
+
"max_length": 1536,
|
| 53 |
"max_position_embeddings": 1536,
|
| 54 |
"min_length": 0,
|
| 55 |
"model_type": "mbart",
|
|
|
|
| 124 |
"1": "LABEL_1"
|
| 125 |
},
|
| 126 |
"image_size": [
|
| 127 |
+
2560,
|
| 128 |
+
1920
|
| 129 |
],
|
| 130 |
"initializer_range": 0.02,
|
| 131 |
"is_decoder": false,
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 809287832
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f4124e06ab24b422e9914e80b86bbdc30c9c37d4c5edddff1429b4a83739dc58
|
| 3 |
size 809287832
|