Instructions to use aisingapore/coherence-momentum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aisingapore/coherence-momentum with Transformers:

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

pipe = pipeline("text-generation", model="aisingapore/coherence-momentum")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("aisingapore/coherence-momentum", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aisingapore/coherence-momentum with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/aisingapore/coherence-momentum

SGLang

How to use aisingapore/coherence-momentum 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 "aisingapore/coherence-momentum" \
    --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": "aisingapore/coherence-momentum",
		"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 "aisingapore/coherence-momentum" \
        --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": "aisingapore/coherence-momentum",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use aisingapore/coherence-momentum with Docker Model Runner:
```
docker model run hf.co/aisingapore/coherence-momentum
```

tainc commited on Mar 2, 2023

Commit

91a37d1

1 Parent(s): 51ddf7b

Replaced links from sgnlp to sgnlp-models

Browse files

Files changed (1) hide show

README.md +4 -4

README.md CHANGED Viewed

@@ -71,10 +71,10 @@ from sgnlp.models.coherence_momentum import CoherenceMomentumModel, CoherenceMom
 # Load Model
 config = CoherenceMomentumConfig.from_pretrained(
-    "https://storage.googleapis.com/sgnlp/models/coherence_momentum/config.json"
 )
 model = CoherenceMomentumModel.from_pretrained(
-    "https://storage.googleapis.com/sgnlp/models/coherence_momentum/pytorch_model.bin",
     config=config
 )
@@ -113,10 +113,10 @@ Please contact the authors to get the dataset if you have a valid LDC license.
 #### Training Results
 - **Training Time:** ~24 hours for ~46000 steps (batch size of 1) on a single A100 GPU
 - **Datasets:** Permuted dataset derived from Linguistic Data Consortium's (LDC) Wall Street Journal (WSJ) dataset.
-- **Training Config:** [link](https://storage.googleapis.com/sgnlp/models/coherence_momentum/config.json)
 # Model Parameters
-- **Model Weights:** [link](https://storage.googleapis.com/sgnlp/models/coherence_momentum/pytorch_model.bin)
 - **Model Inputs:** A paragraph of text. During training, each positive example can be paired with one or more negative examples.
 - **Model Outputs:** Coherence score for the input text.
 - **Model Size:** ~930MB

 # Load Model
 config = CoherenceMomentumConfig.from_pretrained(
+    "https://storage.googleapis.com/sgnlp-models/models/coherence_momentum/config.json"
 )
 model = CoherenceMomentumModel.from_pretrained(
+    "https://storage.googleapis.com/sgnlp-models/models/coherence_momentum/pytorch_model.bin",
     config=config
 )
 #### Training Results
 - **Training Time:** ~24 hours for ~46000 steps (batch size of 1) on a single A100 GPU
 - **Datasets:** Permuted dataset derived from Linguistic Data Consortium's (LDC) Wall Street Journal (WSJ) dataset.
+- **Training Config:** [link](https://storage.googleapis.com/sgnlp-models/models/coherence_momentum/config.json)
 # Model Parameters
+- **Model Weights:** [link](https://storage.googleapis.com/sgnlp-models/models/coherence_momentum/pytorch_model.bin)
 - **Model Inputs:** A paragraph of text. During training, each positive example can be paired with one or more negative examples.
 - **Model Outputs:** Coherence score for the input text.
 - **Model Size:** ~930MB