Instructions to use rishanthrajendhran/VeriFastScore with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rishanthrajendhran/VeriFastScore with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rishanthrajendhran/VeriFastScore")
model = AutoModelForCausalLM.from_pretrained("rishanthrajendhran/VeriFastScore")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rishanthrajendhran/VeriFastScore with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/rishanthrajendhran/VeriFastScore

SGLang

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

Docker Model Runner
How to use rishanthrajendhran/VeriFastScore with Docker Model Runner:
```
docker model run hf.co/rishanthrajendhran/VeriFastScore
```

rishanthrajendhran commited on May 28, 2025

Commit

193296f

verified ·

1 Parent(s): a0c598a

Added link to dataset on HF

Browse files

Files changed (1) hide show

README.md +3 -1

README.md CHANGED Viewed

@@ -5,6 +5,8 @@ language:
 - en
 base_model:
 - meta-llama/Llama-3.1-8B-Instruct
 ---
 # Model Card for Model ID
@@ -124,7 +126,7 @@ print(tokenizer.decode(outputs, skip_special_tokens=True))
 ### Training Data
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-Synthetic (response, evidence, claim, label) examples generated via VeriScore applied to long-form prompts from datasets like Tulu3-Personas. See <a href="https://arxiv.org/abs/2505.16973" style="color:black;">paper</a> for more details.
 ### Training Procedure

 - en
 base_model:
 - meta-llama/Llama-3.1-8B-Instruct
+datasets:
+- rishanthrajendhran/VeriFastScore
 ---
 # Model Card for Model ID
 ### Training Data
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+Synthetic (response, evidence, claim, label) examples generated via VeriScore applied to long-form prompts from datasets like Tulu3-Personas. The dataset is available on <a href="https://huggingface.co/datasets/rishanthrajendhran/VeriFastScore">HuggingFace</a>. See <a href="https://arxiv.org/abs/2505.16973" style="color:black;">paper</a> for more details.
 ### Training Procedure