Instructions to use principled-intelligence/claim-extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use principled-intelligence/claim-extraction with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("principled-intelligence/claim-extraction")
model = AutoModelForCausalLM.from_pretrained("principled-intelligence/claim-extraction")
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

vLLM

How to use principled-intelligence/claim-extraction with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/principled-intelligence/claim-extraction

SGLang

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

Docker Model Runner
How to use principled-intelligence/claim-extraction with Docker Model Runner:
```
docker model run hf.co/principled-intelligence/claim-extraction
```

claim-extraction / tokenizer_config.json

edobobo

Upload ClaimExtractionPipeline

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	{
	"add_prefix_space": false,
	"audio_bos_token": "<\|audio_start\|>",
	"audio_eos_token": "<\|audio_end\|>",
	"audio_token": "<\|audio_pad\|>",
	"backend": "tokenizers",
	"bos_token": null,
	"clean_up_tokenization_spaces": false,
	"eos_token": "<\|im_end\|>",
	"errors": "replace",
	"image_token": "<\|image_pad\|>",
	"is_local": false,
	"local_files_only": false,
	"model_max_length": 262144,
	"model_specific_special_tokens": {
	"audio_bos_token": "<\|audio_start\|>",
	"audio_eos_token": "<\|audio_end\|>",
	"audio_token": "<\|audio_pad\|>",
	"image_token": "<\|image_pad\|>",
	"video_token": "<\|video_pad\|>",
	"vision_bos_token": "<\|vision_start\|>",
	"vision_eos_token": "<\|vision_end\|>"
	},
	"pad_token": "<\|vision_pad\|>",
	"padding_side": "left",
	"pretokenize_regex": "(?i:'s\|'t\|'re\|'ve\|'m\|'ll\|'d)\|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+\|\\p{N}\| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]\|\\s[\\r\\n]+\|\\s+(?!\\S)\|\\s+",
	"processor_class": "Qwen3VLProcessor",
	"split_special_tokens": false,
	"tokenizer_class": "TokenizersBackend",
	"unk_token": null,
	"video_token": "<\|video_pad\|>",
	"vision_bos_token": "<\|vision_start\|>",
	"vision_eos_token": "<\|vision_end\|>"
	}

	{
	"add_prefix_space": false,
	"audio_bos_token": "<\|audio_start\|>",
	"audio_eos_token": "<\|audio_end\|>",
	"audio_token": "<\|audio_pad\|>",
	"backend": "tokenizers",
	"bos_token": null,
	"clean_up_tokenization_spaces": false,
	"eos_token": "<\|im_end\|>",
	"errors": "replace",
	"image_token": "<\|image_pad\|>",
	"is_local": false,
	"local_files_only": false,
	"model_max_length": 262144,
	"model_specific_special_tokens": {
	"audio_bos_token": "<\|audio_start\|>",
	"audio_eos_token": "<\|audio_end\|>",
	"audio_token": "<\|audio_pad\|>",
	"image_token": "<\|image_pad\|>",
	"video_token": "<\|video_pad\|>",
	"vision_bos_token": "<\|vision_start\|>",
	"vision_eos_token": "<\|vision_end\|>"
	},
	"pad_token": "<\|vision_pad\|>",
	"padding_side": "left",
	"pretokenize_regex": "(?i:'s\|'t\|'re\|'ve\|'m\|'ll\|'d)\|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+\|\\p{N}\| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]\|\\s[\\r\\n]+\|\\s+(?!\\S)\|\\s+",
	"processor_class": "Qwen3VLProcessor",
	"split_special_tokens": false,
	"tokenizer_class": "TokenizersBackend",
	"unk_token": null,
	"video_token": "<\|video_pad\|>",
	"vision_bos_token": "<\|vision_start\|>",
	"vision_eos_token": "<\|vision_end\|>"
	}