Instructions to use milan400/falcon_CUAD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use milan400/falcon_CUAD with Transformers:

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

pipe = pipeline("text-generation", model="milan400/falcon_CUAD", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("milan400/falcon_CUAD", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("milan400/falcon_CUAD", trust_remote_code=True, device_map="auto")
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 milan400/falcon_CUAD with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "milan400/falcon_CUAD"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "milan400/falcon_CUAD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/milan400/falcon_CUAD

SGLang

How to use milan400/falcon_CUAD 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 "milan400/falcon_CUAD" \
    --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": "milan400/falcon_CUAD",
		"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 "milan400/falcon_CUAD" \
        --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": "milan400/falcon_CUAD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use milan400/falcon_CUAD with Docker Model Runner:
```
docker model run hf.co/milan400/falcon_CUAD
```

falcon_CUAD / tokenizer_config.json

milan400

Upload tokenizer

a6767f4 verified about 1 year ago

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	{
	"add_prefix_space": false,
	"added_tokens_decoder": {
	"0": {
	"content": ">>TITLE<<",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"1": {
	"content": ">>ABSTRACT<<",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"2": {
	"content": ">>INTRODUCTION<<",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"3": {
	"content": ">>SUMMARY<<",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"4": {
	"content": ">>COMMENT<<",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"5": {
	"content": ">>ANSWER<<",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"6": {
	"content": ">>QUESTION<<",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"7": {
	"content": ">>DOMAIN<<",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"8": {
	"content": ">>PREFIX<<",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"9": {
	"content": ">>SUFFIX<<",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"10": {
	"content": ">>MIDDLE<<",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"11": {
	"content": "<\|endoftext\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	}
	},
	"additional_special_tokens": [
	">>TITLE<<",
	">>ABSTRACT<<",
	">>INTRODUCTION<<",
	">>SUMMARY<<",
	">>COMMENT<<",
	">>ANSWER<<",
	">>QUESTION<<",
	">>DOMAIN<<",
	">>PREFIX<<",
	">>SUFFIX<<",
	">>MIDDLE<<"
	],
	"chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = '' %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 %}{{ system_message.strip() }}{% endif %}{% if message['role'] == 'user' %}{{ '\n\nUser: ' + message['content'].strip().replace('\r\n', '\n').replace('\n\n', '\n') }}{% elif message['role'] == 'assistant' %}{{ '\n\nAssistant: ' + message['content'].strip().replace('\r\n', '\n').replace('\n\n', '\n') }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '\n\nAssistant:' }}{% endif %}",
	"clean_up_tokenization_spaces": false,
	"eos_token": "<\|endoftext\|>",
	"extra_special_tokens": {},
	"model_input_names": [
	"input_ids",
	"attention_mask"
	],
	"model_max_length": 2048,
	"pad_token": "<\|endoftext\|>",
	"tokenizer_class": "PreTrainedTokenizer"
	}