Instructions to use properexit/ArgParser-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use properexit/ArgParser-v1 with Transformers:

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

pipe = pipeline("text-generation", model="properexit/ArgParser-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("properexit/ArgParser-v1")
model = AutoModelForCausalLM.from_pretrained("properexit/ArgParser-v1")
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 properexit/ArgParser-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/properexit/ArgParser-v1

SGLang

How to use properexit/ArgParser-v1 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 "properexit/ArgParser-v1" \
    --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": "properexit/ArgParser-v1",
		"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 "properexit/ArgParser-v1" \
        --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": "properexit/ArgParser-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use properexit/ArgParser-v1 with Docker Model Runner:
```
docker model run hf.co/properexit/ArgParser-v1
```

ArgParser-v1 / train.log

properexit

Initial upload: v1 Qwen-0.5B full FT baseline

1848269 verified 4 days ago

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	Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.
	[train-β] config: configs/phase2_beta_qwen1.5b.yaml
	[train-β] unified dir: phase2_data/unified
	[train-β] excluding sources: ('liararg',)
	[unified] loaded 2450 records across 4 source(s):
	abstrct: 700
	cdcp: 731
	microtext: 112
	perspectrum: 907
	[train-β] split distribution: {'test': 688, 'train': 1494, 'val': 268}
	[train-β] building student: hf (Qwen/Qwen2.5-0.5B-Instruct)
	[student:hf] base_model=Qwen/Qwen2.5-0.5B-Instruct (seq2seq=False)
	Loading weights: 0%\| \| 0/290 [00:00<?, ?it/s] Loading weights: 0%\| \| 1/290 [00:01<05:06, 1.06s/it] Loading weights: 84%\|████████▍ \| 243/290 [00:01<00:00, 288.20it/s] Loading weights: 100%\|██████████\| 290/290 [00:01<00:00, 227.75it/s]
	[transformers] warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead.
	[student:hf] encoding 1494 train + 268 val records (causal format)
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	with device_autocast_ctx, torch.cpu.amp.autocast(**cpu_autocast_kwargs), recompute_context: # type: ignore[attr-defined]
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	[A{'eval_loss': 'nan', 'eval_runtime': '76.08', 'eval_samples_per_second': '3.522', 'eval_steps_per_second': '1.761', 'epoch': '1'}

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	/home/users/jchoudhari/argument-aware-rag/.venv/lib/python3.10/site-packages/torch/utils/checkpoint.py:1399: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
	with device_autocast_ctx, torch.cpu.amp.autocast(**cpu_autocast_kwargs), recompute_context: # type: ignore[attr-defined]
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	[A{'eval_loss': 'nan', 'eval_runtime': '75.91', 'eval_samples_per_second': '3.531', 'eval_steps_per_second': '1.765', 'epoch': '2'}

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	/home/users/jchoudhari/argument-aware-rag/.venv/lib/python3.10/site-packages/torch/utils/checkpoint.py:1399: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
	with device_autocast_ctx, torch.cpu.amp.autocast(**cpu_autocast_kwargs), recompute_context: # type: ignore[attr-defined]
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	[A{'eval_loss': 'nan', 'eval_runtime': '76.28', 'eval_samples_per_second': '3.513', 'eval_steps_per_second': '1.757', 'epoch': '3'}

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	{'train_runtime': '5511', 'train_samples_per_second': '0.813', 'train_steps_per_second': '0.026', 'train_loss': '0.615', 'epoch': '3'}
	Writing model shards: 0%\| \| 0/1 [00:00<?, ?it/s] Writing model shards: 100%\|██████████\| 1/1 [00:05<00:00, 5.53s/it] Writing model shards: 100%\|██████████\| 1/1 [00:05<00:00, 5.53s/it]
	[train-β] saved student to phase2_student_beta_qwen0.5b