Instructions to use jgalego/notam-subject-qwen3.5-0.8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jgalego/notam-subject-qwen3.5-0.8b with Transformers:

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

pipe = pipeline("text-generation", model="jgalego/notam-subject-qwen3.5-0.8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jgalego/notam-subject-qwen3.5-0.8b")
model = AutoModelForCausalLM.from_pretrained("jgalego/notam-subject-qwen3.5-0.8b", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jgalego/notam-subject-qwen3.5-0.8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jgalego/notam-subject-qwen3.5-0.8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jgalego/notam-subject-qwen3.5-0.8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jgalego/notam-subject-qwen3.5-0.8b

SGLang

How to use jgalego/notam-subject-qwen3.5-0.8b 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 "jgalego/notam-subject-qwen3.5-0.8b" \
    --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": "jgalego/notam-subject-qwen3.5-0.8b",
		"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 "jgalego/notam-subject-qwen3.5-0.8b" \
        --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": "jgalego/notam-subject-qwen3.5-0.8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jgalego/notam-subject-qwen3.5-0.8b with Docker Model Runner:
```
docker model run hf.co/jgalego/notam-subject-qwen3.5-0.8b
```

notam-subject-qwen3.5-0.8b

Qwen/Qwen3.5-0.8B fine-tuned with LoRA to name the subject of a NOTAM: one of the 13 classes in DEEL-AI/NOTAM.

Research aid only. Not for flight operations. Not certified or validated for flight planning, dispatch or pilot briefing. Always read the original NOTAM.

What is a NOTAM? A NOTAM is a short, standardized notice that alerts flight crews and dispatchers to a condition that may affect a flight, such as a closed runway, an unavailable navigation aid or a temporary airspace restriction. The compressed wording can be cryptic: RWY 09/27 CLSD DUE TO WIP means runway 09/27 is closed because work is in progress.

🚀 Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "jgalego/notam-subject-qwen3.5-0.8b"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype="auto")

messages = [
    {"role": "system", "content": "Name the main subject of the NOTAM. Answer with one of: Airspaces, Communication and Radar, GPS, Landing Navaids, Obstacles, Parking, Procedure, Runway, Services and Facilities, Signs and Lights, Taxiway, Terminal or Enroute Navaids, Wildlife."},
    {"role": "user", "content": "RWY 09/27 CLSD DUE TO WIP"},
]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    enable_thinking=False,
    return_tensors="pt",
    return_dict=True,
)
out = model.generate(**inputs, max_new_tokens=12, do_sample=False)
print(tokenizer.decode(out[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True))

Labels: Airspaces, Communication and Radar, GPS, Landing Navaids, Obstacles, Parking, Procedure, Runway, Services and Facilities, Signs and Lights, Taxiway, Terminal or Enroute Navaids, Wildlife.

🏋️ Training


Base	Qwen/Qwen3.5-0.8B
Data	DEEL-AI/NOTAM, train split
Method	LoRA, r=16, alpha=32, all linear layers, merged
Steps	522 (2.0 epochs), batch size 16
Learning rate	0.0002, cosine
Mean train loss	0.0672
Hardware	NVIDIA A10G, 16 min

📊 Results

Template-disjoint group of AirsideLabs/notam-typed-decisions: NOTAMs whose templates do not appear in training. Greedy decoding; answers outside the label set count as wrong.

Model	n	Accuracy	Macro-F1	Invalid
jgalego/notam-subject-qwen3.5-0.8b	3252	0.948	0.942	1
Qwen/Qwen3.5-0.8B	3252	0.195	0.117	13

Reference baselines reported by Airside Labs on the same group (n=3,252):

Model	Accuracy	Macro-F1
TF-IDF + logistic regression	0.936	0.930
Qwen3.5-4B, self-distilled	0.904	0.894
Qwen3.5-4B, zero-shot	0.787	0.750

⚠️ Limitations

Trained on 13 fixed classes; NOTAMs about anything else get the nearest one.
English ICAO-style NOTAM text only.

Downloads last month: 228

Safetensors

Model size

0.8B params

Tensor type

BF16

Model tree for jgalego/notam-subject-qwen3.5-0.8b

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Finetuned

(511)

this model

Datasets used to train jgalego/notam-subject-qwen3.5-0.8b

Space using jgalego/notam-subject-qwen3.5-0.8b 1

Collection including jgalego/notam-subject-qwen3.5-0.8b

Mayday

Collection

Small models for aviation and safety engineering. Research aids, not certified. • 7 items • Updated 10 days ago

Evaluation results

Accuracy on NOTAM subject, template-disjoint
self-reported

0.948
Macro-F1 on NOTAM subject, template-disjoint
self-reported

0.942