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 / README.md

jgalego

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	---
	base_model: Qwen/Qwen3.5-0.8B
	datasets:
	- DEEL-AI/NOTAM
	- AirsideLabs/notam-typed-decisions
	language: en
	library_name: transformers
	license: apache-2.0
	pipeline_tag: text-generation
	tags:
	- aviation
	- notam
	- text-classification
	model-index:
	- name: notam-subject-qwen3.5-0.8b
	results:
	- task:
	type: text-classification
	dataset:
	name: NOTAM subject, template-disjoint
	type: AirsideLabs/notam-typed-decisions
	metrics:
	- type: accuracy
	value: 0.948
	name: Accuracy
	- type: macro_f1
	value: 0.942
	name: Macro-F1
	---

	# notam-subject-qwen3.5-0.8b

	[Qwen/Qwen3.5-0.8B](https://huggingface.co/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](https://huggingface.co/datasets/DEEL-AI/NOTAM).

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

	> [!NOTE]
	> 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

	```python
	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](https://huggingface.co/Qwen/Qwen3.5-0.8B) \|
	\| Data \| [DEEL-AI/NOTAM](https://huggingface.co/datasets/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](https://huggingface.co/datasets/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](https://huggingface.co/datasets/AirsideLabs/notam-typed-decisions/blob/main/baselines.md) 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.