Instructions to use small-models-for-glam/Qwen3.5-0.8B-SFT-name-parser-yaml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use small-models-for-glam/Qwen3.5-0.8B-SFT-name-parser-yaml with Transformers:

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

pipe = pipeline("text-generation", model="small-models-for-glam/Qwen3.5-0.8B-SFT-name-parser-yaml")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("small-models-for-glam/Qwen3.5-0.8B-SFT-name-parser-yaml")
model = AutoModelForCausalLM.from_pretrained("small-models-for-glam/Qwen3.5-0.8B-SFT-name-parser-yaml", 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 small-models-for-glam/Qwen3.5-0.8B-SFT-name-parser-yaml with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "small-models-for-glam/Qwen3.5-0.8B-SFT-name-parser-yaml"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "small-models-for-glam/Qwen3.5-0.8B-SFT-name-parser-yaml",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/small-models-for-glam/Qwen3.5-0.8B-SFT-name-parser-yaml

SGLang

How to use small-models-for-glam/Qwen3.5-0.8B-SFT-name-parser-yaml 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 "small-models-for-glam/Qwen3.5-0.8B-SFT-name-parser-yaml" \
    --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": "small-models-for-glam/Qwen3.5-0.8B-SFT-name-parser-yaml",
		"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 "small-models-for-glam/Qwen3.5-0.8B-SFT-name-parser-yaml" \
        --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": "small-models-for-glam/Qwen3.5-0.8B-SFT-name-parser-yaml",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use small-models-for-glam/Qwen3.5-0.8B-SFT-name-parser-yaml with Docker Model Runner:
```
docker model run hf.co/small-models-for-glam/Qwen3.5-0.8B-SFT-name-parser-yaml
```

Model Card for Qwen3.5-0.8B-SFT-name-parser-yaml

This model is a fine-tuned version of Qwen 3.5 (0.8B parameters) designed to parse unstructured, highly varied historical person names into a strict, structured YAML format. It was developed to process millions of records for cultural heritage platforms efficiently and cost-effectively.

Model Details

Model Description

This model addresses a specific entity extraction problem in the Galleries, Libraries, Archives, and Museums (GLAM) sector. Large frontier models proved too expensive and struggled with strict JSON syntax and component isolation when parsing messy historical names (achieving only ~70% accuracy).

By fine-tuning a small open-source model (0.8B parameters) on high-quality synthetic data and changing the target output format from JSON to YAML, this model achieves 94% to 96% accuracy while remaining small enough to run highly efficiently on local hardware. It extracts components such as first names, last names, middle names, life dates, flourished dates, titles, and extra information.

Developed by: Yale University LUX Platform Team / small-models-for-glam
Model type: Causal Language Model (Fine-tuned for specific entity extraction)
Language(s) (NLP): English (en), with multi-lingual naming conventions included (e.g., French, German, Italian, Arabic, Chinese).
License: Apache 2.0 (Inherited from Qwen base model)
Finetuned from model: Qwen 3.5 0.8B

Model Sources

Repository: https://huggingface.co/small-models-for-glam/Qwen3.5-0.8B-SFT-name-parser-yaml
Associated Dataset: synthetic-parsed-names-yaml

Uses

Direct Use

The model is intended to be used directly for extracting structured data from raw text strings of historical names.

Given an input string like "Cynthia Ponce'Hayes (born 1633), born 1633", the model will output syntactically valid YAML identifying the parsed components:

first_name: Cynthia
last_name: Ponce'Hayes
middle_names: []
temporal:
- start: 1633
  end: null
  type: life_span
titles: []
extra_info: []

Downloads last month: 14

Safetensors

Model size

1B params

Tensor type

BF16

small-models-for-glam
/

Qwen3.5-0.8B-SFT-name-parser-yaml