Instructions to use dylanhogg/gnaf-structured-address-v0.2-712c28b-20251019-170453 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use dylanhogg/gnaf-structured-address-v0.2-712c28b-20251019-170453 with PEFT:
```
Task type is invalid.
```

How to use dylanhogg/gnaf-structured-address-v0.2-712c28b-20251019-170453 with Transformers:

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

pipe = pipeline("text-generation", model="dylanhogg/gnaf-structured-address-v0.2-712c28b-20251019-170453")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dylanhogg/gnaf-structured-address-v0.2-712c28b-20251019-170453")
model = AutoModelForCausalLM.from_pretrained("dylanhogg/gnaf-structured-address-v0.2-712c28b-20251019-170453", 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 dylanhogg/gnaf-structured-address-v0.2-712c28b-20251019-170453 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dylanhogg/gnaf-structured-address-v0.2-712c28b-20251019-170453"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dylanhogg/gnaf-structured-address-v0.2-712c28b-20251019-170453",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dylanhogg/gnaf-structured-address-v0.2-712c28b-20251019-170453

SGLang

How to use dylanhogg/gnaf-structured-address-v0.2-712c28b-20251019-170453 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 "dylanhogg/gnaf-structured-address-v0.2-712c28b-20251019-170453" \
    --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": "dylanhogg/gnaf-structured-address-v0.2-712c28b-20251019-170453",
		"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 "dylanhogg/gnaf-structured-address-v0.2-712c28b-20251019-170453" \
        --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": "dylanhogg/gnaf-structured-address-v0.2-712c28b-20251019-170453",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dylanhogg/gnaf-structured-address-v0.2-712c28b-20251019-170453 with Docker Model Runner:
```
docker model run hf.co/dylanhogg/gnaf-structured-address-v0.2-712c28b-20251019-170453
```

finetune_output

This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct on the gnaf-2022-structured-training-1000000-v0-instruct-train dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 2
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 2.0
mixed_precision_training: Native AMP

Training results

Framework versions

PEFT 0.17.1
Transformers 4.57.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1

Full training details

model_version='v0.2' aest_now='20251019-170453' target_model_name='dylanhogg/gnaf-structured-address-v0.2-712c28b-20251019-170453' train_args_hash='712c28b' train_args= { "stage": "sft", "do_train": true, "model_name_or_path": "meta-llama/Llama-3.2-1B-Instruct", "dataset": "gnaf-2022-structured-training-1000000-v0-instruct-train", "eval_dataset": "gnaf-2022-structured-training-1000000-v0-instruct-test", "template": "llama3", "finetuning_type": "lora", "lora_target": "all", "output_dir": "finetune_output", "plot_loss": true, "per_device_train_batch_size": 2, "gradient_accumulation_steps": 4, "lr_scheduler_type": "cosine", "logging_steps": 5, "warmup_ratio": 0.1, "save_steps": 1000, "learning_rate": 5e-05, "num_train_epochs": 2.0, "max_samples": 10000, "max_grad_norm": 1.0, "loraplus_lr_ratio": 16.0, "fp16": true, "report_to": "none" }