Instructions to use TrevorJS/check-amount-deverbalizer-smollm2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TrevorJS/check-amount-deverbalizer-smollm2 with Transformers:

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

pipe = pipeline("text-generation", model="TrevorJS/check-amount-deverbalizer-smollm2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TrevorJS/check-amount-deverbalizer-smollm2")
model = AutoModelForCausalLM.from_pretrained("TrevorJS/check-amount-deverbalizer-smollm2")
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 TrevorJS/check-amount-deverbalizer-smollm2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TrevorJS/check-amount-deverbalizer-smollm2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TrevorJS/check-amount-deverbalizer-smollm2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TrevorJS/check-amount-deverbalizer-smollm2

SGLang

How to use TrevorJS/check-amount-deverbalizer-smollm2 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 "TrevorJS/check-amount-deverbalizer-smollm2" \
    --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": "TrevorJS/check-amount-deverbalizer-smollm2",
		"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 "TrevorJS/check-amount-deverbalizer-smollm2" \
        --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": "TrevorJS/check-amount-deverbalizer-smollm2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TrevorJS/check-amount-deverbalizer-smollm2 with Docker Model Runner:
```
docker model run hf.co/TrevorJS/check-amount-deverbalizer-smollm2
```

check-amount-deverbalizer-smollm2

This model is a fine-tuned version of HuggingFaceTB/SmolLM2-135M-Instruct on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1520
Json Parse Rate: 0.96
Dollar Accuracy: 0.96
Cents Accuracy: 0.96
Digit Count Accuracy: 0.96
Perfect Match: 0.96

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 128
eval_batch_size: 2
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Json Parse Rate	Dollar Accuracy	Cents Accuracy	Digit Count Accuracy	Perfect Match
0.1588	0.2128	200	0.1580	0.9571	0.9271	0.9529	0.95	0.9186
0.1576	0.4255	400	0.1549	0.96	0.9557	0.96	0.96	0.9557
0.1546	0.6383	600	0.1539	0.96	0.9571	0.96	0.96	0.9571
0.154	0.8511	800	0.1541	0.9586	0.95	0.9586	0.9586	0.95
0.1539	1.0638	1000	0.1536	0.96	0.9557	0.96	0.96	0.9557
0.1544	1.2766	1200	0.1524	0.96	0.96	0.96	0.96	0.96
0.1535	1.4894	1400	0.1529	0.96	0.9571	0.96	0.96	0.9571
0.1537	1.7021	1600	0.1525	0.96	0.9586	0.96	0.96	0.9586
0.1548	1.9149	1800	0.1527	0.96	0.9586	0.96	0.96	0.9586
0.1545	2.1277	2000	0.1524	0.96	0.96	0.96	0.96	0.96
0.1534	2.3404	2200	0.1526	0.96	0.96	0.96	0.96	0.96
0.1533	2.5532	2400	0.1522	0.96	0.96	0.96	0.96	0.96
0.1524	2.7660	2600	0.1521	0.96	0.96	0.96	0.96	0.96
0.1527	2.9787	2800	0.1520	0.96	0.96	0.96	0.96	0.96

Framework versions

Transformers 4.48.2
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

Downloads last month: 5

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for TrevorJS/check-amount-deverbalizer-smollm2

Base model

HuggingFaceTB/SmolLM2-135M

Quantized

HuggingFaceTB/SmolLM2-135M-Instruct

Finetuned

(328)

this model

TrevorJS
/

check-amount-deverbalizer-smollm2