Instructions to use trollek/SmolImagePromptHelper-135M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use trollek/SmolImagePromptHelper-135M with Transformers:

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

pipe = pipeline("text-generation", model="trollek/SmolImagePromptHelper-135M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("trollek/SmolImagePromptHelper-135M")
model = AutoModelForCausalLM.from_pretrained("trollek/SmolImagePromptHelper-135M")
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

vLLM

How to use trollek/SmolImagePromptHelper-135M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "trollek/SmolImagePromptHelper-135M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trollek/SmolImagePromptHelper-135M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/trollek/SmolImagePromptHelper-135M

SGLang

How to use trollek/SmolImagePromptHelper-135M 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 "trollek/SmolImagePromptHelper-135M" \
    --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": "trollek/SmolImagePromptHelper-135M",
		"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 "trollek/SmolImagePromptHelper-135M" \
        --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": "trollek/SmolImagePromptHelper-135M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use trollek/SmolImagePromptHelper-135M with Docker Model Runner:
```
docker model run hf.co/trollek/SmolImagePromptHelper-135M
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Smol Image Prompt Helper

This is meant to be a drop-in replacement for my last image prompt helper but with a new trick and a much smaller size. It achieves the following results on the evaluation set:

Loss: 1.0077

Model description

Lets say you have a node in ComfyUI to parse JSON and send the appropriate prompt to the text encoders. Tadaaa:

You are an AI assistant tasked with expanding and formatting image prompts. You are given an input that you will need to write image prompts for different text encoders.
Always respond with the following format:
{
    "clip_l": "<keywords from image analysis>",
    "clip_g": "<simple descriptions of the image>",
    "t5xxl": "<complex semanticly rich description of the image>",
    "negative": "<contrasting keywords for what is not in the image>"
}

Intended uses & limitations

Have a look at the dataset that I created (ImagePromptHelper-v02 (CC BY 4.0)) and you will see whaaaaat I've doooone.

Training procedure

I continued the pretraining with SDXL and Flux prompts and then SFT'd it on my own dataset.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 1
seed: 443
gradient_accumulation_steps: 8
total_train_batch_size: 64
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: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
1.1631	0.3966	500	1.2816
1.019	0.7932	1000	1.1431
0.9857	1.1896	1500	1.0818
1.0436	1.5862	2000	1.0459
0.9918	1.9827	2500	1.0235
0.9287	2.3791	3000	1.0114
0.9205	2.7757	3500	1.0079

Framework versions

Transformers 4.50.0
Pytorch 2.6.0+cu126
Datasets 3.4.1
Tokenizers 0.21.0

Downloads last month: 4

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for trollek/SmolImagePromptHelper-135M

Base model

HuggingFaceTB/SmolLM2-135M

Finetuned

(902)

this model

Quantizations

1 model

trollek
/

SmolImagePromptHelper-135M