Instructions to use cyttic/exp23-directfit-unfrozen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cyttic/exp23-directfit-unfrozen with Transformers:

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

pipe = pipeline("image-text-to-text", model="cyttic/exp23-directfit-unfrozen")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("cyttic/exp23-directfit-unfrozen")
model = AutoModelForMultimodalLM.from_pretrained("cyttic/exp23-directfit-unfrozen")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cyttic/exp23-directfit-unfrozen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cyttic/exp23-directfit-unfrozen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cyttic/exp23-directfit-unfrozen",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cyttic/exp23-directfit-unfrozen

SGLang

How to use cyttic/exp23-directfit-unfrozen 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 "cyttic/exp23-directfit-unfrozen" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cyttic/exp23-directfit-unfrozen",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "cyttic/exp23-directfit-unfrozen" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cyttic/exp23-directfit-unfrozen",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cyttic/exp23-directfit-unfrozen with Docker Model Runner:
```
docker model run hf.co/cyttic/exp23-directfit-unfrozen
```

exp23-directfit-unfrozen

This model is a fine-tuned version of cyttic/exp22-exp2warm-directfit-frozen on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4588
Cer: 0.0245
Wer: 0.0647

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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
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: linear
lr_scheduler_warmup_steps: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Cer	Wer
1.2577	0.0424	2500	1.3378	0.1416	0.2986
1.0413	0.0848	5000	1.2441	0.1150	0.2561
0.8436	0.1272	7500	1.0854	0.0917	0.2106
0.7762	0.1696	10000	0.9314	0.0735	0.1700
0.7096	0.2120	12500	0.8954	0.0627	0.1534
0.6205	0.2544	15000	0.7907	0.0550	0.1378
0.5873	0.2968	17500	0.7408	0.0486	0.1215
0.5504	0.3392	20000	0.6894	0.0427	0.1112
0.4826	0.3816	22500	0.6537	0.0406	0.1042
0.5224	0.4240	25000	0.6245	0.0380	0.0965
0.4430	0.4664	27500	0.5866	0.0366	0.0933
0.4219	0.5088	30000	0.5724	0.0340	0.0873
0.3557	0.5512	32500	0.5586	0.0318	0.0849
0.3866	0.5936	35000	0.5399	0.0328	0.0845
0.3586	0.6360	37500	0.5267	0.0305	0.0793
0.4009	0.6784	40000	0.5082	0.0301	0.0769
0.3140	0.7208	42500	0.5011	0.0289	0.0740
0.3435	0.7632	45000	0.4874	0.0276	0.0725
0.3141	0.8056	47500	0.4807	0.0267	0.0691
0.3200	0.8480	50000	0.4738	0.0267	0.0687
0.3201	0.8904	52500	0.4682	0.0250	0.0664
0.3314	0.9328	55000	0.4633	0.0249	0.0652
0.3427	0.9752	57500	0.4604	0.0244	0.0648
0.3020	1.0	58962	0.4588	0.0245	0.0647