Instructions to use vishwa27/GIT_inf_only_ep5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vishwa27/GIT_inf_only_ep5 with Transformers:

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

pipe = pipeline("image-text-to-text", model="vishwa27/GIT_inf_only_ep5")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("vishwa27/GIT_inf_only_ep5")
model = AutoModelForMultimodalLM.from_pretrained("vishwa27/GIT_inf_only_ep5")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vishwa27/GIT_inf_only_ep5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vishwa27/GIT_inf_only_ep5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vishwa27/GIT_inf_only_ep5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vishwa27/GIT_inf_only_ep5

SGLang

How to use vishwa27/GIT_inf_only_ep5 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 "vishwa27/GIT_inf_only_ep5" \
    --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": "vishwa27/GIT_inf_only_ep5",
		"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 "vishwa27/GIT_inf_only_ep5" \
        --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": "vishwa27/GIT_inf_only_ep5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vishwa27/GIT_inf_only_ep5 with Docker Model Runner:
```
docker model run hf.co/vishwa27/GIT_inf_only_ep5
```

GIT_inf_only_ep5

This model is a fine-tuned version of microsoft/git-base on the Sherlock dataset. It achieves the following results on the evaluation set:

Loss: 0.0494
Rouge1: 44.6137
Rouge2: 13.6972
Rougel: 43.5306
Rougelsum: 43.5484
Gen Len: 207.45

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
0.0526	1.0	1586	0.0505	3.3402	0.8279	3.239	3.2361	207.45
0.0457	2.0	3172	0.0496	40.1232	13.4573	39.2584	39.2555	207.4505
0.0404	3.0	4758	0.0492	42.6704	12.4947	41.5807	41.5836	207.4505
0.0368	4.0	6344	0.0494	44.5041	14.6203	43.3769	43.423	207.4505
0.0331	5.0	7930	0.0494	44.6137	13.6972	43.5306	43.5484	207.45

Framework versions

Transformers 4.38.1
Pytorch 2.2.1+cu121
Datasets 2.17.1
Tokenizers 0.15.2

Downloads last month: 1

Safetensors

Model size

0.2B params

Tensor type

F32

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