Instructions to use ahmed-masry/ChartInstruct-LLama2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ahmed-masry/ChartInstruct-LLama2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="ahmed-masry/ChartInstruct-LLama2")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("ahmed-masry/ChartInstruct-LLama2")
model = AutoModelForImageTextToText.from_pretrained("ahmed-masry/ChartInstruct-LLama2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ahmed-masry/ChartInstruct-LLama2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ahmed-masry/ChartInstruct-LLama2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ahmed-masry/ChartInstruct-LLama2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ahmed-masry/ChartInstruct-LLama2

SGLang

How to use ahmed-masry/ChartInstruct-LLama2 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 "ahmed-masry/ChartInstruct-LLama2" \
    --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": "ahmed-masry/ChartInstruct-LLama2",
		"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 "ahmed-masry/ChartInstruct-LLama2" \
        --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": "ahmed-masry/ChartInstruct-LLama2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ahmed-masry/ChartInstruct-LLama2 with Docker Model Runner:
```
docker model run hf.co/ahmed-masry/ChartInstruct-LLama2
```

ChartInstruct-LLama2

File size: 774 Bytes

7718a87

{
  "_valid_processor_keys": [
    "images",
    "do_resize",
    "size",
    "resample",
    "do_thumbnail",
    "do_align_long_axis",
    "do_pad",
    "random_padding",
    "do_rescale",
    "rescale_factor",
    "do_normalize",
    "image_mean",
    "image_std",
    "return_tensors",
    "data_format",
    "input_data_format"
  ],
  "do_align_long_axis": false,
  "do_normalize": true,
  "do_pad": true,
  "do_rescale": true,
  "do_resize": true,
  "do_thumbnail": true,
  "image_mean": [
    0.5,
    0.5,
    0.5
  ],
  "image_processor_type": "DonutImageProcessor",
  "image_std": [
    0.5,
    0.5,
    0.5
  ],
  "processor_class": "LlavaProcessor",
  "resample": 2,
  "rescale_factor": 0.00392156862745098,
  "size": {
    "height": 512,
    "width": 512
  }
}