Instructions to use RantiRepo/Qwen2-VL-7B-ChartQAtesting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RantiRepo/Qwen2-VL-7B-ChartQAtesting with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
model = PeftModel.from_pretrained(base_model, "RantiRepo/Qwen2-VL-7B-ChartQAtesting")

Transformers

How to use RantiRepo/Qwen2-VL-7B-ChartQAtesting with Transformers:

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

pipe = pipeline("text-generation", model="RantiRepo/Qwen2-VL-7B-ChartQAtesting")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("RantiRepo/Qwen2-VL-7B-ChartQAtesting", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RantiRepo/Qwen2-VL-7B-ChartQAtesting with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RantiRepo/Qwen2-VL-7B-ChartQAtesting"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RantiRepo/Qwen2-VL-7B-ChartQAtesting",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RantiRepo/Qwen2-VL-7B-ChartQAtesting

SGLang

How to use RantiRepo/Qwen2-VL-7B-ChartQAtesting 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 "RantiRepo/Qwen2-VL-7B-ChartQAtesting" \
    --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": "RantiRepo/Qwen2-VL-7B-ChartQAtesting",
		"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 "RantiRepo/Qwen2-VL-7B-ChartQAtesting" \
        --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": "RantiRepo/Qwen2-VL-7B-ChartQAtesting",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RantiRepo/Qwen2-VL-7B-ChartQAtesting with Docker Model Runner:
```
docker model run hf.co/RantiRepo/Qwen2-VL-7B-ChartQAtesting
```

RantiRepo commited on Feb 14

Commit

f7e61c9

verified ·

1 Parent(s): 18772b8

Upload processor_config.json with huggingface_hub

Browse files

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processor_config.json +63 -0

processor_config.json ADDED Viewed

	@@ -0,0 +1,63 @@

+{
+  "image_processor": {
+    "data_format": "channels_first",
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
+      0.48145466,
+      0.4578275,
+      0.40821073
+    ],
+    "image_processor_type": "Qwen2VLImageProcessorFast",
+    "image_std": [
+      0.26862954,
+      0.26130258,
+      0.27577711
+    ],
+    "merge_size": 2,
+    "patch_size": 14,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "size": {
+      "longest_edge": 12845056,
+      "shortest_edge": 3136
+    },
+    "temporal_patch_size": 2
+  },
+  "processor_class": "Qwen2VLProcessor",
+  "video_processor": {
+    "data_format": "channels_first",
+    "default_to_square": true,
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "do_sample_frames": false,
+    "image_mean": [
+      0.48145466,
+      0.4578275,
+      0.40821073
+    ],
+    "image_processor_type": "Qwen2VLImageProcessor",
+    "image_std": [
+      0.26862954,
+      0.26130258,
+      0.27577711
+    ],
+    "max_frames": 768,
+    "merge_size": 2,
+    "min_frames": 4,
+    "patch_size": 14,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "return_metadata": false,
+    "size": {
+      "longest_edge": 12845056,
+      "shortest_edge": 3136
+    },
+    "temporal_patch_size": 2,
+    "video_processor_type": "Qwen2VLVideoProcessor"
+  }
+}