Instructions to use OEvortex/HelpingAI-Vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OEvortex/HelpingAI-Vision with Transformers:

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

pipe = pipeline("image-text-to-text", model="OEvortex/HelpingAI-Vision", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("OEvortex/HelpingAI-Vision", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OEvortex/HelpingAI-Vision with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OEvortex/HelpingAI-Vision

SGLang

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

Docker Model Runner
How to use OEvortex/HelpingAI-Vision with Docker Model Runner:
```
docker model run hf.co/OEvortex/HelpingAI-Vision
```

Abhaykoul commited on Jan 21, 2024

Commit

e6f3920

verified ·

1 Parent(s): 51a6725

Update README.md

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README.md +9 -4

README.md CHANGED Viewed

@@ -109,17 +109,22 @@ Describe the image.<|im_end|>
 **Process inputs**
 ```python
-inputs = processor(prompt, raw_image, model, return_tensors='pt')
 inputs['input_ids'] = inputs['input_ids'].to(model.device)
 inputs['attention_mask'] = inputs['attention_mask'].to(model.device)
 ```
 **Generate the data**
 ```python
-import torch
 with torch.inference_mode():
-  output = model.generate(**inputs, max_new_tokens=200, do_sample=True, temperature=0.4, pad_token_id=tokenizer.eos_token_id, eos_token_id=tokenizer.eos_token_id)
 ```

 **Process inputs**
 ```python
+with torch.inference_mode():
+  inputs = processor(prompt, raw_image, model, return_tensors='pt')
 inputs['input_ids'] = inputs['input_ids'].to(model.device)
 inputs['attention_mask'] = inputs['attention_mask'].to(model.device)
+from transformers import TextStreamer
+streamer = TextStreamer(tokenizer)
 ```
 **Generate the data**
 ```python
+%%time
 with torch.inference_mode():
+  output = model.generate(**inputs, max_new_tokens=200, do_sample=True, top_p=0.9, temperature=1.2, eos_token_id=tokenizer.eos_token_id, streamer=streamer)
+print(tokenizer.decode(output[0]).replace(prompt, "").replace("<|im_end|>", ""))
 ```