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

Libraries
PEFT
How to use DVLe/emo_fastvlm_vqa with PEFT:
```
Task type is invalid.
```

How to use DVLe/emo_fastvlm_vqa with Transformers:

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

pipe = pipeline("text-generation", model="DVLe/emo_fastvlm_vqa", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("DVLe/emo_fastvlm_vqa", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DVLe/emo_fastvlm_vqa with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/DVLe/emo_fastvlm_vqa

SGLang

How to use DVLe/emo_fastvlm_vqa 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 "DVLe/emo_fastvlm_vqa" \
    --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": "DVLe/emo_fastvlm_vqa",
		"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 "DVLe/emo_fastvlm_vqa" \
        --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": "DVLe/emo_fastvlm_vqa",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

emo_fastvlm_vqa

File size: 1,931 Bytes

277ea8e

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    "AutoModelForCausalLM": "llava_qwen.LlavaQwen2ForCausalLM"
  },
  "bos_token_id": 151643,
  "dtype": "bfloat16",
  "eos_token_id": 151645,
  "freeze_mm_mlp_adapter": false,
  "hidden_act": "silu",
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  "mm_patch_merge_type": "flat",
  "mm_projector_lr": null,
  "mm_projector_type": "mlp2x_gelu",
  "mm_use_im_patch_token": false,
  "mm_use_im_start_end": false,
  "mm_vision_select_feature": "patch",
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  "model_type": "llava_qwen2",
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}