Instructions to use Infi-MM/infimm-hd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Infi-MM/infimm-hd with Transformers:

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

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

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Infi-MM/infimm-hd", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Infi-MM/infimm-hd with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Infi-MM/infimm-hd

SGLang

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

Docker Model Runner
How to use Infi-MM/infimm-hd with Docker Model Runner:
```
docker model run hf.co/Infi-MM/infimm-hd
```

lllliuhhhhggg commited on Mar 4, 2024

Commit

af3d978

verified ·

1 Parent(s): aa89ed1

Update eva_vit_model.py

Browse files

Files changed (1) hide show

eva_vit_model.py +1 -5

eva_vit_model.py CHANGED Viewed

@@ -11,13 +11,9 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import yaml
-from open_clip.transform import image_transform
 from timm.models.layers import drop_path, to_2tuple, trunc_normal_
-from open_flamingo.src.util.download_utils import download_pretrained_weights_from_hdfs
-from open_flamingo.src.visual_encoder.rope import VisionRotaryEmbeddingFast
-from open_flamingo.src.visual_encoder.transformer import Attention, PatchDropout
-from open_flamingo.src.xperf_training import FTFlashAttention, FTLayerNorm, FTLinear
 if os.getenv("ENV_TYPE") == "deepspeed":
     try:

 import torch.nn as nn
 import torch.nn.functional as F
 import yaml
 from timm.models.layers import drop_path, to_2tuple, trunc_normal_
 if os.getenv("ENV_TYPE") == "deepspeed":
     try: