Instructions to use keizer69/moondream-flowmind with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use keizer69/moondream-flowmind with PEFT:
```
Base model is not found.
```

How to use keizer69/moondream-flowmind with Transformers:

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

pipe = pipeline("image-text-to-text", model="keizer69/moondream-flowmind", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("keizer69/moondream-flowmind", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use keizer69/moondream-flowmind with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "keizer69/moondream-flowmind"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "keizer69/moondream-flowmind",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/keizer69/moondream-flowmind

SGLang

How to use keizer69/moondream-flowmind 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 "keizer69/moondream-flowmind" \
    --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": "keizer69/moondream-flowmind",
		"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 "keizer69/moondream-flowmind" \
        --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": "keizer69/moondream-flowmind",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use keizer69/moondream-flowmind with Docker Model Runner:
```
docker model run hf.co/keizer69/moondream-flowmind
```

moondream-flowmind

File size: 558 Bytes

9a2b6bf

# Adopted from https://github.com/crowsonkb/k-diffusion/blob/transformer-model-v2/k_diffusion/layers.py

import torch
import torch.nn as nn
import math


class FourierFeatures(nn.Module):
    def __init__(self, in_features, out_features, std=1.0):
        super().__init__()
        assert out_features % 2 == 0
        self.register_buffer(
            "weight", torch.randn([out_features // 2, in_features]) * std
        )

    def forward(self, input):
        f = 2 * math.pi * input @ self.weight.T
        return torch.cat([f.cos(), f.sin()], dim=-1)