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 / README.md

keizer69

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	---
	base_model: vikhyatk/moondream2
	library_name: peft
	pipeline_tag: image-text-to-text
	tags:
	- vision
	- image-classification
	- web-automation
	- lora
	- transformers
	---

	# Moondream FlowMind - Web UI Element Classifier

	LoRA adapter for Moondream2 fine-tuned on 2,711 web UI element screenshots to classify element purpose.

	## Model Details

	### Model Description

	This is a LoRA adapter fine-tuned on vikhyatk/moondream2 (2024-08-26 revision) to classify web UI elements from screenshots. The model identifies 13 element types: menu, login, signup, search, submit, claim, cart, play, pause, skip-ad, close, accept, close-ad, unknown.

	- Developed by: FlowHunter
	- Model type: Vision-Language Model (LoRA adapter)
	- Base model: vikhyatk/moondream2
	- License: Apache 2.0
	- Finetuned from model: vikhyatk/moondream2

	### Model Sources [optional]

	<!-- Provide the basic links for the model. -->

	- Repository: [More Information Needed]
	- Paper [optional]: [More Information Needed]
	- Demo [optional]: [More Information Needed]

	## Uses

	<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

	### Direct Use

	<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

	[More Information Needed]

	### Downstream Use [optional]

	<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

	[More Information Needed]

	### Out-of-Scope Use

	<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

	[More Information Needed]

	## Bias, Risks, and Limitations

	<!-- This section is meant to convey both technical and sociotechnical limitations. -->

	[More Information Needed]

	### Recommendations

	<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

	Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

	## How to Get Started with the Model

	Use the code below to get started with the model.

	[More Information Needed]

	## Training Details

	### Training Data

	<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

	[More Information Needed]

	### Training Procedure

	<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

	#### Preprocessing [optional]

	[More Information Needed]


	#### Training Hyperparameters

	- Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

	#### Speeds, Sizes, Times [optional]

	<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

	[More Information Needed]

	## Evaluation

	<!-- This section describes the evaluation protocols and provides the results. -->

	### Testing Data, Factors & Metrics

	#### Testing Data

	<!-- This should link to a Dataset Card if possible. -->

	[More Information Needed]

	#### Factors

	<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

	[More Information Needed]

	#### Metrics

	<!-- These are the evaluation metrics being used, ideally with a description of why. -->

	[More Information Needed]

	### Results

	[More Information Needed]

	#### Summary



	## Model Examination [optional]

	<!-- Relevant interpretability work for the model goes here -->

	[More Information Needed]

	## Environmental Impact

	<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

	Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

	- Hardware Type: [More Information Needed]
	- Hours used: [More Information Needed]
	- Cloud Provider: [More Information Needed]
	- Compute Region: [More Information Needed]
	- Carbon Emitted: [More Information Needed]

	## Technical Specifications [optional]

	### Model Architecture and Objective

	[More Information Needed]

	### Compute Infrastructure

	[More Information Needed]

	#### Hardware

	[More Information Needed]

	#### Software

	[More Information Needed]

	## Citation [optional]

	<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

	BibTeX:

	[More Information Needed]

	APA:

	[More Information Needed]

	## Glossary [optional]

	<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

	[More Information Needed]

	## More Information [optional]

	[More Information Needed]

	## Model Card Authors [optional]

	[More Information Needed]

	## Model Card Contact

	[More Information Needed]
	### Framework versions

	- PEFT 0.19.1