Instructions to use aparaselli/llava-7b-ft-unbound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aparaselli/llava-7b-ft-unbound with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="aparaselli/llava-7b-ft-unbound")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("aparaselli/llava-7b-ft-unbound") model = AutoModelForMultimodalLM.from_pretrained("aparaselli/llava-7b-ft-unbound", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use aparaselli/llava-7b-ft-unbound with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aparaselli/llava-7b-ft-unbound" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aparaselli/llava-7b-ft-unbound", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/aparaselli/llava-7b-ft-unbound
- SGLang
How to use aparaselli/llava-7b-ft-unbound 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 "aparaselli/llava-7b-ft-unbound" \ --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": "aparaselli/llava-7b-ft-unbound", "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 "aparaselli/llava-7b-ft-unbound" \ --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": "aparaselli/llava-7b-ft-unbound", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use aparaselli/llava-7b-ft-unbound with Docker Model Runner:
docker model run hf.co/aparaselli/llava-7b-ft-unbound
Your Fine-tuned LLaVA Model
This model is a fine-tuned version of llava-hf/llava-1.5-7b-hf.
Model Description
- Base Model: llava-hf/llava-1.5-7b-hf
- Model Type: Vision-Language Model
- Architecture: LlavaForConditionalGeneration
- Processor: Use the original processor from
llava-hf/llava-1.5-7b-hf
Usage
from transformers import LlavaForConditionalGeneration, LlavaProcessor
import torch
# Load fine-tuned model
model = LlavaForConditionalGeneration.from_pretrained("aparaselli/llava-7b-ft-unbound", trust_remote_code=True)
# Load original processor (recommended approach)
processor = LlavaProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf", trust_remote_code=True)
# Your inference code here...
Important Notes
- This repository contains only the fine-tuned model weights
- Always use the processor from
llava-hf/llava-1.5-7b-hffor best compatibility - The model was fine-tuned on top of the base model but uses the original tokenization and image processing
Training Details
Add details about your training process, dataset, and hyperparameters here.
Evaluation
Add evaluation results here.
Citation
If you use this model, please cite the original LLaVA paper and mention your fine-tuning work.
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llava-hf/llava-1.5-7b-hf