Instructions to use marianna13/openhermes-7b-llava-instruct-665k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use marianna13/openhermes-7b-llava-instruct-665k with Transformers:

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

pipe = pipeline("text-generation", model="marianna13/openhermes-7b-llava-instruct-665k")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("marianna13/openhermes-7b-llava-instruct-665k", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use marianna13/openhermes-7b-llava-instruct-665k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "marianna13/openhermes-7b-llava-instruct-665k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "marianna13/openhermes-7b-llava-instruct-665k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/marianna13/openhermes-7b-llava-instruct-665k

SGLang

How to use marianna13/openhermes-7b-llava-instruct-665k 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 "marianna13/openhermes-7b-llava-instruct-665k" \
    --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": "marianna13/openhermes-7b-llava-instruct-665k",
		"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 "marianna13/openhermes-7b-llava-instruct-665k" \
        --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": "marianna13/openhermes-7b-llava-instruct-665k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use marianna13/openhermes-7b-llava-instruct-665k with Docker Model Runner:
```
docker model run hf.co/marianna13/openhermes-7b-llava-instruct-665k
```

Model Card for OpenHermes-7B-llava-instruct

Model Details

Model Description

Developed by: LAION, SkunkworksAI
Model type: LLaVA is an open-source chatbot trained by fine-tuning OpenHermes on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture
Finetuned from model: OpenHermes-7B
Finetuned from model: Apache-2.0

Model Sources

Repository: BakLLaVa

Evaluation

model	SQA	POPE	GQA
llava-1.5-7b	67.97%	85.30%	61.96%
openhermes-7b-llava-instruct-665k	59.64%	84.60%	42.39%

Downloads last month: 6

Safetensors

Model size

7B params

Tensor type

F32

marianna13
/

openhermes-7b-llava-instruct-665k

Model Card for OpenHermes-7B-llava-instruct

Model Details

Model Description

Model Sources

Evaluation

Datasets used to train marianna13/openhermes-7b-llava-instruct-665k