Instructions to use ChaoticNeutrals/ChaoticVision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ChaoticNeutrals/ChaoticVision with Transformers:

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

pipe = pipeline("text-generation", model="ChaoticNeutrals/ChaoticVision")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ChaoticNeutrals/ChaoticVision", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ChaoticNeutrals/ChaoticVision with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ChaoticNeutrals/ChaoticVision

SGLang

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

Docker Model Runner
How to use ChaoticNeutrals/ChaoticVision with Docker Model Runner:
```
docker model run hf.co/ChaoticNeutrals/ChaoticVision
```

ChaoticVision

This is a highly experimental merge of two Mistral Llava models with the intent of splitting out a mmproj projector file with a more robust captioning capability. I do not know if this model is functional, and will not be testing it as a language model, so use at your own risk.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: jeiku/llavamistral1.6configedit
        layer_range: [0, 32]
      - model: jeiku/noushermesvisionalphaconfigedit
        layer_range: [0, 32]
merge_method: slerp
base_model: jeiku/llavamistral1.6configedit
parameters:
  t:
    - filter: self_attn
      value: [0.5, 0.5, 0.5, 0.5, 0.5]
    - filter: mlp
      value: [0.5, 0.5, 0.5, 0.5, 0.5]
    - value: 0.5
dtype: bfloat16

Downloads last month: 16

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for ChaoticNeutrals/ChaoticVision

jeiku/llavamistral1.6configedit

jeiku/noushermesvisionalphaconfigedit

Merge model

this model