Instructions to use billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF", dtype="auto")

llama-cpp-python

How to use billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF",
	filename="NousResearch_Nous-Hermes-2-Vision-GGUF_Q4_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF:Q4_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF:Q4_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF:Q4_0

Use Docker

docker model run hf.co/billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF:Q4_0

LM Studio
Jan

vLLM

How to use billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF:Q4_0

SGLang

How to use billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF 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 "billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF" \
    --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": "billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF",
		"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 "billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF" \
        --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": "billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF with Ollama:
```
ollama run hf.co/billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF:Q4_0
```

Unsloth Studio

How to use billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF with Docker Model Runner:
```
docker model run hf.co/billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF:Q4_0
```

Lemonade

How to use billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF:Q4_0

Run and chat with the model

lemonade run user.llava-NousResearch_Nous-Hermes-2-Vision-GGUF-Q4_0

List all available models

lemonade list

GGUF Quants by Twobob, Thanks to @jartine and @cmp-nct for the assists

It's vicuna ref: here

Caveat emptor: There is still some kind of bug in the inference that is likely to get fixed upstream. Just FYI

Nous-Hermes-2-Vision - Mistral 7B

In the tapestry of Greek mythology, Hermes reigns as the eloquent Messenger of the Gods, a deity who deftly bridges the realms through the art of communication. It is in homage to this divine mediator that I name this advanced LLM "Hermes," a system crafted to navigate the complex intricacies of human discourse with celestial finesse.

Model description

Nous-Hermes-2-Vision stands as a pioneering Vision-Language Model, leveraging advancements from the renowned OpenHermes-2.5-Mistral-7B by teknium. This model incorporates two pivotal enhancements, setting it apart as a cutting-edge solution:

SigLIP-400M Integration: Diverging from traditional approaches that rely on substantial 3B vision encoders, Nous-Hermes-2-Vision harnesses the formidable SigLIP-400M. This strategic choice not only streamlines the model's architecture, making it more lightweight, but also capitalizes on SigLIP's remarkable capabilities. The result? A remarkable boost in performance that defies conventional expectations.
Custom Dataset Enriched with Function Calling: Our model's training data includes a unique feature – function calling. This distinctive addition transforms Nous-Hermes-2-Vision into a Vision-Language Action Model. Developers now have a versatile tool at their disposal, primed for crafting a myriad of ingenious automations.

This project is led by qnguyen3 and teknium.

Training

Dataset

220K from LVIS-INSTRUCT4V
60K from ShareGPT4V
150K Private Function Calling Data
50K conversations from teknium's OpenHermes-2.5

Usage

Prompt Format

Like other LLaVA's variants, this model uses Vicuna-V1 as its prompt template. Please refer to conv_llava_v1 in this file
For Gradio UI, please visit this GitHub Repo

Function Calling

For functiong calling, the message should start with a <fn_call> tag. Here is an example:

<fn_call>{
  "type": "object",
  "properties": {
    "bus_colors": {
      "type": "array",
      "description": "The colors of the bus in the image.",
      "items": {
        "type": "string",
        "enum": ["red", "blue", "green", "white"]
      }
    },
    "bus_features": {
      "type": "string",
      "description": "The features seen on the back of the bus."
    },
    "bus_location": {
      "type": "string",
      "description": "The location of the bus (driving or pulled off to the side).",
      "enum": ["driving", "pulled off to the side"]
    }
  }
}

Output:

{
  "bus_colors": ["red", "white"],
  "bus_features": "An advertisement",
  "bus_location": "driving"
}

Example

Chat

Function Calling

Input image:

Input message:

<fn_call>{
    "type": "object",
    "properties": {
      "food_list": {
        "type": "array",
        "description": "List of all the food",
        "items": {
          "type": "string",
        }
      },
    }
}

Output:

{
    "food_list": [
        "Double Burger",
        "Cheeseburger",
        "French Fries",
        "Shakes",
        "Coffee"
    ]
}

Downloads last month: 1,580

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF

Base model

mistralai/Mistral-7B-v0.1

Quantized

(187)

this model

billborkowski
/

llava-NousResearch_Nous-Hermes-2-Vision-GGUF

Nous-Hermes-2-Vision - Mistral 7B

Model description

Training

Dataset

Usage

Prompt Format

Function Calling

Example

Chat

Function Calling

Model tree for billborkowski/llava-NousResearch_Nous-Hermes-2-Vision-GGUF

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