Instructions to use Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF",
	filename="Llama-3.2-11B-Vision-Instruct-mmproj.f16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF:F16

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 Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF:F16

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 Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF:F16

Use Docker

docker model run hf.co/Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF:F16

LM Studio
Jan
Ollama
How to use Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF with Ollama:
```
ollama run hf.co/Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF:F16
```

Unsloth Studio

How to use Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-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 Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-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 Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF to start chatting

How to use Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF:F16

Run Hermes

hermes

Docker Model Runner
How to use Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF:F16
```

Lemonade

How to use Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Sci-fi-vy/Llama-3.2-11B-Vision-Instruct-GGUF:F16

Run and chat with the model

lemonade run user.Llama-3.2-11B-Vision-Instruct-GGUF-F16

List all available models

lemonade list

Special Thanks

A huge thank you to the Meta and Llama team for creating and releasing this models.

Model Information

The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.

Model developer: Meta

Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.

Llama 3.2 family of models Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.

Model Release Date: Sept 25, 2024

Status: This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety.

License: Use of Llama 3.2 is governed by the Llama 3.2 Community License (a custom, commercial license agreement).

Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model README. For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go here.

Downloads last month: 318

GGUF

Model size

10B params

Architecture

mllama

Hardware compatibility

4-bit

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