Instructions to use Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF",
	filename="Hermes-3-Llama-3.1-8B-OF32.EF32.IQ4_K.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 Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF:F32
# Run inference directly in the terminal:
llama-cli -hf Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF:F32

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF:F32
# Run inference directly in the terminal:
llama-cli -hf Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF:F32

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 Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF:F32
# Run inference directly in the terminal:
./llama-cli -hf Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF:F32

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 Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF:F32
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF:F32

Use Docker

docker model run hf.co/Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF:F32

LM Studio
Jan
Ollama
How to use Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF with Ollama:
```
ollama run hf.co/Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF:F32
```

Unsloth Studio

How to use Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-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 Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-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 Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF with Docker Model Runner:
```
docker model run hf.co/Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF:F32
```

Lemonade

How to use Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF:F32

Run and chat with the model

lemonade run user.Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF-F32

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Custom GGUF quants of NousResearch/Hermes-3-Llama-3.1-8B, where the Output Tensors are quantized to Q8_0 while the Embeddings are kept at F32. Enjoy! 🧠🔥🚀

Update: This repo now contains OF32.EF32 GGUF IQuants for even more accuracy. Enjoy! 😋

UPDATE: This repo now contains updated O.E.IQuants, which were quantized, using a new F32-imatrix, using llama.cpp version: 4658 (855cd073). This particular version of llama.cpp added support for Non-Contiguous RMS Norms. This has enhanced model coherence and further increased model creativity (from testing).

Downloads last month: 281

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

6-bit

8-bit

32-bit

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Collection including Joseph717171/Hermes-3-Llama-3.1-8B-OQ8_0-F32.EF32.IQ4_K-Q8_0-GGUF

GGUF LLaMa-3.1-8B-Instruct-Based OQ8_0.EF32 IQuants

Collection

Custom GGUF quants of Llama-3.1-8B-Instruct fine-tunes, where the Output Tensors are quantized to Q8_0 while the Embeddings are kept at F32. 🧠🔥🚀 • 4 items • Updated Feb 14, 2025