Instructions to use QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF:Q4_K_M

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 QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF:Q4_K_M

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 QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/OpenHermes-2.5-Mistral-7B-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": "QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF:Q4_K_M

Ollama
How to use QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/OpenHermes-2.5-Mistral-7B-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 QuantFactory/OpenHermes-2.5-Mistral-7B-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 QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OpenHermes-2.5-Mistral-7B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

OpenHermes-2.5-Mistral-7B-GGUF

GGUF quantized versions for OpenHermes-2.5-Mistral-7B
Created using llama.cpp

Model description

OpenHermes 2.5 Mistral 7B is a state of the art Mistral Fine-tune, a continuation of OpenHermes 2 model, which trained on additional code datasets.

Potentially the most interesting finding from training on a good ratio (est. of around 7-14% of the total dataset) of code instruction was that it has boosted several non-code benchmarks, including TruthfulQA, AGIEval, and GPT4All suite. It did however reduce BigBench benchmark score, but the net gain overall is significant.

The code it trained on also improved it's humaneval score (benchmarking done by Glaive team) from 43% @ Pass 1 with Open Herms 2 to 50.7% @ Pass 1 with Open Hermes 2.5.

OpenHermes was trained on 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape. [More details soon]

Filtering was extensive of these public datasets, as well as conversion of all formats to ShareGPT, which was then further transformed by axolotl to use ChatML.

Downloads last month: 420

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/OpenHermes-2.5-Mistral-7B-GGUF

Base model

mistralai/Mistral-7B-v0.1

Finetuned

teknium/OpenHermes-2.5-Mistral-7B

Quantized

(52)

this model