Instructions to use LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF",
	filename="OpenHermes-2.5-Code-290k-13B-Q3_K_L.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LoneStriker/OpenHermes-2.5-Code-290k-13B-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 LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LoneStriker/OpenHermes-2.5-Code-290k-13B-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 LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF with Ollama:
```
ollama run hf.co/LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF:Q4_K_M
```

Unsloth Studio

How to use LoneStriker/OpenHermes-2.5-Code-290k-13B-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 LoneStriker/OpenHermes-2.5-Code-290k-13B-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 LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF to start chatting

Docker Model Runner
How to use LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF with Docker Model Runner:
```
docker model run hf.co/LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF:Q4_K_M
```

Lemonade

How to use LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OpenHermes-2.5-Code-290k-13B-GGUF-Q4_K_M

List all available models

lemonade list

OpenHermes-2.5-Code-290k-13B

OpenHermes-2.5-Code-290k-13B is a state of the art Llama-2 Fine-tune, which is trained on additional code dataset. This model is trained on my existing dataset OpenHermes-2.5-Code-290k. This dataset is amalgamation of two datasets. I have used OpenHermes-2.5 a super quality dataset made avaliable by teknium. Other datset is my own Code-290k-ShareGPT. Dataset is in Vicuna/ShareGPT format. There are around 1.29 million set of conversations. I have cleaned the dataset provided by Teknium and removed metadata such as "source" & "category" etc. This dataset has primarily synthetically generated instruction and chat samples.

This model has enhanced coding capabilities besides other capabilities such as Blogging, story generation, Q&A and many more.

Training:

Entire model was trained on 4 x A100 80GB. For 2 epoch, training took 21 Days. Fschat & DeepSpeed codebase was used for training purpose. This was trained on Llama-2 by Meta.

This is a full fine tuned model. Links for quantized models will be updated soon.

GPTQ, GGUF, AWQ & Exllama

GPTQ: TBA

GGUF: TBA

AWQ: TBA

Exllama v2: TBA

Example Prompt:

This is a conversation with your helpful AI assistant. AI assistant can generate Code in various Programming Languages along with necessary explanation. It can generate Story, Blogs .....

Context
You are a helpful AI assistant.

USER: <prompt>
ASSISTANT:

You can modify above Prompt as per your requirement. I have used ShareGPT/Vicuna format v1.1 .

I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development.

Thank you for your love & support.

Example Output

I will update soon.

Downloads last month: 22

GGUF

Model size

13B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

LoneStriker
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OpenHermes-2.5-Code-290k-13B-GGUF

Dataset used to train LoneStriker/OpenHermes-2.5-Code-290k-13B-GGUF