Instructions to use Kquant03/Hippolyta-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kquant03/Hippolyta-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Kquant03/Hippolyta-7B-GGUF",
	filename="ggml-model-q2_k.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Kquant03/Hippolyta-7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Kquant03/Hippolyta-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Kquant03/Hippolyta-7B-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 Kquant03/Hippolyta-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Kquant03/Hippolyta-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 Kquant03/Hippolyta-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Kquant03/Hippolyta-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 Kquant03/Hippolyta-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Kquant03/Hippolyta-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Kquant03/Hippolyta-7B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Kquant03/Hippolyta-7B-GGUF with Ollama:
```
ollama run hf.co/Kquant03/Hippolyta-7B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Kquant03/Hippolyta-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 Kquant03/Hippolyta-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 Kquant03/Hippolyta-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 Kquant03/Hippolyta-7B-GGUF to start chatting

Docker Model Runner
How to use Kquant03/Hippolyta-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/Kquant03/Hippolyta-7B-GGUF:Q4_K_M
```

Lemonade

How to use Kquant03/Hippolyta-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Kquant03/Hippolyta-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Hippolyta-7B-GGUF-Q4_K_M

List all available models

lemonade list

The flower of Ares.

These are the GGUF files of the fine-tuned model. To be compiled with llama.cpp on oobabooga or VLLm.

Fine-tuned on mistralai/Mistral-7B-v0.1...my team and I reformatted many different datasets and included a small amount of private stuff to see how much we could improve mistral.

I spoke to it personally for about an hour, and I believe we need to work on our format for the private dataset a bit more, but other than that, it turned out great. I will be uploading it to open llm evaluations, today.

Provided files

Name	Quant method	Bits	Size	Max RAM required	Use case
Q2_K Tiny	Q2_K	2	2.7 GB	4.7 GB	smallest, significant quality loss - not recommended for most purposes
Q3_K_M	Q3_K_M	3	3.52 GB	5.52 GB	very small, high quality loss
Q4_0	Q4_0	4	4.11 GB	6.11 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Q4_K_M	Q4_K_M	4	4.37 GB	6.37 GB	medium, balanced quality - recommended
Q5_0	Q5_0	5	5 GB	7 GB	legacy; large, balanced quality
Q5_K_M	Q5_K_M	5	5.13 GB	7.13 GB	large, balanced quality - recommended
Q6 XL	Q6_K	6	5.94 GB	7.94 GB	very large, extremely low quality loss
Q8 XXL	Q8_0	8	7.7 GB	9.7 GB	very large, extremely low quality loss - not recommended

Uses Mistral prompt template with chat-instruct.

Downloads last month: 249

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

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Kquant03
/

Hippolyta-7B-GGUF

The flower of Ares.

These are the GGUF files of the fine-tuned model. To be compiled with llama.cpp on oobabooga or VLLm.

Provided files

Datasets used to train Kquant03/Hippolyta-7B-GGUF