Instructions to use redponike/ether0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use redponike/ether0-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="redponike/ether0-GGUF",
	filename="ether0-BF16.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 redponike/ether0-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf redponike/ether0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf redponike/ether0-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 redponike/ether0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf redponike/ether0-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 redponike/ether0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf redponike/ether0-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 redponike/ether0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf redponike/ether0-GGUF:Q4_K_M

Use Docker

docker model run hf.co/redponike/ether0-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use redponike/ether0-GGUF with Ollama:
```
ollama run hf.co/redponike/ether0-GGUF:Q4_K_M
```

Unsloth Studio new

How to use redponike/ether0-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 redponike/ether0-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 redponike/ether0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for redponike/ether0-GGUF to start chatting

Docker Model Runner
How to use redponike/ether0-GGUF with Docker Model Runner:
```
docker model run hf.co/redponike/ether0-GGUF:Q4_K_M
```

Lemonade

How to use redponike/ether0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull redponike/ether0-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.ether0-GGUF-Q4_K_M

List all available models

lemonade list

GGUF quants of futurehouse/ether0

Link to preprint/paper

As this model's training was primarily done on SMILES strings of organic molecules, and is therefore meant for conversations containing a good amount of them, I recommend using Q8_0, Q6_K, Q5_K_M, or Q5_K_S if your bandwidth for these particular sizes yield an acceptable performance for you. The perplexity of these quants should be good enough.

Using llama.cpp b5602 (commit 745aa5319b9930068aff5e87cf5e9eef7227339b)

The importance matrix was generated with calibration_datav3.txt.

All quants were generated/calibrated with the imatrix, including the K quants.

Quantized from BF16.

Downloads last month: 95

GGUF

Model size

24B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

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Model tree for redponike/ether0-GGUF

Base model

mistralai/Mistral-Small-24B-Base-2501

Finetuned

mistralai/Mistral-Small-24B-Instruct-2501

Finetuned

futurehouse/ether0

Quantized

(6)

this model