Instructions to use DiYaZeN/aya-sl-biz-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DiYaZeN/aya-sl-biz-8b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DiYaZeN/aya-sl-biz-8b",
	filename="model-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use DiYaZeN/aya-sl-biz-8b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DiYaZeN/aya-sl-biz-8b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DiYaZeN/aya-sl-biz-8b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DiYaZeN/aya-sl-biz-8b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DiYaZeN/aya-sl-biz-8b: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 DiYaZeN/aya-sl-biz-8b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DiYaZeN/aya-sl-biz-8b: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 DiYaZeN/aya-sl-biz-8b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DiYaZeN/aya-sl-biz-8b:Q4_K_M

Use Docker

docker model run hf.co/DiYaZeN/aya-sl-biz-8b:Q4_K_M

LM Studio
Jan

vLLM

How to use DiYaZeN/aya-sl-biz-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DiYaZeN/aya-sl-biz-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DiYaZeN/aya-sl-biz-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DiYaZeN/aya-sl-biz-8b:Q4_K_M

Ollama
How to use DiYaZeN/aya-sl-biz-8b with Ollama:
```
ollama run hf.co/DiYaZeN/aya-sl-biz-8b:Q4_K_M
```

Unsloth Studio

How to use DiYaZeN/aya-sl-biz-8b 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 DiYaZeN/aya-sl-biz-8b 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 DiYaZeN/aya-sl-biz-8b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DiYaZeN/aya-sl-biz-8b to start chatting

Atomic Chat new
Docker Model Runner
How to use DiYaZeN/aya-sl-biz-8b with Docker Model Runner:
```
docker model run hf.co/DiYaZeN/aya-sl-biz-8b:Q4_K_M
```

Lemonade

How to use DiYaZeN/aya-sl-biz-8b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DiYaZeN/aya-sl-biz-8b:Q4_K_M

Run and chat with the model

lemonade run user.aya-sl-biz-8b-Q4_K_M

List all available models

lemonade list

Aya Sl Biz 8B

This is a GGUF format quantized version of a fine-tuned CohereForAI/aya-23-8B model.

Model Details

Original Model: CohereForAI/aya-23-8B
Quantization Type: Q4_K_M
Format: GGUF
Conversion Date: 2024-10-31
Framework: llama.cpp

Usage

This model can be used with llama.cpp. Here's how to use it:

# Basic usage
./llama-cli -m path_to_model.gguf -n 512 --prompt "Your prompt here"

# Chat format
./llama-cli -m path_to_model.gguf --temp 0.7 --repeat-penalty 1.2 -n 512 --prompt "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are Command-R, a helpful AI assistant.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Your prompt here<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"

Quantization Details

This model was quantized using the Q4_K_M format, which offers a good balance between model size and performance. The quantization was performed using llama.cpp's quantization tools.

Original model size: ~16GB Quantized model size: ~4.7GB

License

This model is released under the Apache 2.0 license.

Downloads last month: 5

GGUF

Model size

8B params

Architecture

command-r

Hardware compatibility

4-bit