Instructions to use duyntnet/aya-expanse-8b-imatrix-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use duyntnet/aya-expanse-8b-imatrix-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="duyntnet/aya-expanse-8b-imatrix-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("duyntnet/aya-expanse-8b-imatrix-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use duyntnet/aya-expanse-8b-imatrix-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 duyntnet/aya-expanse-8b-imatrix-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf duyntnet/aya-expanse-8b-imatrix-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 duyntnet/aya-expanse-8b-imatrix-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf duyntnet/aya-expanse-8b-imatrix-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 duyntnet/aya-expanse-8b-imatrix-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf duyntnet/aya-expanse-8b-imatrix-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 duyntnet/aya-expanse-8b-imatrix-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf duyntnet/aya-expanse-8b-imatrix-GGUF:Q4_K_M
Use Docker
docker model run hf.co/duyntnet/aya-expanse-8b-imatrix-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use duyntnet/aya-expanse-8b-imatrix-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "duyntnet/aya-expanse-8b-imatrix-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": "duyntnet/aya-expanse-8b-imatrix-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/duyntnet/aya-expanse-8b-imatrix-GGUF:Q4_K_M
- SGLang
How to use duyntnet/aya-expanse-8b-imatrix-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "duyntnet/aya-expanse-8b-imatrix-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "duyntnet/aya-expanse-8b-imatrix-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "duyntnet/aya-expanse-8b-imatrix-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "duyntnet/aya-expanse-8b-imatrix-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use duyntnet/aya-expanse-8b-imatrix-GGUF with Ollama:
ollama run hf.co/duyntnet/aya-expanse-8b-imatrix-GGUF:Q4_K_M
- Unsloth Studio
How to use duyntnet/aya-expanse-8b-imatrix-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 duyntnet/aya-expanse-8b-imatrix-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 duyntnet/aya-expanse-8b-imatrix-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for duyntnet/aya-expanse-8b-imatrix-GGUF to start chatting
- Docker Model Runner
How to use duyntnet/aya-expanse-8b-imatrix-GGUF with Docker Model Runner:
docker model run hf.co/duyntnet/aya-expanse-8b-imatrix-GGUF:Q4_K_M
- Lemonade
How to use duyntnet/aya-expanse-8b-imatrix-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull duyntnet/aya-expanse-8b-imatrix-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.aya-expanse-8b-imatrix-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Quantizations of https://huggingface.co/CohereForAI/aya-expanse-8b
Inference Clients/UIs
From original readme
Aya Expanse is an open-weight research release of a model with highly advanced multilingual capabilities. It focuses on pairing a highly performant pre-trained Command family of models with the result of a year’s dedicated research from Cohere For AI, including data arbitrage, multilingual preference training, safety tuning, and model merging. The result is a powerful multilingual large language model serving 23 languages.
We cover 23 languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese
This model card corresponds to the 8-billion version of the Aya Expanse model. We also released an 32-billion version which you can find here.
- Developed by: Cohere For AI
- Point of Contact: Cohere For AI: cohere.for.ai
- License: CC-BY-NC, requires also adhering to C4AI's Acceptable Use Policy
- Model: Aya Expanse 8B
- Model Size: 8 billion parameters
Try Aya Expanse
Before downloading the weights, you can try out Aya Expanse in our hosted Hugging Face Space.
Usage
Please install transformers from the source repository.
# pip install 'git+https://github.com/huggingface/transformers.git'
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "CohereForAI/aya-expanse-8b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# Format the message with the chat template
messages = [{"role": "user", "content": "Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz"}]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
gen_tokens = model.generate(
input_ids,
max_new_tokens=100,
do_sample=True,
temperature=0.3,
)
gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)
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docker model run hf.co/duyntnet/aya-expanse-8b-imatrix-GGUF: