Text Generation
Transformers
GGUF
PyTorch
TensorBoard
Safetensors
mistral
quantized
2-bit
3-bit
4-bit precision
5-bit
6-bit
8-bit precision
GGUF
axolotl
7b
Generated from Trainer
conversational
afr
amh
ara
aze
bel
ben
bul
cat
ceb
ces
cym
dan
deu
ell
eng
epo
est
eus
fin
fil
fra
fry
gla
gle
glg
guj
hat
hau
heb
hin
hun
hye
ibo
ind
isl
ita
jav
jpn
kan
kat
kaz
khm
kir
kor
kur
lao
lav
lat
lit
ltz
mal
mar
mkd
mlg
mlt
mon
mri
msa
mya
nep
nld
nor
nso
nya
ory
pan
pes
pol
por
pus
ron
rus
sin
slk
slv
smo
sna
snd
som
sot
spa
sqi
srp
sun
swa
swe
tam
tel
tgk
tha
tur
twi
ukr
urd
uzb
vie
xho
yid
yor
zho
zul
CohereForAI/aya_dataset
text-generation-inference
Instructions to use MaziyarPanahi/Mistral-7B-Instruct-Aya-101-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaziyarPanahi/Mistral-7B-Instruct-Aya-101-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MaziyarPanahi/Mistral-7B-Instruct-Aya-101-GGUF")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MaziyarPanahi/Mistral-7B-Instruct-Aya-101-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use MaziyarPanahi/Mistral-7B-Instruct-Aya-101-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 MaziyarPanahi/Mistral-7B-Instruct-Aya-101-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf MaziyarPanahi/Mistral-7B-Instruct-Aya-101-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 MaziyarPanahi/Mistral-7B-Instruct-Aya-101-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf MaziyarPanahi/Mistral-7B-Instruct-Aya-101-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 MaziyarPanahi/Mistral-7B-Instruct-Aya-101-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf MaziyarPanahi/Mistral-7B-Instruct-Aya-101-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 MaziyarPanahi/Mistral-7B-Instruct-Aya-101-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf MaziyarPanahi/Mistral-7B-Instruct-Aya-101-GGUF:Q4_K_M
Use Docker
docker model run hf.co/MaziyarPanahi/Mistral-7B-Instruct-Aya-101-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use MaziyarPanahi/Mistral-7B-Instruct-Aya-101-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MaziyarPanahi/Mistral-7B-Instruct-Aya-101-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": "MaziyarPanahi/Mistral-7B-Instruct-Aya-101-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/MaziyarPanahi/Mistral-7B-Instruct-Aya-101-GGUF:Q4_K_M
- SGLang
How to use MaziyarPanahi/Mistral-7B-Instruct-Aya-101-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 "MaziyarPanahi/Mistral-7B-Instruct-Aya-101-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": "MaziyarPanahi/Mistral-7B-Instruct-Aya-101-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 "MaziyarPanahi/Mistral-7B-Instruct-Aya-101-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": "MaziyarPanahi/Mistral-7B-Instruct-Aya-101-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use MaziyarPanahi/Mistral-7B-Instruct-Aya-101-GGUF with Ollama:
ollama run hf.co/MaziyarPanahi/Mistral-7B-Instruct-Aya-101-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use MaziyarPanahi/Mistral-7B-Instruct-Aya-101-GGUF with Docker Model Runner:
docker model run hf.co/MaziyarPanahi/Mistral-7B-Instruct-Aya-101-GGUF:Q4_K_M
- Lemonade
How to use MaziyarPanahi/Mistral-7B-Instruct-Aya-101-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MaziyarPanahi/Mistral-7B-Instruct-Aya-101-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Mistral-7B-Instruct-Aya-101-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat