Instructions to use sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF", dtype="auto")

llama-cpp-python

How to use sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF",
	filename="mistral-7b-instruct-v0.2-turkish.Q2_K.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 sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sayhan/Mistral-7B-Instruct-v0.2-turkish-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 sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sayhan/Mistral-7B-Instruct-v0.2-turkish-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 sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sayhan/Mistral-7B-Instruct-v0.2-turkish-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 sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF:Q4_K_M

Use Docker

docker model run hf.co/sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sayhan/Mistral-7B-Instruct-v0.2-turkish-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": "sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF:Q4_K_M

SGLang

How to use sayhan/Mistral-7B-Instruct-v0.2-turkish-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 "sayhan/Mistral-7B-Instruct-v0.2-turkish-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": "sayhan/Mistral-7B-Instruct-v0.2-turkish-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 "sayhan/Mistral-7B-Instruct-v0.2-turkish-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": "sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF with Ollama:
```
ollama run hf.co/sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF:Q4_K_M
```

Unsloth Studio

How to use sayhan/Mistral-7B-Instruct-v0.2-turkish-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 sayhan/Mistral-7B-Instruct-v0.2-turkish-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 sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF with Docker Model Runner:
```
docker model run hf.co/sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF:Q4_K_M
```

Lemonade

How to use sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-7B-Instruct-v0.2-turkish-GGUF-Q4_K_M

List all available models

lemonade list

Mistral 7B Instruct v0.2 Turkish

Model creator: malhajar
Original model: Mistral-7B-Instruct-v0.2-turkish

Description

This repo contains GGUF format model files for malhajar's Mistral 7B Instruct v0.2 Turkish

Original model

Developed by: Mohamad Alhajar
Language(s) (NLP): Turkish
Finetuned from model: mistralai/Mistral-7B-Instruct-v0.2

Quantization methods

quantization method	bits	size	use case	recommended
Q2_K	2	2.72 GB	smallest, significant quality loss	❌
Q3_K_S	3	3.16 GB	very small, high quality loss	❌
Q3_K_M	3	3.52 GB	very small, high quality loss	❌
Q3_K_L	3	3.82 GB	small, substantial quality loss	❌
Q4_0	4	4.11 GB	legacy; small, very high quality loss	❌
Q4_K_S	4	4.14 GB	small, greater quality loss	❌
Q4_K_M	4	4.37 GB	medium, balanced quality	✅
Q5_0	5	5.00 GB	legacy; medium, balanced quality	❌
Q5_K_S	5	5.00 GB	large, low quality loss	✅
Q5_K_M	5	5.13 GB	large, very low quality loss	✅
Q6_K	6	5.94 GB	very large, extremely low quality loss	❌
Q8_0	8	7.70 GB	very large, extremely low quality loss	❌
FP16	16	14.5 GB	enormous, minuscule quality loss	❌

Prompt Template

### Instruction:
<prompt> (without the <>)
### Response:

Downloads last month: 142

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF

Base model

malhajar/Mistral-7B-Instruct-v0.2-turkish

Quantized

(3)

this model

Collection including sayhan/Mistral-7B-Instruct-v0.2-turkish-GGUF

Turkish LLM GGUF Quantizations

Collection

GGUF format model files for Turkish-speaking large language models. • 3 items • Updated Feb 15, 2024 • 2