Instructions to use matrixportalx/TR-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use matrixportalx/TR-V1 with Transformers:

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

pipe = pipeline("text-generation", model="matrixportalx/TR-V1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("matrixportalx/TR-V1")
model = AutoModelForCausalLM.from_pretrained("matrixportalx/TR-V1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use matrixportalx/TR-V1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "matrixportalx/TR-V1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "matrixportalx/TR-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/matrixportalx/TR-V1

SGLang

How to use matrixportalx/TR-V1 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 "matrixportalx/TR-V1" \
    --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": "matrixportalx/TR-V1",
		"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 "matrixportalx/TR-V1" \
        --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": "matrixportalx/TR-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use matrixportalx/TR-V1 with Docker Model Runner:
```
docker model run hf.co/matrixportalx/TR-V1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

TR-V1

Model Açıklaması:

Bu model, matrixportal/TR tabanlı olarak aşağıdaki veri set(ler)iyle Türkçe dili ve kültürüne yönelik olarak LoRA yöntemiyle ince ayar uygulanarak geliştirilmiştir:

cenfis/alpaca-turkish-combined

Bu eğitim ile modelin Türkçe dilinde daha doğal, bağlama duyarlı ve etkili yanıtlar üretebilmesi hedeflenmiştir. Çalışma, açık kaynak topluluğuna katkı sağlamayı ve Türkçe doğal dil işleme alanında gelişimi desteklemeyi amaçlamaktadır.