Instructions to use umarigan/LLama-3-8B-Instruction-tr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use umarigan/LLama-3-8B-Instruction-tr with Transformers:

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

pipe = pipeline("text-generation", model="umarigan/LLama-3-8B-Instruction-tr")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("umarigan/LLama-3-8B-Instruction-tr")
model = AutoModelForCausalLM.from_pretrained("umarigan/LLama-3-8B-Instruction-tr")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use umarigan/LLama-3-8B-Instruction-tr with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "umarigan/LLama-3-8B-Instruction-tr"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "umarigan/LLama-3-8B-Instruction-tr",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/umarigan/LLama-3-8B-Instruction-tr

SGLang

How to use umarigan/LLama-3-8B-Instruction-tr 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 "umarigan/LLama-3-8B-Instruction-tr" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "umarigan/LLama-3-8B-Instruction-tr",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "umarigan/LLama-3-8B-Instruction-tr" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "umarigan/LLama-3-8B-Instruction-tr",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use umarigan/LLama-3-8B-Instruction-tr 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 umarigan/LLama-3-8B-Instruction-tr 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 umarigan/LLama-3-8B-Instruction-tr to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for umarigan/LLama-3-8B-Instruction-tr to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="umarigan/LLama-3-8B-Instruction-tr",
    max_seq_length=2048,
)

Docker Model Runner
How to use umarigan/LLama-3-8B-Instruction-tr with Docker Model Runner:
```
docker model run hf.co/umarigan/LLama-3-8B-Instruction-tr
```

Uploaded model

Developed by: umarigan
License: apache-2.0
Finetuned from model : unsloth/llama-3-8b-bnb-4bit

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Usage Examples


# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("umarigan/LLama-3-8B-Instruction-tr")
model = AutoModelForCausalLM.from_pretrained("umarigan/LLama-3-8B-Instruction-tr")
alpaca_prompt = """
Görev:
{}

Girdi:
{}

Cevap:
{}"""

inputs = tokenizer(
[
    alpaca_prompt.format(
        "bir haftada 3 kilo verebileceğim 5 öneri sunabilir misin?", # Görev
        "", # Girdi
        "", # Cevap - boş bırakın!
    )
], return_tensors = "pt")
outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
tokenizer.batch_decode(outputs)

Output:
<|begin_of_text|> Görev: bir haftada 3 kilo verebileceğim 5 öneri sunabilir misin?

Girdi:

Cevap:

1. Yemeklerinizde daha az tuz kullanın. 2. Daha fazla sebze ve meyve tüketin. 3. Daha fazla su için. 4. Daha fazla egzersiz yapın. 5. Daha fazla uyku alın.<|end_of_text|>

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