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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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 Desktop
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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