Instructions to use JuLsez4R/Llama-turkish-lawbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JuLsez4R/Llama-turkish-lawbot with Transformers:

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

pipe = pipeline("text-generation", model="JuLsez4R/Llama-turkish-lawbot")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JuLsez4R/Llama-turkish-lawbot")
model = AutoModelForCausalLM.from_pretrained("JuLsez4R/Llama-turkish-lawbot", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JuLsez4R/Llama-turkish-lawbot with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JuLsez4R/Llama-turkish-lawbot"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JuLsez4R/Llama-turkish-lawbot",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/JuLsez4R/Llama-turkish-lawbot

SGLang

How to use JuLsez4R/Llama-turkish-lawbot 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 "JuLsez4R/Llama-turkish-lawbot" \
    --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": "JuLsez4R/Llama-turkish-lawbot",
		"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 "JuLsez4R/Llama-turkish-lawbot" \
        --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": "JuLsez4R/Llama-turkish-lawbot",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use JuLsez4R/Llama-turkish-lawbot with Docker Model Runner:
```
docker model run hf.co/JuLsez4R/Llama-turkish-lawbot
```

results

This model is a fine-tuned version of meta-llama/Llama-3.2-3B-Instruct on Renicames/turkish-law-chatbot dataset, designed to generate responses based on Turkish legal questions and answers.

Model description

This model is fine-tuned to imporove its ability to generate responses for a Turkish law chatbot. meta-llama/Llama-3.2-3B-Instruct was used as the base model, which can pretty much handle various tasks. Just specialized it with the dataset of Renicames/turkish-law-chatbot.

Intended uses & limitations

Intended uses

This model can be deployed in applications aimed at providing legal information or answering legal questions in Turkish.
Useful for generating legal text, such as explanations of laws or providing examples of legal processes in Turkish.

Limitaions

While the model has been fine-tuned on the Turkish legal domain, it may still lack the depth and specificity required for complex legal inquiries. It might not be suitable for professional legal advice.
As with any AI model, it may reflect biases found in the training data, so it should be used with caution in critical applications.
This model is focused on the Turkish language, so it may not perform well in other languages or mixed-language queries.

Training and evaluation data

Training data details:

Source: Renicames/turkish-law-chatbot dataset
Languages: Turkish
Content: Legal questions and answers
Size: The dataset consists of several thousand question-answer pairs related to Turkish law.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 16
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

Downloads last month: 19

Safetensors

Model size

3B params

Tensor type

F32

Model tree for JuLsez4R/Llama-turkish-lawbot

Base model

meta-llama/Llama-3.2-3B-Instruct

Finetuned

(2016)

this model

JuLsez4R
/

Llama-turkish-lawbot