Text Generation
Transformers
ONNX
Safetensors
Turkish
qwen3
turkish
türkçe
turkish-language-model
turkish-llm
turkish-slm
small-language-model
language-model
causal-lm
decoder-only
pretrained
pretraining
from-scratch
trained-from-scratch
chat
instruction-following
turkish-nlp
natural-language-processing
conversational
text-generation-inference
Instructions to use TozAI/Toz-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TozAI/Toz-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TozAI/Toz-1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TozAI/Toz-1") model = AutoModelForCausalLM.from_pretrained("TozAI/Toz-1", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TozAI/Toz-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TozAI/Toz-1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TozAI/Toz-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/TozAI/Toz-1
- SGLang
How to use TozAI/Toz-1 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 "TozAI/Toz-1" \ --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": "TozAI/Toz-1", "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 "TozAI/Toz-1" \ --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": "TozAI/Toz-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use TozAI/Toz-1 with Docker Model Runner:
docker model run hf.co/TozAI/Toz-1
Download tokenizer_config.json from TozAI/Toz-1: direct link, hf CLI and curl.
- Browser
- Download file 616 Bytes
-
https://huggingface.co/TozAI/Toz-1/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://TozAI/Toz-1/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/TozAI/Toz-1/resolve/main/tokenizer_config.json
616 Bytes
| { | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|endoftext|>", | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 1024, | |
| "pad_token": "<|endoftext|>", | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "chat_template": "{%- for m in messages -%}{%- if m['role'] == 'system' -%}{{- m['content'] + '\\n' -}}{%- elif m['role'] == 'user' -%}{{- 'Kullanıcı: ' + m['content'] + '\\n' -}}{%- elif m['role'] == 'assistant' -%}{{- 'Töz: ' + m['content'] + eos_token -}}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{- 'Töz: ' -}}{%- endif -%}" | |
| } |