Text Generation
Transformers
Safetensors
Tatar
llama
tatar
turkic
qypchaq
from-scratch
literary
base-model
text-generation-inference
Instructions to use yasalma/TATlit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yasalma/TATlit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="yasalma/TATlit")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("yasalma/TATlit") model = AutoModelForCausalLM.from_pretrained("yasalma/TATlit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use yasalma/TATlit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "yasalma/TATlit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yasalma/TATlit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/yasalma/TATlit
- SGLang
How to use yasalma/TATlit 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 "yasalma/TATlit" \ --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": "yasalma/TATlit", "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 "yasalma/TATlit" \ --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": "yasalma/TATlit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use yasalma/TATlit with Docker Model Runner:
docker model run hf.co/yasalma/TATlit
Add benchmark tables and link TatBLiMP to its dataset
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by ilchats - opened
README.md
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The training corpus is withheld on stewardship grounds for a minoritized language's literary heritage; a full datasheet with per-document provenance is available on reasonable request. The tokenizer and the model weights are released.
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## License
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The model weights and the tokenizer are released under **CC BY-NC-SA 4.0**. The accompanying TatBLiMP benchmark is released under **CC BY-NC 4.0**. The training corpus is not released.
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## Citation
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The training corpus is withheld on stewardship grounds for a minoritized language's literary heritage; a full datasheet with per-document provenance is available on reasonable request. The tokenizer and the model weights are released.
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## License
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The model weights and the tokenizer are released under **CC BY-NC-SA 4.0**. The accompanying [TatBLiMP](https://huggingface.co/datasets/yasalma/TatBLiMP) benchmark is released under **CC BY-NC 4.0**. The training corpus is not released.
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## Citation
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