Instructions to use Voicelab/trurl-2-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Voicelab/trurl-2-13b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Voicelab/trurl-2-13b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Voicelab/trurl-2-13b") model = AutoModelForCausalLM.from_pretrained("Voicelab/trurl-2-13b", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Voicelab/trurl-2-13b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Voicelab/trurl-2-13b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Voicelab/trurl-2-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Voicelab/trurl-2-13b
- SGLang
How to use Voicelab/trurl-2-13b 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 "Voicelab/trurl-2-13b" \ --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": "Voicelab/trurl-2-13b", "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 "Voicelab/trurl-2-13b" \ --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": "Voicelab/trurl-2-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Voicelab/trurl-2-13b with Docker Model Runner:
docker model run hf.co/Voicelab/trurl-2-13b
MMLU
Just curious, does the training dataset includes the MMLU dataset which is used during evaluation? If so, is it still fair to use MMLU as a metric to evaluate this model? Because compared with other 13B(or maybe 30B) models, the MMLU metric is very high.
Hi,
Yes, our model was trained on MMLU* as we stated in the readme description and results table we provided. This model was supposed to be our private one, however, it was decided by the company to release it too. Our main goal was an improvement of the model in Polish, and our private evaluation didn't include any of the leaderboard datasets, as we focused on testing the model in Polish. Originally, we planned to release the model trained without the MMLU, but the training is still in progress. We will release it as soon as it ends, but it will take another few weeks. Our 7b model shows results without MMLU.
We didn't have any plans of evaluating trurl-13b on MMLU in the leaderboard, unfortunately, anyone can send the model for evaluation.
Since it is already in the leaderboard we will send on Monday the request to the HF leaderboard to remove our 13b model from the MMLU leaderboard.
I hope this answers your question.
*It was only a part of MMLU, modified to only text (no ABCD answers) but still MMLU was in the training set.
Hello,
Just a quick update. Our model have been excluded from the leaderboard and we asked if it's possible to add a new parameter to the model card that would block submitting the model to the leaderboard so similar situation won't happen (third party submitting it again).
We will release the model trained without MMLU when the training will end, and this one will be submitted to the leaderboard by us. However, we guess that results will not be much higher than Llama 2 (if not slightly lower) as the model was trained to work better on Polish, especially on business-related tasks, and no effort was made towards beating other models on HF benchmarks.
Cheers
Just a quick update. The new Trurl is available here: https://huggingface.co/Voicelab/trurl-2-13b-academic
