license: apache-2.0
language:
- en
datasets:
- allenai/dolma
tags:
- rene
- mamba
- cartesia
library_name: cartesia_pytorch
Model Card for Rene
Rene is a 1.3 billion-parameter language model trained by Cartesia. Rene has a hybrid architecture based on Mamba-2, with feedforward and sliding window attention layers interspersed. It uses the allenai/OLMo-1B-hf tokenizer. Rene was pretrained on 1.5 trillion tokens of the Dolma-1.7 dataset. For more details, see our blog post.
Usage
This is the PyTorch version of the model, and it's intended to run on CUDA devices. For use on Mac computers, please use the native MLX version instead.
Installation
The Rene model depends on the cartesia-pytorch package, which can be installed with pip as follows:
pip install --no-binary :all: cartesia-pytorch
Generation example
from cartesia_pytorch import ReneLMHeadModel
from transformers import AutoTokenizer
model = ReneLMHeadModel.from_pretrained("cartesia-ai/Rene-v0.1-1.3b-pytorch").half().cuda()
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-1B-hf")
in_message = ["Rene Descartes was"]
inputs = tokenizer(in_message, return_tensors="pt")
outputs = model.generate(inputs.input_ids.cuda(), max_length=50, top_k=100, top_p=0.99)
out_message = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
print(out_message)
# Example output: "Rene Descartes was a French mathematician, philosopher, and scientist. Descartes is famously credited for creating the Cartesian coordinate system: a 3 dimensional representation of points, vectors, and directions. This work is, for the most part" ...
Evaluation example
You can use our cartesia_lm_eval wrapper around the Language Model Evaluation Harness to evaluate our model on standard text benchmarks. Example command (clone this repo and run the below from within the cartesia-pytorch directory):
python -m evals.cartesia_lm_eval --model rene_ssm --model_args pretrained=cartesia-ai/Rene-v0.1-1.3b-pytorch,trust_remote_code=True --trust_remote_code --tasks copa,hellaswag,piqa,arc_easy,arc_challenge,winogrande,openbookqa --cache_requests true --batch_size auto:4 --output_path outputs/rene_evals/
Results on common benchmarks
| Model | Params (B) | Train Tokens | COPA | HellaSwag | MMLU (5-shot) | PIQA | ARC-e | ARC-c | WinoGrande | OpenBookQA | Average |
|---|---|---|---|---|---|---|---|---|---|---|---|
| allenai/OLMo-1B-hf | 1.2 | 3.0 | 82.0 | 62.9 | 26.2 | 75.1 | 57.4 | 31.1 | 60.0 | 36.2 | 53.9 |
| apple/OpenELM-1_1B | 1.1 | 1.5 | 81.0 | 64.8 | 27.1 | 75.6 | 55.4 | 32.3 | 61.9 | 36.2 | 54.3 |
| state-spaces/mamba2-1.3b | 1.3 | 0.3 | 82.0 | 60.0 | 25.8 | 73.7 | 64.2 | 33.3 | 61.0 | 37.8 | 54.7 |
| microsoft/phi-1_5 | 1.4 | 0.15 | 79.0 | 62.6 | 42.5 | 75.5 | 73.2 | 48.0 | 72.8 | 48.0 | 62.7 |
| Qwen/Qwen2-1.5B | 1.5 | 7.0 | 80.0 | 65.4 | 56.0 | 75.5 | 60.4 | 35.0 | 65.8 | 36.4 | 59.3 |
| RWKV/rwkv-6-world-1b6 | 1.6 | 1.1 | 84.0 | 58.3 | 25.9 | 73.5 | 56.7 | 34.1 | 60.0 | 37.4 | 53.7 |
| stabilityai/stablelm-2-1_6b | 1.6 | 4.0 | 86.0 | 69.0 | 38.1 | 76.7 | 68.1 | 38.9 | 63.6 | 38.8 | 59.9 |
| HuggingFaceTB/SmolLM-1.7B | 1.7 | 1.0 | 76.0 | 65.8 | 29.9 | 76.1 | 73.5 | 46.4 | 60.9 | 42.0 | 58.8 |
| h2oai/h2o-danube2-1.8b-base | 1.8 | 3.0 | 82.0 | 72.4 | 39.9 | 77.3 | 69.0 | 39.9 | 63.9 | 41.4 | 60.7 |
| google/recurrentgemma-2b | 2.7 | 2.0 | 62.0 | 61.8 | 32.3 | 68.8 | 46.4 | 29.9 | 57.1 | 29.0 | 48.4 |
| cognitivecomputations/TinyDolphin-2.8.1-1.1b | 1.1 | 71.0 | 59.9 | 25.7 | 73.1 | 55.8 | 33.0 | 59.7 | 36.6 | 51.9 | |
| cartesia-ai/Rene-v0.1-1.3b-pytorch (OUR MODEL) | 1.3 | 1.5 | 82.0 | 69.4 | 32.6 | 77.5 | 61.7 | 34.4 | 62.9 | 39.2 | 57.5 |
Bias, Risks, and Limitations
Rene is a pretrained base model which has not undergone any alignment or instruction tuning, and therefore does not have any moderation or safety guarantees. Users should implement appropriate guardrails and moderation mechanisms based on their particular needs in order to ensure responsible and ethical usage.
About Cartesia
At Cartesia, we're building real-time multimodal intelligence for every device.