[!IMPORTANT]
This model is a base pretrained model which requires further finetuning for most use cases. For a more interactive experience, we recommend tensoropera/Fox-1-1.6B-Instruct-v0.1, the instruction-tuned version of Fox-1.

Fox-1 is a decoder-only transformer-based small language model (SLM) with 1.6B total parameters developed by TensorOpera AI. The model was trained with a 3-stage data curriculum on 3 trillion tokens of text and code data in 8K sequence length. Fox-1 uses Grouped Query Attention (GQA) with 4 key-value heads and 16 attention heads for faster inference.

For the full details of this model please read Fox-1 technical report and release blog post.

Benchmarks

We evaluated Fox-1 on ARC Challenge (25-shot), HellaSwag (10-shot), TruthfulQA (0-shot), MMLU (5-shot), Winogrande (5-shot), and GSM8k (5-shot). We follow the Open LLM Leaderboard's evaluation setup and report the average score of the 6 benchmarks. The model was evaluated on a machine with 8*H100 GPUs.

	Fox-1-1.6B	Qwen-1.5-1.8B	Gemma-2B	StableLM-2-1.6B	OpenELM-1.1B
GSM8k	36.39%	34.04%	17.06%	17.74%	2.27%
MMLU	43.05%	47.15%	41.71%	39.16%	27.28%
ARC Challenge	41.21%	37.20%	49.23%	44.11%	36.26%
HellaSwag	62.82%	61.55%	71.60%	70.46%	65.23%
TruthfulQA	38.66%	39.37%	33.05%	38.77%	36.98%
Winogrande	60.62%	65.51%	65.51%	65.27%	61.64%
Average	47.13%	46.81%	46.36%	45.92%	38.28%

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	7.69
IFEval (0-Shot)	27.66
BBH (3-Shot)	7.40
MATH Lvl 5 (4-Shot)	1.28
GPQA (0-shot)	1.79
MuSR (0-shot)	3.87
MMLU-PRO (5-shot)	4.13