[!IMPORTANT] This model is an instruction tuned model which requires alignment before it can be used in production. We will release the chat version soon.

Fox-1 is a decoder-only transformer-based small language model (SLM) with 1.6B total parameters developed by TensorOpera AI. The model was pre-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.

Fox-1-Instruct-v0.1 is an instruction-tuned (SFT) version of Fox-1-1.6B that has an 8K native context length. The model was finetuned with 5B tokens of instruction following and multi-turn conversation data.

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

Getting-Started

The model and a live inference endpoint are available on the TensorOpera AI Platform.

For detailed deployment instructions, refer to the Step-by-Step Guide on how to deploy Fox-1-Instruct on the TensorOpera AI Platform.

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-Instruct-v0.1	Fox-1-1.6B	Qwen1.5-1.8B-Chat	Gemma-2B-It	OpenELM-1.1B-Instruct
GSM8k	39.20%	36.39%	18.20%	4.47%	0.91%
MMLU	44.99%	43.05%	45.77%	37.70%	25.70%
ARC Challenge	43.60%	41.21%	38.99%	43.34%	40.36%
HellaSwag	63.39%	62.82%	60.31%	62.72%	71.67%
TruthfulQA	44.12%	38.66%	40.57%	45.86%	45.96%
Winogrande	62.67%	60.62%	59.51%	61.33%	61.96%
Average	49.66%	47.13%	43.89%	42.57%	41.09%

tensoropera/Fox-1-1.6B-Instruct-v0.1

Model Card for Fox-1-1.6B-Instruct

Getting-Started

Benchmarks

tensoropera/
Fox-1-1.6B-Instruct-v0.1