Update README.md

README.md

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----
-license: apache-2.0
-language:
-- en
----
+---
+license: apache-2.0
+language:
+  - en
+---
+
+## Model Card for Fox-1-1.6B-Instruct
+
+> [!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](https://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
+our [release blog post](https://blog.tensoropera.ai/tensoropera-unveils-fox-foundation-model-a-pioneering-open-source-slm-leading-the-way-against-tech-giants).
+
+## 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%                |
+