---
title: Llama-3.1-Arctic-ExCoT-70B
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/Llama-3.1-Arctic-ExCoT-70B"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/Llama-3.1-Arctic-ExCoT-70B.md"
repository: AI-ModelScope/Llama-3.1-Arctic-ExCoT-70B
last_updated: 2025-04-02
license: cc-by-nc-4.0
model_type:
  - llama
architectures:
  - LlamaForCausalLM
parameters: 70.6B
tensor_type:
  - F32
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 472
stars: 1
---

# Llama-3.1-Arctic-ExCoT-70B

> Llama-3.1-Arctic-ExCoT-70B - AI-ModelScope 在 ModelScope 开源的模型。Arctic Text2SQL: ExCoT

AI-ModelScope/Llama-3.1-Arctic-ExCoT-70B 是 ModelScope 魔搭社区上的 70.6B 参数机器学习模型，采用 cc-by-nc-4.0 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: AI-ModelScope/Llama-3.1-Arctic-ExCoT-70B
- **License**: cc-by-nc-4.0
- **Parameters**: 70.6B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 472
- **Stars**: 1
- **Last updated**: 2025-04-02

Source: https://www.modelscope.cn/models/AI-ModelScope/Llama-3.1-Arctic-ExCoT-70B

---

# Arctic Text2SQL: ExCoT

Snowflake’s AI research team introduces ExCoT, the first model in the Arctic Text2SQL family. ExCoT is a novel framework that combines CoT prompting with SQL execution-based DPO, using execution results — not human preferences — as the feedback signal. This enables scalable, high-quality model optimization without requiring expensive human annotations.

Based on our internal testing, ExCoT delivered state-of-the-art results on the [BIRD-test benchmark](https://bird-bench.github.io/), achieving best-in-class performance in the single-model, single-inference category using only public datasets (BIRD and Spider) and no additional Text2SQL data:

* [Llama-3.1-Arctic-ExCoT-70B](https://huggingface.co/Snowflake/Llama-3.1-Arctic-ExCoT-70B) improved execution accuracy on the BIRD-dev set from the base model’s 57.37% to 68.51%. [Qwen-2.5-coder-Arctic-ExCoT-32B](https://huggingface.co/Snowflake/Qwen-2.5-coder-Arctic-ExCoT-32B) achieved similarly strong gains.

* Both models significantly outperformed other well-known frontier general-purpose models, achieving over 10 points of improvement.

For more details about ExCoT and how to use it:

* ❄️ [Arctic Text2SQL: Introducing ExCoT for Execution-Guided Chain-of-Thought Optimization (blog)]()
* 📝 [ExCoT: Optimizing Reasoning for Text-to-SQL with Execution Feedback (arxiv)](https://arxiv.org/pdf/2503.19988)
* 🚀 [Getting started guide using ArcticTraining](https://github.com/snowflakedb/ArcticTraining/tree/main/projects/excot_dpo)

## Evaluation results

| Model                                 |  |              |
|--------------------------------------|----------------|--------------|
|                                      | BIRD Ex% Dev        | BIRD Ex% Test     |
| Arctic-ExCoT-70B (LLaMA 3.1 70B)      | **68.51** | 68.53    |
| Arctic-ExCoT-32B (Qwen-2.5-Coder 32B) | 68.25   | 68.19    |
| XiYanSQL-QwenCoder*                   | 67.01   | **69.03** |
| OpenAI GPT-4o                         | 54.04   | –        |
| OpenAI GPT-4                          | 46.35   | 54.89    |
| Anthropic Claude 3.5-Sonnet          | 50.13   | –        |
| Claude-2                              | 42.70   | 49.02    |
| OpenAI o1-mini                        | 52.41   | –        |
| OpenAI o3-mini                        | 53.72   | –        |
| Mistral-large-2407 (123B)             | 53.52   | 55.84    |
| DeepSeek-V2 (236B)                    | 56.13   | 56.68    |

Top Single-Model, Single-Inference Results on the BIRD Leaderboard (as of March 25, 2025). *XiYanSQL-QwenCoder: there are some challenges to reproduce the numbers [[1]](https://github.com/XGenerationLab/XiYanSQL-QwenCoder/issues/4)[[2]](https://modelscope.cn/models/XGenerationLab/XiYanSQL-QwenCoder-32B-2412/feedback/issueDetail/22708).