---
title: ESFT-vanilla-lite
canonical_url: "https://www.modelscope.cn/models/deepseek-ai/ESFT-vanilla-lite"
md_url: "https://www.modelscope.cn/models/deepseek-ai/ESFT-vanilla-lite.md"
repository: deepseek-ai/ESFT-vanilla-lite
last_updated: 2024-11-21
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - deepseek_v2
architectures:
  - DeepseekV2ForCausalLM
parameters: 15.7B
tensor_type:
  - BF16
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: 1666
stars: 0
---

# ESFT-vanilla-lite

> ESFT-vanilla-lite - deepseek-ai 在 ModelScope 开源的模型。The vanilla model used in our Expert-Specialized Fine-Tuning (ESFT) research paper: https://arxiv.org/abs/2407.01906.

deepseek-ai/ESFT-vanilla-lite 是 ModelScope 魔搭社区上的 15.7B 参数text-generation模型，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: deepseek-ai/ESFT-vanilla-lite
- **Tasks**: text-generation
- **Parameters**: 15.7B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 1666
- **Stars**: 0
- **Last updated**: 2024-11-21

Source: https://www.modelscope.cn/models/deepseek-ai/ESFT-vanilla-lite

---

The vanilla model used in our Expert-Specialized Fine-Tuning (ESFT) research paper: https://arxiv.org/abs/2407.01906.

To use this model and specialized expert sets, please refer to the scripts at https://github.com/deepseek-ai/ESFT.

For the customized models used in this paper, please refer to https://huggingface.co/deepseek-ai/ESFT-{gate, token}-{task_name}-lite.