What are the hardware requirements for ESFT Vanilla Lite?

ESFT Vanilla Lite requires approximately 31.4 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed ESFT Vanilla Lite and how large is it?

ESFT Vanilla Lite is developed by deepseek-ai, a model with 15.7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate ESFT Vanilla Lite?

ESFT Vanilla Lite is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

ESFT Vanilla Lite by deepseek-ai — VRAM 31.4GB, 4K context

Name: ESFT Vanilla Lite
Author: deepseek-ai

ESFT Vanilla Lite is an open-source language model by deepseek-ai. Features: 15.7b LLM, VRAM: 31.4GB, Context: 4K, LLM Explorer Score: 0.13.

Arxiv:2407.01906 Conversational Custom code Deepseek v2 Endpoints compatible Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/deepseek-ai/ESFT-vanilla-lite

ESFT Vanilla Lite Parameters and Internals

Model Type

Expert-Specialized Fine-Tuning

Additional Notes

To use this model and specialized expert sets, refer to the scripts at the specified GitHub repository. Customized models used in the research can be found on Hugging Face.

LLM Name	ESFT Vanilla Lite
Repository 🤗	https://huggingface.co/deepseek-ai/ESFT-vanilla-lite
Model Size	15.7b
Required VRAM	31.4 GB
Updated	2026-08-05
Maintainer	deepseek-ai
Model Type	deepseek_v2
Model Files	8.6 GB: 1-of-4 8.6 GB: 2-of-4 8.6 GB: 3-of-4 5.6 GB: 4-of-4
Model Architecture	DeepseekV2ForCausalLM
Context Length	4096
Model Max Length	4096
Transformers Version	4.33.1
Tokenizer Class	LlamaTokenizerFast
Beginning of Sentence Token	<｜begin▁of▁sentence｜>
End of Sentence Token	<｜end▁of▁sentence｜>
Vocabulary Size	102400
Torch Data Type	bfloat16

Rank the ESFT Vanilla Lite capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to ESFT Vanilla Lite

Best Alternatives	Context / RAM	Downloads	Likes
...eepSeek Coder V2 Lite Instruct	160K / 31.4 GB	542062	628
DeepSeek V2 Lite Chat	160K / 31.4 GB	1354449	144
DeepSeek V2 Lite	160K / 31.4 GB	494498	183
...e Sft Math7k 1epoch 1e 4 Gamma	160K / 31.4 GB	11	1
Dsv2 Lite Coder Codeio Pp	160K / 31.4 GB	2	2
DeepSeek Coder V2 Lite Base	160K / 31.4 GB	5350	114
KwaiCoder DS V2 Lite Base	160K / 31.4 GB	37	7
...2 Lite Instruct FlashAttnPatch	160K / 31.4 GB	24	0
...eek Coder V2 Lite Instruct AWQ	160K / 9.1 GB	7853	11
LunarAI	160K / 31.4 GB	24	0

Note: green Score (e.g. "73.2") means that the model is better than deepseek-ai/ESFT-vanilla-lite.