What are the hardware requirements for DeepSeek Coder V2 Lite Instruct FlashAttnPatch?

DeepSeek Coder V2 Lite Instruct FlashAttnPatch requires approximately 31.4 GB of VRAM and supports a context window of 160K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed DeepSeek Coder V2 Lite Instruct FlashAttnPatch and how large is it?

DeepSeek Coder V2 Lite Instruct FlashAttnPatch is developed by BirdL, 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 DeepSeek Coder V2 Lite Instruct FlashAttnPatch?

DeepSeek Coder V2 Lite Instruct FlashAttnPatch is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

DeepSeek Coder V2 Lite Instruct FlashAttnPatch by BirdL — VRAM 31.4GB, 160K context

Name: DeepSeek Coder V2 Lite Instruct FlashAttnPatch
Author: BirdL

DeepSeek Coder V2 Lite Instruct FlashAttnPatch is an open-source language model by BirdL. Features: 15.7b LLM, VRAM: 31.4GB, Context: 160K, License: other, Instruction-Based, Code Generating, LLM Explorer Score: 0.13.

Arxiv:2401.06066 Codegen Conversational Custom code Deepseek v2 Endpoints compatible Instruct Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/BirdL/DeepSeek-Coder-V2-Lite-Instruct-FlashAttnPatch

DeepSeek Coder V2 Lite Instruct FlashAttnPatch Parameters and Internals

Model Type

Mixture-of-Experts, Code Intelligence

Use Cases

Areas:

Code Intelligence

Primary Use Cases:

Coding and mathematical reasoning tasks

Additional Notes

DeepSeek-Coder-V2 expedites advancements in code-related tasks and supports a wider range of programming languages.

Supported Languages

languages_supported (338 programming languages)

Training Details

Data Volume:

6 trillion tokens

Methodology:

Continued pre-training from an intermediate checkpoint

Context Length:

128000

Model Architecture:

Mixture-of-Experts (MoE)

Input Output

Accepted Modalities:

Text

Output Format:

Text

LLM Name	DeepSeek Coder V2 Lite Instruct FlashAttnPatch
Repository 🤗	https://huggingface.co/BirdL/DeepSeek-Coder-V2-Lite-Instruct-FlashAttnPatch
Model Size	15.7b
Required VRAM	31.4 GB
Updated	2026-08-25
Maintainer	BirdL
Model Type	deepseek_v2
Instruction-Based	Yes
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
Generates Code	Yes
Model Architecture	DeepseekV2ForCausalLM
License	other
Context Length	163840
Model Max Length	163840
Transformers Version	4.39.3
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 DeepSeek Coder V2 Lite Instruct FlashAttnPatch 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 DeepSeek Coder V2 Lite Instruct FlashAttnPatch

Best Alternatives	Context / RAM	Downloads	Likes
...eepSeek Coder V2 Lite Instruct	160K / 31.4 GB	542062	628
...eek Coder V2 Lite Instruct AWQ	160K / 9.1 GB	7853	11

Note: green Score (e.g. "73.2") means that the model is better than BirdL/DeepSeek-Coder-V2-Lite-Instruct-FlashAttnPatch.