DeepSeek-V2-Lite-IMat-GGUF Open Source Model - Reduce resource requirements and facilitate free deployment and application

Deepseek V2 Lite IMat GGUF

Developed by legraphista

The GGUF quantized version of DeepSeek-V2-Lite, processed by Llama.cpp imatrix quantization, reduces storage and computing resource requirements and facilitates deployment.

Large Language Model #Efficient quantization #Multi-precision adaptation #Lightweight deployment

Downloads 491

Release Time : 5/26/2024

Model Overview

This model is a quantized version of DeepSeek-V2-Lite, suitable for efficient inference on resource-constrained devices.

Model Features

Efficient quantization

Through Llama.cpp imatrix quantization processing, significantly reduces the model size and computing resource requirements.

Multiple quantization options

Provides multiple quantization levels (such as Q8_0, Q6_K, Q4_K, etc.) to adapt to different hardware requirements.

Easy to deploy

Supports running on multiple devices and is suitable for local inference.

Model Capabilities

Text generation

Efficient inference

Use Cases

Local inference

Text generation

Run the model on a local device to generate text.

Efficiently generate text, suitable for resource-constrained environments.

🚀 DeepSeek-V2-Lite-IMat-GGUF

This is a llama.cpp imatrix quantization of deepseek-ai/DeepSeek-V2-Lite, offering a more efficient way to use the model.

Original Model: deepseek-ai/DeepSeek-V2-Lite
Original dtype: BF16 (bfloat16)
Quantized by: llama.cpp https://github.com/ggerganov/llama.cpp/pull/7519
IMatrix dataset: here

📦 Files

IMatrix

Status: ✅ Available
Link: here

Common Quants

Filename	Quant type	File Size	Status	Uses IMatrix	Is Split
DeepSeek-V2-Lite.Q8_0.gguf	Q8_0	16.70GB	✅ Available	❌ No	📦 No
DeepSeek-V2-Lite.Q6_K.gguf	Q6_K	14.07GB	✅ Available	❌ No	📦 No
DeepSeek-V2-Lite.Q4_K.gguf	Q4_K	10.36GB	✅ Available	✅ Yes	📦 No
DeepSeek-V2-Lite.Q3_K.gguf	Q3_K	8.13GB	✅ Available	✅ Yes	📦 No
DeepSeek-V2-Lite.Q2_K.gguf	Q2_K	6.43GB	✅ Available	✅ Yes	📦 No

All Quants

Filename	Quant type	File Size	Status	Uses IMatrix	Is Split
DeepSeek-V2-Lite.FP16.gguf	F16	31.42GB	✅ Available	❌ No	📦 No
DeepSeek-V2-Lite.BF16.gguf	BF16	31.42GB	✅ Available	❌ No	📦 No
DeepSeek-V2-Lite.Q5_K.gguf	Q5_K	11.85GB	✅ Available	❌ No	📦 No
DeepSeek-V2-Lite.Q5_K_S.gguf	Q5_K_S	11.14GB	✅ Available	❌ No	📦 No
DeepSeek-V2-Lite.Q4_K_S.gguf	Q4_K_S	9.53GB	✅ Available	✅ Yes	📦 No
DeepSeek-V2-Lite.Q3_K_L.gguf	Q3_K_L	8.46GB	✅ Available	✅ Yes	📦 No
DeepSeek-V2-Lite.Q3_K_S.gguf	Q3_K_S	7.49GB	✅ Available	✅ Yes	📦 No
DeepSeek-V2-Lite.Q2_K_S.gguf	Q2_K_S	6.46GB	✅ Available	✅ Yes	📦 No
DeepSeek-V2-Lite.IQ4_NL.gguf	IQ4_NL	8.91GB	✅ Available	✅ Yes	📦 No
DeepSeek-V2-Lite.IQ4_XS.gguf	IQ4_XS	8.57GB	✅ Available	✅ Yes	📦 No
DeepSeek-V2-Lite.IQ3_M.gguf	IQ3_M	7.55GB	✅ Available	✅ Yes	📦 No
DeepSeek-V2-Lite.IQ3_S.gguf	IQ3_S	7.49GB	✅ Available	✅ Yes	📦 No
DeepSeek-V2-Lite.IQ3_XS.gguf	IQ3_XS	7.12GB	✅ Available	✅ Yes	📦 No
DeepSeek-V2-Lite.IQ3_XXS.gguf	IQ3_XXS	6.96GB	✅ Available	✅ Yes	📦 No
DeepSeek-V2-Lite.IQ2_M.gguf	IQ2_M	6.33GB	✅ Available	✅ Yes	📦 No
DeepSeek-V2-Lite.IQ2_S.gguf	IQ2_S	6.01GB	✅ Available	✅ Yes	📦 No
DeepSeek-V2-Lite.IQ2_XS.gguf	IQ2_XS	5.97GB	✅ Available	✅ Yes	📦 No
DeepSeek-V2-Lite.IQ2_XXS.gguf	IQ2_XXS	5.64GB	✅ Available	✅ Yes	📦 No
DeepSeek-V2-Lite.IQ1_M.gguf	IQ1_M	5.24GB	✅ Available	✅ Yes	📦 No
DeepSeek-V2-Lite.IQ1_S.gguf	IQ1_S	4.99GB	✅ Available	✅ Yes	📦 No

📥 Downloading using huggingface-cli

If you do not have huggingface-cli installed:

pip install -U "huggingface_hub[cli]"

Download the specific file you want:

huggingface-cli download legraphista/DeepSeek-V2-Lite-IMat-GGUF --include "DeepSeek-V2-Lite.Q8_0.gguf" --local-dir ./

If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run:

huggingface-cli download legraphista/DeepSeek-V2-Lite-IMat-GGUF --include "DeepSeek-V2-Lite.Q8_0/*" --local-dir DeepSeek-V2-Lite.Q8_0
# see FAQ for merging GGUF's

💻 Inference

Llama.cpp

llama.cpp/main -m DeepSeek-V2-Lite.Q8_0.gguf --color -i -p "prompt here"

❓ FAQ

Why is the IMatrix not applied everywhere?

According to this investigation, it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results).

How do I merge a split GGUF?

Make sure you have gguf-split available
- To get hold of gguf-split, navigate to https://github.com/ggerganov/llama.cpp/releases
- Download the appropriate zip for your system from the latest release
- Unzip the archive and you should be able to find gguf-split
Locate your GGUF chunks folder (ex: DeepSeek-V2-Lite.Q8_0)
Run gguf-split --merge DeepSeek-V2-Lite.Q8_0/DeepSeek-V2-Lite.Q8_0-00001-of-XXXXX.gguf DeepSeek-V2-Lite.Q8_0.gguf
- Make sure to point gguf-split to the first chunk of the split.

Got a suggestion? Ping me @legraphista!

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご