Downloads · 30 days
103
8% of all-time downloads
grapevine-AI/Qwen2-57B-A14B-Instruct-GGUF
Qwen2-57B-A14B-Instruct-GGUF is a machine learning model from grapevine-AI. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
This is GGUF of Qwen2-57B-A14B-Instruct.<br I think this is the world's first successful example to make Qwen2-57B-A14B-Instruct's gguf with imatrix.
Downloads · 30 days
103
8% of all-time downloads
All-time downloads
1.2K
Public
Repo size
243 GB
Likes
0
Public
Click a slice to open those files.
.gguf243 GB · 100%
From the Hugging Face model README
This is GGUF of Qwen2-57B-A14B-Instruct.<br> I think this is the world's first successful example to make Qwen2-57B-A14B-Instruct's gguf with imatrix.
TFMC/imatrix-dataset-for-japanese-llm.<br> This dataset contains English and many Japanese sentence.
First I made Q6_K then tried to convert it to i-quants with --allow-requantize option.<br>
Surprisingly (re)quantizeing process has been completed without ploblems.<br>
I will show more detail below.
At first, I converted safetensors to GGUF.
Second, I converted f16 to Q8_0.<br> This is aiming to accelerate next process because I don't have enough memory to deal with high precision tensor.
I calculate imatrix with the Q8_0.<br>
I seem to some people succeed to calculate imatrix so I think anyone can make imatrix.<br>
At this time, I use '-fa' option because I want to finish culculation as much as possible.<br>
However, later I knew some people claim Qwen2 needs -fa option to work correctly.
This is most important step. Firstly I converted f16 to Q6_K.<br> Never try to make i-quants directly. No one may succeed to make it directly.
I converted the Q6_K to i-quants with imatrix.<br> Strangely the process has been finished and the i-quants may work.
GeForce RTX 3090 and llama.cpp windows binary b3065
Apache 2.0
Alibaba Cloud