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aya-23-35B-IMat-GGUF

Text Generation
GGUF
quantized
GGUF
imatrix
quantization
imat
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aya-23-35B-IMat-GGUF / README.md
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metadata
base_model: CohereForAI/aya-23-35B
inference: false
language:
  - en
  - fr
  - de
  - es
  - it
  - pt
  - ja
  - ko
  - zh
  - ar
  - el
  - fa
  - pl
  - id
  - cs
  - he
  - hi
  - nl
  - ro
  - ru
  - tr
  - uk
  - vi
library_name: gguf
license: cc-by-nc-4.0
pipeline_tag: text-generation
quantized_by: legraphista
tags:
  - quantized
  - GGUF
  - imatrix
  - quantization

aya-23-35B-IMat-GGUF

Llama.cpp imatrix quantization of aya-23-35B-IMat-GGUF

Original Model: CohereForAI/aya-23-35B
Original dtype: FP16 (float16)
Quantized by: llama.cpp b2998
IMatrix dataset: here

Files

IMatrix

Status: ✅ Available
Link: here

Common Quants

Filename Quant type File Size Status Uses IMatrix Is Split
aya-23-35B.Q8_0/* Q8_0 37.18GB ✅ Available ⚪ No ✂ Yes
aya-23-35B.Q6_K.gguf Q6_K 28.71GB ✅ Available ⚪ No 📦 No
aya-23-35B.Q4_K.gguf Q4_K 21.53GB ✅ Available 🟢 Yes 📦 No
aya-23-35B.Q3_K.gguf Q3_K 17.62GB ✅ Available 🟢 Yes 📦 No
aya-23-35B.Q2_K.gguf Q2_K 13.82GB ✅ Available 🟢 Yes 📦 No

All Quants

Filename Quant type File Size Status Uses IMatrix Is Split
aya-23-35B.FP16/* F16 69.97GB ✅ Available ⚪ No ✂ Yes
aya-23-35B.Q5_K.gguf Q5_K 25.01GB ✅ Available ⚪ No 📦 No
aya-23-35B.Q5_K_S.gguf Q5_K_S 24.34GB ✅ Available ⚪ No 📦 No
aya-23-35B.Q4_K_S.gguf Q4_K_S 20.38GB ✅ Available 🟢 Yes 📦 No
aya-23-35B.Q3_K_L.gguf Q3_K_L 19.15GB ✅ Available 🟢 Yes 📦 No
aya-23-35B.Q3_K_S.gguf Q3_K_S 15.86GB ✅ Available 🟢 Yes 📦 No
aya-23-35B.Q2_K_S.gguf Q2_K_S 12.74GB ✅ Available 🟢 Yes 📦 No
aya-23-35B.IQ4_NL.gguf IQ4_NL 20.23GB ✅ Available 🟢 Yes 📦 No
aya-23-35B.IQ4_XS.gguf IQ4_XS 19.20GB ✅ Available 🟢 Yes 📦 No
aya-23-35B.IQ3_M.gguf IQ3_M 16.70GB ✅ Available 🟢 Yes 📦 No
aya-23-35B.IQ3_S.gguf IQ3_S 15.86GB ✅ Available 🟢 Yes 📦 No
aya-23-35B.IQ3_XS IQ3_XS - ⏳ Processing 🟢 Yes -
aya-23-35B.IQ3_XXS IQ3_XXS - ⏳ Processing 🟢 Yes -
aya-23-35B.IQ2_M IQ2_M - ⏳ Processing 🟢 Yes -
aya-23-35B.IQ2_S IQ2_S - ⏳ Processing 🟢 Yes -
aya-23-35B.IQ2_XS IQ2_XS - ⏳ Processing 🟢 Yes -
aya-23-35B.IQ2_XXS IQ2_XXS - ⏳ Processing 🟢 Yes -
aya-23-35B.IQ1_M IQ1_M - ⏳ Processing 🟢 Yes -
aya-23-35B.IQ1_S IQ1_S - ⏳ Processing 🟢 Yes -

Downloading using huggingface-cli

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you want:

huggingface-cli download legraphista/aya-23-35B-IMat-GGUF --include "aya-23-35B.Q8_0.gguf" --local-dir ./

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

huggingface-cli download legraphista/aya-23-35B-IMat-GGUF --include "aya-23-35B.Q8_0/*" --local-dir aya-23-35B.Q8_0
# see FAQ for merging GGUF's

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?

  1. Make sure you have gguf-split available
  2. Locate your GGUF chunks folder (ex: aya-23-35B.Q8_0)
  3. Run gguf-split --merge aya-23-35B.Q8_0/aya-23-35B.Q8_0-00001-of-XXXXX.gguf aya-23-35B.Q8_0.gguf
    • Make sure to point gguf-split to the first chunk of the split.

Got a suggestion? Ping me @legraphista!