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Open-weight model · Text generation

Ling-1T-GGUF

by Mohannad Amr honda6666/Ling-1T-GGUF

Ling-1T-GGUF is an open-weight model for text generation from Mohannad Amr, released under MIT License. Its published files total 2.8 TB.

This quant collection REQUIRES ikllama.cpp fork to support the ik's latest SOTA quants and optimizations! Do not download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc!

Parameters—
Context—
Weights2.8 TB
Licensemit
AccessOpen weights
Monthly Downloads—

Model Card

By Mohannad Amr, published under mit, revision 44588840cd75.

This quant collection REQUIRES ikllama.cpp fork to support the ik's latest SOTA quants and optimizations! Do not download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc! NOTE ikllama.cpp can also run your existing GGUFs from bartowski, unsloth, mradermacher, etc if you want to try it out before downloading my quants. Some of ik's new quants are supported with Nexesenex/croco.cpp fork of KoboldCPP with Windows builds for CUDA 12.9. Also check for Windows builds by Thireus here. which have been CUDA 12.8. These quants provide best in class perplexity for the given memory footprint. Shout out to Wendell and the Level1Techs crew, the…

Read Mohannad Amr's full model card

ik_llama.cpp imatrix Quantizations of inclusionAI/Ling-1T

This quant collection REQUIRES ik_llama.cpp fork to support the ik's latest SOTA quants and optimizations! Do not download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc!

NOTE ik_llama.cpp can also run your existing GGUFs from bartowski, unsloth, mradermacher, etc if you want to try it out before downloading my quants.

Some of ik's new quants are supported with Nexesenex/croco.cpp fork of KoboldCPP with Windows builds for CUDA 12.9. Also check for Windows builds by Thireus here. which have been CUDA 12.8.

These quants provide best in class perplexity for the given memory footprint.

Big Thanks

Shout out to Wendell and the Level1Techs crew, the community Forums, YouTube Channel! BIG thanks for providing BIG hardware expertise and access to run these experiments and make these great quants available to the community!!!

Also thanks to all the folks in the quanting and inferencing community on BeaverAI Club Discord and on r/LocalLLaMA for tips and tricks helping each other run, test, and benchmark all the fun new models!

Finally, I appreciate all the support from aifoundry.org and team as well as huggingface for hosting all these big quants!

Quant Collection

Perplexity computed against wiki.test.raw.

This one is just a test quant for baseline perplexity comparison: * Q8_0 989.678 GiB (8.504 BPW) - Final estimate: PPL = 1.9859 +/- 0.00907

IQ5_K 689.866 GiB (5.928 BPW)

Final estimate: PPL = 1.9897 +/- 0.00910

Secret Recipe 
#!/usr/bin/env bash

custom="
# 80 Repeating Layers [0-79]

# Attention
blk\..*\.attn_qkv.*=q8_0
blk\..*\.attn_output.*=q8_0

# First 4 Dense Layers [0-3]
blk\..*\.ffn_down\.weight=q8_0
blk\..*\.ffn_(gate|up)\.weight=q8_0

# Shared Expert Layers [3-79]
blk\..*\.ffn_down_shexp\.weight=q8_0
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0

# Routed Experts Layers [3-79]
blk\..*\.ffn_down_exps\.weight=iq6_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq5_k

# Non-Repeating Layers
token_embd\.weight=iq6_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Ling-1T-GGUF/imatrix-Ling-1T-Q8_0.dat \
    /mnt/data/models/ubergarm/Ling-1T-GGUF/Ling-1T-BF16-00001-of-00046.gguf \
    /mnt/data/models/ubergarm/Ling-1T-GGUF/Ling-1T-IQ5_K.gguf \
    IQ5_K \
    192

smol-IQ4_KSS 471.923 GiB (4.055 BPW)

Final estimate: PPL = 2.0176 +/- 0.00927

Secret Recipe 
#!/usr/bin/env bash

custom="
# 80 Repeating Layers [0-79]

# Attention
blk\..*\.attn_qkv.*=iq6_k
blk\..*\.attn_output.*=iq6_k

# First 4 Dense Layers [0-3]
blk\..*\.ffn_down\.weight=iq5_ks
blk\..*\.ffn_(gate|up)\.weight=iq5_ks

# Shared Expert Layers [3-79]
blk\..*\.ffn_down_shexp\.weight=iq5_ks
blk\..*\.ffn_(gate|up)_shexp\.weight=iq5_ks

# Routed Experts Layers [3-79]
blk\..*\.ffn_down_exps\.weight=iq4_kss
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_kss

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Ling-1T-GGUF/imatrix-Ling-1T-Q8_0.dat \
    /mnt/data/models/ubergarm/Ling-1T-GGUF/Ling-1T-BF16-00001-of-00046.gguf \
    /mnt/data/models/ubergarm/Ling-1T-GGUF/Ling-1T-smol-IQ4_KSS.gguf \
    IQ4_KSS \
    192

smol-IQ3_KS 378.853 GiB (3.255 BPW)

Final estimate: PPL = 2.0770 +/- 0.00968

Secret Recipe 
#!/usr/bin/env bash

custom="
# 80 Repeating Layers [0-79]

# Attention
blk\..*\.attn_qkv.*=iq6_k
blk\..*\.attn_output.*=iq6_k

# First 4 Dense Layers [0-3]
blk\..*\.ffn_down\.weight=iq5_ks
blk\..*\.ffn_(gate|up)\.weight=iq5_ks

# Shared Expert Layers [3-79]
blk\..*\.ffn_down_shexp\.weight=iq5_ks
blk\..*\.ffn_(gate|up)_shexp\.weight=iq5_ks

# Routed Experts Layers [3-79]
blk\..*\.ffn_down_exps\.weight=iq3_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq3_ks

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Ling-1T-GGUF/imatrix-Ling-1T-Q8_0.dat \
    /mnt/data/models/ubergarm/Ling-1T-GGUF/Ling-1T-BF16-00001-of-00046.gguf \
    /mnt/data/models/ubergarm/Ling-1T-GGUF/Ling-1T-smol-IQ3_KS.gguf \
    IQ3_KS \
    192

IQ2_K 330.923 GiB (2.843 BPW)

Final estimate: PPL = PPL = 2.2169 +/- 0.01055

This will use full q8_0 for VRAM layers and likely suit 384 RAM/VRAM.

Secret Recipe 
#!/usr/bin/env bash

custom="
# 80 Repeating Layers [0-79]

# Attention
blk\..*\.attn_qkv.*=q8_0
blk\..*\.attn_output.*=q8_0

# First 4 Dense Layers [0-3]
blk\..*\.ffn_down\.weight=q8_0
blk\..*\.ffn_(gate|up)\.weight=q8_0

# Shared Expert Layers [3-79]
blk\..*\.ffn_down_shexp\.weight=q8_0
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0

# Routed Experts Layers [3-79]
blk\..*\.ffn_down_exps\.weight=iq3_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_k

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Ling-1T-GGUF/imatrix-Ling-1T-Q8_0.dat \
    /mnt/data/models/ubergarm/Ling-1T-GGUF/Ling-1T-BF16-00001-of-00046.gguf \
    /mnt/data/models/ubergarm/Ling-1T-GGUF/Ling-1T-IQ2_K.gguf \
    IQ2_K \
    192

smol-IQ2_KS 264.984 GiB (2.277 BPW)

Final estimate: PPL = 2.4429 +/- 0.01191

Should hopefully fit in 250 GiB RAM + 15 GiB VRAM + kv-cache/context...

Leaving the attn.*/first 4 dense layers/shexp at full q8_0 would take about 20.1 GiB VRAM which is how the iqN_k quants are done.

Secret Recipe 
custom="
# 80 Repeating Layers [0-79]

# Attention
blk\.(0|1|2|3)\.attn_qkv.*=q8_0
blk\.(0|1|2|3)\.attn_output.*=q8_0
blk\..*\.attn_qkv.*=iq6_k
blk\..*\.attn_output.*=iq6_k

# First 4 Dense Layers [0-3]
blk\..*\.ffn_down\.weight=iq5_ks
blk\..*\.ffn_(gate|up)\.weight=iq5_ks

# Shared Expert Layers [3-79]
blk\..*\.ffn_down_shexp\.weight=iq5_ks
blk\..*\.ffn_(gate|up)_shexp\.weight=iq5_ks

# Routed Experts Layers [3-79]
blk\..*\.ffn_down_exps\.weight=iq2_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_ks

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Ling-1T-GGUF/imatrix-Ling-1T-Q8_0.dat \
    /mnt/data/models/ubergarm/Ling-1T-GGUF/Ling-1T-BF16-00001-of-00046.gguf \
    /mnt/data/models/ubergarm/Ling-1T-GGUF/Ling-1T-smol-IQ2_KS.gguf \
    IQ2_KS \
    192

smol-IQ2_XXS 249.92 GiB (2.15 BPW)

Final estimate: PPL = 2.5870 +/- 0.01279

This is a rare mainline compatible quant I released for folks to test this PR: https://github.com/ggml-org/llama.cpp/pull/16063

Secret Recipe 
#!/usr/bin/env bash

custom="
# 80 Repeating Layers [0-79]

# Attention
blk\.(0|1|2|3)\.attn_qkv.*=q8_0
blk\.(0|1|2|3)\.attn_output.*=q8_0
blk\..*\.attn_qkv.*=q6_K
blk\..*\.attn_output.*=q6_K

# First 4 Dense Layers [0-3]
blk\..*\.ffn_down\.weight=q5_K
blk\..*\.ffn_(gate|up)\.weight=q4_K

# Shared Expert Layers [3-79]
blk\..*\.ffn_down_shexp\.weight=q5_K
blk\..*\.ffn_(gate|up)_shexp\.weight=q4_K

# Routed Experts Layers [3-79]
blk\..*\.ffn_down_exps\.weight=iq2_xxs
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_xxs

# Non-Repeating Layers
token_embd\.weight=q4_K
output\.weight=q6_K
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Ling-1T-GGUF/imatrix-Ling-1T-Q8_0.dat \
    /mnt/data/models/ubergarm/Ling-1T-GGUF/Ling-1T-BF16-00001-of-00046.gguf \
    /mnt/data/models/ubergarm/Ling-1T-GGUF/Ling-1T-smol-IQ2_XXS.gguf \
    IQ2_XXS \
    192

smol-IQ1_KT 215.423 GiB (1.851 BPW)

Final estimate: PPL = 2.8581 +/- 0.01471

One of the smallest yet functional quants available, but keep in mind KT types can be slower on CPU inferencing due to likely being computed bottlenecked calculating trellis during TG. Still worth a try if this is all your rig can fit!

Secret Recipe 
#!/usr/bin/env bash

custom="
# 80 Repeating Layers [0-79]

# Attention
blk\..*\.attn_qkv.*=iq6_k
blk\..*\.attn_output.*=iq6_k

# First 4 Dense Layers [0-3]
blk\..*\.ffn_down\.weight=iq5_ks
blk\..*\.ffn_(gate|up)\.weight=iq5_ks

# Shared Expert Layers [3-79]
blk\..*\.ffn_down_shexp\.weight=iq5_ks
blk\..*\.ffn_(gate|up)_shexp\.weight=iq5_ks

# Routed Experts Layers [3-79]
blk\..*\.ffn_down_exps\.weight=iq1_kt
blk\..*\.ffn_(gate|up)_exps\.weight=iq1_kt

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Ling-1T-GGUF/imatrix-Ling-1T-Q8_0.dat \
    /mnt/data/models/ubergarm/Ling-1T-GGUF/Ling-1T-BF16-00001-of-00046.gguf \
    /mnt/data/models/ubergarm/Ling-1T-GGUF/Ling-1T-smol-IQ1_KT.gguf \
    IQ1_KT \
    192

Quick Start

# Clone and checkout
$ git clone https://github.com/ikawrakow/ik_llama.cpp
$ cd ik_llama.cpp

# Build for hybrid CPU+CUDA
$ cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON
$ cmake --build build --config Release -j $(nproc)

# CPU-Only Inference
# `-ger` is still fresh:
# https://github.com/ikawrakow/ik_llama.cpp/pull/836
# Omit numactl and `--numa ...` if you have only a single NUMA node
# set batches/threads/kv cache as desired
# NOTE: multiple slots e.g. `--parallel 2` may case error after canceling generation then starting a new one at the moment
SOCKET=0
numactl -N "$SOCKET" -m "$SOCKET" \
./build/bin/llama-server \
    --model "$model"\
    --alias ubergarm/Ling-1T-GGUF \
    --ctx-size 32768 \
    -fa -fmoe -ger \
    -ctk q8_0 -ctv q8_0 \
    -ub 4096 -b 4096 \
    --parallel 1 \
    --threads 128 \
    --threads-batch 192 \
    --numa numactl \
    --host 127.0.0.1 \
    --port 8080 \
    --no-mmap \
    --no-display-prompt

# Hybrid GPU+CPU Inference
# WARNING: Haven't tested this personally yet...
# `-ger` on CUDA may not be merged yet:
# https://github.com/ikawrakow/ik_llama.cpp/pull/838
# Omit numactl and `--numa ...` if you have only a single NUMA node
# set batches/threads/kv cache as desired
# NOTE: multiple slots e.g. `--parallel 2` may case error after canceling generation then starting a new one at the moment
SOCKET=0
numactl -N "$SOCKET" -m "$SOCKET" \
./build/bin/llama-server \
    --model "$model"\
    --alias ubergarm/Ling-1T-GGUF \
    --ctx-size 32768 \
    -fa -fmoe -ger \
    -ctk q8_0 -ctv q8_0 \
    -ub 4096 -b 4096 \
    -ngl 99 \
    -ot "blk\.(4|5|6)\.ffn_.*=CUDA0" \
    -ot "blk\.(7|8|9)\.ffn_.*=CUDA1" \
    -ot exps=CPU \
    --parallel 1 \
    --threads 128 \
    --threads-batch 192 \
    --numa numactl \
    --host 127.0.0.1 \
    --port 8080 \
    --no-mmap \
    --no-display-prompt

# optional use this once after downloading to confirm good files
    --validate-quants

# NOTE: if you *really* want over the official 32k supported context consider these options:
#  64k
--ctx-size  65536 --rope-scaling yarn --rope-scale 2 --yarn-orig-ctx 32768 --override-kv bailingmoe2.context_length=int:65536
# 128k (longer extension likely reduces quality of output, always use minimum context required)
--ctx-size 131072 --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768 --override-kv bailingmoe2.context_length=int:131072
# Details:
# * https://github.com/ikawrakow/ik_llama.cpp/discussions/839#discussioncomment-14745117
# * https://github.com/ikawrakow/ik_llama.cpp/issues/873

References

Identity and Version

Repository
honda6666/Ling-1T-GGUF
Publisher
Mohannad Amr
Task
Text generation
Modality
Text
Library
Not stated by the source
Parameters
Not stated by the source
Languages
Not stated by the source
Revision
44588840cd75bbbf848c5b54460efa2f8c4bec8c
First published
2026-10-01
Last updated
2026-10-01

Files and Weights

65 files, 2.8 TB in total. The weights are 61 files totalling 2.8 TB in gguf.

Weights61 files · 2.8 TB
Documentation1 file · 13.7 KB
Other2 files · 1.4 GB
Repository1 file · 1.7 KB
Every file
FileTypeSizeSHA-256
IQ2_K/Ling-1T-IQ2_K-00001-of-00008.ggufWeights44.4 GB 90e97465527c
IQ2_K/Ling-1T-IQ2_K-00002-of-00008.ggufWeights44.9 GB 4b5f4f2dfaf0
IQ2_K/Ling-1T-IQ2_K-00003-of-00008.ggufWeights44.9 GB 204b68d4423f
IQ2_K/Ling-1T-IQ2_K-00004-of-00008.ggufWeights44.3 GB d738080b98c4
IQ2_K/Ling-1T-IQ2_K-00005-of-00008.ggufWeights44.9 GB 7ccd2199e8ef
IQ2_K/Ling-1T-IQ2_K-00006-of-00008.ggufWeights44.9 GB bd4799ad446b
IQ2_K/Ling-1T-IQ2_K-00007-of-00008.ggufWeights44.3 GB 1e65e6393264
IQ2_K/Ling-1T-IQ2_K-00008-of-00008.ggufWeights42.7 GB 7f2cc6a980cb
IQ5_K/Ling-1T-IQ5_K-00001-of-00016.ggufWeights48.4 GB f1ec4d118659
IQ5_K/Ling-1T-IQ5_K-00002-of-00016.ggufWeights48.4 GB b09565f82066
IQ5_K/Ling-1T-IQ5_K-00003-of-00016.ggufWeights48.4 GB 93f2f00ecd11
IQ5_K/Ling-1T-IQ5_K-00004-of-00016.ggufWeights48.4 GB 16051c54bfa9
IQ5_K/Ling-1T-IQ5_K-00005-of-00016.ggufWeights48.4 GB 336b63b629a1
IQ5_K/Ling-1T-IQ5_K-00006-of-00016.ggufWeights48.4 GB f0c272053eab
IQ5_K/Ling-1T-IQ5_K-00007-of-00016.ggufWeights48.4 GB 545ec3e7d799
IQ5_K/Ling-1T-IQ5_K-00008-of-00016.ggufWeights48.4 GB fe29e6a73aa7
IQ5_K/Ling-1T-IQ5_K-00009-of-00016.ggufWeights48.4 GB 634266ba4433
IQ5_K/Ling-1T-IQ5_K-00010-of-00016.ggufWeights48.4 GB 764d7e9fc36d
IQ5_K/Ling-1T-IQ5_K-00011-of-00016.ggufWeights48.4 GB 9f9c38517bb5
IQ5_K/Ling-1T-IQ5_K-00012-of-00016.ggufWeights48.4 GB f7fbf09f460c
IQ5_K/Ling-1T-IQ5_K-00013-of-00016.ggufWeights48.4 GB d9264c8a7067
IQ5_K/Ling-1T-IQ5_K-00014-of-00016.ggufWeights48.4 GB 66fcf6fc5061
IQ5_K/Ling-1T-IQ5_K-00015-of-00016.ggufWeights48.3 GB 9374215a3d07
IQ5_K/Ling-1T-IQ5_K-00016-of-00016.ggufWeights14.6 GB 193a52822e91
smol-IQ1_KT/Ling-1T-smol-IQ1_KT-00001-of-00005.ggufWeights47.0 GB 7b7e7dc5484b
smol-IQ1_KT/Ling-1T-smol-IQ1_KT-00002-of-00005.ggufWeights46.5 GB 03e72adea5e2
smol-IQ1_KT/Ling-1T-smol-IQ1_KT-00003-of-00005.ggufWeights47.0 GB 48e5b1b93d61
smol-IQ1_KT/Ling-1T-smol-IQ1_KT-00004-of-00005.ggufWeights46.9 GB 5d1e37a6f2e4
smol-IQ1_KT/Ling-1T-smol-IQ1_KT-00005-of-00005.ggufWeights44.1 GB 181faacc1de6
smol-IQ2_KS/Ling-1T-smol-IQ2_KS-00001-of-00006.ggufWeights46.8 GB 10ae32d86fe9
smol-IQ2_KS/Ling-1T-smol-IQ2_KS-00002-of-00006.ggufWeights48.0 GB 426b292b5f16
smol-IQ2_KS/Ling-1T-smol-IQ2_KS-00003-of-00006.ggufWeights48.0 GB c919aeac09a6
smol-IQ2_KS/Ling-1T-smol-IQ2_KS-00004-of-00006.ggufWeights47.1 GB 74cdddd8587e
smol-IQ2_KS/Ling-1T-smol-IQ2_KS-00005-of-00006.ggufWeights47.7 GB 41094a0d3c77
smol-IQ2_KS/Ling-1T-smol-IQ2_KS-00006-of-00006.ggufWeights47.0 GB 9b1f3ff50482
smol-IQ2_XXS/Ling-1T-smol-IQ2_XXS-00001-of-00006.ggufWeights45.3 GB e96d8d5a6258
smol-IQ2_XXS/Ling-1T-smol-IQ2_XXS-00002-of-00006.ggufWeights45.5 GB 6abf8720f0e6
smol-IQ2_XXS/Ling-1T-smol-IQ2_XXS-00003-of-00006.ggufWeights44.9 GB 2d7ff887949b
smol-IQ2_XXS/Ling-1T-smol-IQ2_XXS-00004-of-00006.ggufWeights45.6 GB 4e02743f316e
smol-IQ2_XXS/Ling-1T-smol-IQ2_XXS-00005-of-00006.ggufWeights46.0 GB 793cea2b1539
smol-IQ2_XXS/Ling-1T-smol-IQ2_XXS-00006-of-00006.ggufWeights41.0 GB f21150e30aa0
smol-IQ3_KS/Ling-1T-smol-IQ3_KS-00001-of-00009.ggufWeights46.3 GB 8890854fde04
smol-IQ3_KS/Ling-1T-smol-IQ3_KS-00002-of-00009.ggufWeights46.2 GB e19bd08c2c15
smol-IQ3_KS/Ling-1T-smol-IQ3_KS-00003-of-00009.ggufWeights46.1 GB 7669bb9e060a
smol-IQ3_KS/Ling-1T-smol-IQ3_KS-00004-of-00009.ggufWeights45.5 GB be614c177ca6
smol-IQ3_KS/Ling-1T-smol-IQ3_KS-00005-of-00009.ggufWeights46.2 GB 100cb6cfa819
smol-IQ3_KS/Ling-1T-smol-IQ3_KS-00006-of-00009.ggufWeights46.2 GB 3884a37ec2d8
smol-IQ3_KS/Ling-1T-smol-IQ3_KS-00007-of-00009.ggufWeights46.1 GB a617663582bd
smol-IQ3_KS/Ling-1T-smol-IQ3_KS-00008-of-00009.ggufWeights45.5 GB 5abd6a173c68
smol-IQ3_KS/Ling-1T-smol-IQ3_KS-00009-of-00009.ggufWeights38.6 GB a7f1fb9006e9
smol-IQ4-KSS/Ling-1T-smol-IQ4_KSS-00001-of-00011.ggufWeights46.5 GB 48d4c6688056
smol-IQ4-KSS/Ling-1T-smol-IQ4_KSS-00002-of-00011.ggufWeights46.0 GB 1992c75a4816
smol-IQ4-KSS/Ling-1T-smol-IQ4_KSS-00003-of-00011.ggufWeights46.7 GB 4bfbca0e3c99
smol-IQ4-KSS/Ling-1T-smol-IQ4_KSS-00004-of-00011.ggufWeights46.2 GB 321a5ba7b0cf
smol-IQ4-KSS/Ling-1T-smol-IQ4_KSS-00005-of-00011.ggufWeights46.0 GB d88bb629e257
smol-IQ4-KSS/Ling-1T-smol-IQ4_KSS-00006-of-00011.ggufWeights46.8 GB 42518548e821
smol-IQ4-KSS/Ling-1T-smol-IQ4_KSS-00007-of-00011.ggufWeights46.0 GB aa94cfdbc0a3
smol-IQ4-KSS/Ling-1T-smol-IQ4_KSS-00008-of-00011.ggufWeights46.7 GB d60a0094bed8
smol-IQ4-KSS/Ling-1T-smol-IQ4_KSS-00009-of-00011.ggufWeights46.2 GB b54febaf29bd
smol-IQ4-KSS/Ling-1T-smol-IQ4_KSS-00010-of-00011.ggufWeights46.0 GB 4e3560cd3a48
smol-IQ4-KSS/Ling-1T-smol-IQ4_KSS-00011-of-00011.ggufWeights43.5 GB 9b7ac17e2e2e
README.mdDocumentation13.7 KB —
images/perplexity.pngOther148.6 KB 15a7cdea1d26
imatrix-Ling-1T-Q8_0.datOther1.4 GB 62fe6ae01bf5
.gitattributesRepository1.7 KB —

License and Download

License
mit
Access
Open weights, no gate
Download size
2.8 TB
Download from Mohannad Amr

Released by Mohannad Amr through its official repository on Hugging Face. Read the license.

Built From

  • Derived from inclusionAI/Ling-1T
  • Quantized from inclusionAI/Ling-1T

Memory Requirements

PrecisionWeights in memory
As published2.8 TB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About Ling-1T-GGUF

Can I use Ling-1T-GGUF commercially?

Yes. Ling-1T-GGUF is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.

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