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Cataloged from honda6666/Ling-1T-GGUF
All models by honda6666 →ik_llama.cpp imatrix Quantizations of inclusionAI/Ling-1TThis 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.
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!
Perplexity computed against wiki.test.raw.
[Image blocked: Perplexity Chart]
This one is just a test quant for baseline perplexity comparison:
Q8_0 989.678 GiB (8.504 BPW)
Final estimate: PPL = 1.9897 +/- 0.00910
Final estimate: PPL = 2.0176 +/- 0.00927
Final estimate: PPL = 2.0770 +/- 0.00968
Final estimate: PPL = PPL = 2.2169 +/- 0.01055
This will use full q8_0 for VRAM layers and likely suit 384 RAM/VRAM.
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.
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
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!
#!/usr/bin/env bash
custom="# 80 Repeating Layers [0-79]
# Attentionblk\..*\.attn_qkv.*=q8_0blk\..*\.attn_output.*=q8_0
# First 4 Dense Layers [0-3]blk\..*\.ffn_down\.weight=q8_0blk\..*\.ffn_(gate|up)\.weight=q8_0
# Shared Expert Layers [3-79]blk\..*\.ffn_down_shexp\.weight=q8_0blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0
# Routed Experts Layers [3-79]blk\..*\.ffn_down_exps\.weight=iq6_kblk\..*\.ffn_(gate|up)_exps\.weight=iq5_k
# Non-Repeating Layerstoken_embd\.weight=iq6_koutput\.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#!/usr/bin/env bash
custom="# 80 Repeating Layers [0-79]
# Attentionblk\..*\.attn_qkv.*=iq6_kblk\..*\.attn_output.*=iq6_k
# First 4 Dense Layers [0-3]blk\..*\.ffn_down\.weight=iq5_ksblk\..*\.ffn_(gate|up)\.weight=iq5_ks
# Shared Expert Layers [3-79]blk\..*\.ffn_down_shexp\.weight=iq5_ksblk\..*\.ffn_(gate|up)_shexp\.weight=iq5_ks
# Routed Experts Layers [3-79]blk\..*\.ffn_down_exps\.weight=iq4_kssblk\..*\.ffn_(gate|up)_exps\.weight=iq4_kss
# Non-Repeating Layerstoken_embd\.weight=iq4_koutput\.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#!/usr/bin/env bash
custom="# 80 Repeating Layers [0-79]
# Attentionblk\..*\.attn_qkv.*=iq6_kblk\..*\.attn_output.*=iq6_k
# First 4 Dense Layers [0-3]blk\..*\.ffn_down\.weight=iq5_ksblk\..*\.ffn_(gate|up)\.weight=iq5_ks
# Shared Expert Layers [3-79]blk\..*\.ffn_down_shexp\.weight=iq5_ksblk\..*\.ffn_(gate|up)_shexp\.weight=iq5_ks
# Routed Experts Layers [3-79]blk\..*\.ffn_down_exps\.weight=iq3_ksblk\..*\.ffn_(gate|up)_exps\.weight=iq3_ks
# Non-Repeating Layerstoken_embd\.weight=iq4_koutput\.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#!/usr/bin/env bash
custom="# 80 Repeating Layers [0-79]
# Attentionblk\..*\.attn_qkv.*=q8_0blk\..*\.attn_output.*=q8_0
# First 4 Dense Layers [0-3]blk\..*\.ffn_down\.weight=q8_0blk\..*\.ffn_(gate|up)\.weight=q8_0
# Shared Expert Layers [3-79]blk\..*\.ffn_down_shexp\.weight=q8_0blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0
# Routed Experts Layers [3-79]blk\..*\.ffn_down_exps\.weight=iq3_kblk\..*\.ffn_(gate|up)_exps\.weight=iq2_k
# Non-Repeating Layerstoken_embd\.weight=iq4_koutput\.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 \ 192custom="# 80 Repeating Layers [0-79]
# Attentionblk\.(0|1|2|3)\.attn_qkv.*=q8_0blk\.(0|1|2|3)\.attn_output.*=q8_0blk\..*\.attn_qkv.*=iq6_kblk\..*\.attn_output.*=iq6_k
# First 4 Dense Layers [0-3]blk\..*\.ffn_down\.weight=iq5_ksblk\..*\.ffn_(gate|up)\.weight=iq5_ks
# Shared Expert Layers [3-79]blk\..*\.ffn_down_shexp\.weight=iq5_ksblk\..*\.ffn_(gate|up)_shexp\.weight=iq5_ks
# Routed Experts Layers [3-79]blk\..*\.ffn_down_exps\.weight=iq2_ksblk\..*\.ffn_(gate|up)_exps\.weight=iq2_ks
# Non-Repeating Layerstoken_embd\.weight=iq4_koutput\.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#!/usr/bin/env bash
custom="# 80 Repeating Layers [0-79]
# Attentionblk\.(0|1|2|3)\.attn_qkv.*=q8_0blk\.(0|1|2|3)\.attn_output.*=q8_0blk\..*\.attn_qkv.*=q6_Kblk\..*\.attn_output.*=q6_K
# First 4 Dense Layers [0-3]blk\..*\.ffn_down\.weight=q5_Kblk\..*\.ffn_(gate|up)\.weight=q4_K
# Shared Expert Layers [3-79]blk\..*\.ffn_down_shexp\.weight=q5_Kblk\..*\.ffn_(gate|up)_shexp\.weight=q4_K
# Routed Experts Layers [3-79]blk\..*\.ffn_down_exps\.weight=iq2_xxsblk\..*\.ffn_(gate|up)_exps\.weight=iq2_xxs
# Non-Repeating Layerstoken_embd\.weight=q4_Koutput\.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#!/usr/bin/env bash
custom="# 80 Repeating Layers [0-79]
# Attentionblk\..*\.attn_qkv.*=iq6_kblk\..*\.attn_output.*=iq6_k
# First 4 Dense Layers [0-3]blk\..*\.ffn_down\.weight=iq5_ksblk\..*\.ffn_(gate|up)\.weight=iq5_ks
# Shared Expert Layers [3-79]blk\..*\.ffn_down_shexp\.weight=iq5_ksblk\..*\.ffn_(gate|up)_shexp\.weight=iq5_ks
# Routed Experts Layers [3-79]blk\..*\.ffn_down_exps\.weight=iq1_ktblk\..*\.ffn_(gate|up)_exps\.weight=iq1_kt
# Non-Repeating Layerstoken_embd\.weight=iq4_koutput\.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# 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 $(nproc)
# CPU-Only Inference# `-ger` is still fresh:# https://github.com/ikawrakow/ik_llama.cpp/pull/836# Omit numactl and `--numa ...` if you have only a single NUMA node# set batches/threads/kv cache as desired# NOTE: multiple slots e.g. `--parallel 2` may case error after canceling generation then starting a new one at the momentSOCKET=0numactl -N "$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 momentSOCKET=0numactl -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