ubergarm/Step-3.5-Flash-GGUF

Name: ubergarm/Step-3.5-Flash-GGUF
Author: ubergarm

High-quality GGUF model

8.0K 📥 Downloads

34 ❤️ Likes

0 📁 GGUF Files

0 B 💾 Total Size

2 weeks ago 🔄 Last Updated

📋 Model Description

quantized_by: ubergarm pipeline_tag: text-generation base_model: stepfun-ai/Step-3.5-Flash basemodelrelation: quantized license: apache-2.0 tags:

imatrix
conversational
ik_llama.cpp
step3p5

`ik_llama.cpp` imatrix Quantizations of stepfun-ai/Step-3.5-Flash

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! Thanks to huggingface for hosting all these big quants!

Finally, I really appreciate the support from aifoundry.org so check out their open source RISC-V based solutions!

Quant Collection

Perplexity computed against wiki.test.raw. (lower is "better")

!Perplexity Chart

These two are just a test quants for baseline perplexity comparison and not available for download here:

BF16 366.952 GiB (16.004 BPW)

- PPL over 561 chunks for n_ctx=512 = 2.4169 +/- 0.01107

Q8_0 195.031 GiB (8.506 BPW)

- PPL over 561 chunks for n_ctx=512 = 2.4188 +/- 0.01109

NOTE: The first split file is much smaller on purpose to only contain metadata, its fine!

IQ5_K 136.891 GiB (5.970 BPW)

PPL over 561 chunks for n_ctx=512 = 2.4304 +/- 0.01117

👈 Secret Recipe

#!/usr/bin/env bash

custom="
45 Repeating Layers [0-44]


Attention [0-44] GPU
blk\..\.attngate.=q80
blk\..\.attnq.=q80
blk\..\.attnk.=q80
blk\..\.attnv.=q80
blk\..\.attnoutput.=q80

First 3 Dense Layers [0-2] GPU
blk\..*\.ffndown\.weight=q80
blk\..*\.ffn(gate|up)\.weight=q80

Shared Expert Layers [3-44] GPU
blk\..*\.ffndownshexp\.weight=q8_0
blk\..*\.ffn(gate|up)shexp\.weight=q8_0

Routed Experts Layers [3-44] CPU
blk\..*\.ffndownexps\.weight=iq6_k
blk\..*\.ffn(gate|up)exps\.weight=iq5_k

Non-Repeating Layers
tokenembd\.weight=q80
output\.weight=q8_0
"

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/Step-3.5-Flash-GGUF/imatrix-Step-3.5-Flash-BF16.dat \
    /mnt/data/models/ubergarm/Step-3.5-Flash-GGUF/Step-3.5-Flash-288x7.4B-BF16-00001-of-00009.gguf \
    /mnt/data/models/ubergarm/Step-3.5-Flash-GGUF/Step-3.5-Flash-IQ5_K.gguf \
    IQ5_K \
    128

IQ4_XS 100.53 GiB (4.38 BPW)

PPL over 561 chunks for n_ctx=512 = 2.5181 +/- 0.01178

NOTE: This mainline compatible quant does not use imatrix.

👈 Secret Recipe

#!/usr/bin/env bash

custom="
45 Repeating Layers [0-44]


Attention [0-44] GPU
blk\..\.attngate.=q80
blk\..\.attnq.=q80
blk\..\.attnk.=q80
blk\..\.attnv.=q80
blk\..\.attnoutput.=q80

First 3 Dense Layers [0-2] GPU
blk\..*\.ffndown\.weight=q80
blk\..*\.ffn(gate|up)\.weight=q80

Shared Expert Layers [3-44] GPU
blk\..*\.ffndownshexp\.weight=q8_0
blk\..*\.ffn(gate|up)shexp\.weight=q8_0

Routed Experts Layers [3-44] CPU
blk\..*\.ffndownexps\.weight=iq4_xs
blk\..*\.ffn(gate|up)exps\.weight=iq4_xs

Non-Repeating Layers
tokenembd\.weight=q4K
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" \
    /mnt/data/models/ubergarm/Step-3.5-Flash-GGUF/Step-3.5-Flash-288x7.4B-BF16-00001-of-00009.gguf \
    /mnt/data/models/ubergarm/Step-3.5-Flash-GGUF/Step-3.5-Flash-IQ4_XS.gguf \
    IQ4_XS \
    128

smol-IQ4_KSS 94.080 GiB (4.103 BPW)

PPL over 561 chunks for n_ctx=512 = 2.5705 +/- 0.01211

👈 Secret Recipe

#!/usr/bin/env bash

custom="
45 Repeating Layers [0-44]


Attention [0-44] GPU
blk\..\.attngate.=iq6k
blk\..\.attnq.=iq6k
blk\..\.attnk.=iq6k
blk\..\.attnv.=iq6k
blk\..\.attnoutput.=iq6k

First 3 Dense Layers [0-2] GPU
blk\..*\.ffndown\.weight=iq6k
blk\..*\.ffn(gate|up)\.weight=iq6k

Shared Expert Layers [3-44] GPU
blk\..*\.ffndownshexp\.weight=iq6_k
blk\..*\.ffn(gate|up)shexp\.weight=iq6_k

Routed Experts Layers [3-44] CPU
blk\..*\.ffndownexps\.weight=iq4_kss
blk\..*\.ffn(gate|up)exps\.weight=iq4_kss

Non-Repeating Layers
tokenembd\.weight=iq4k
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/Step-3.5-Flash-GGUF/imatrix-Step-3.5-Flash-BF16.dat \
    /mnt/data/models/ubergarm/Step-3.5-Flash-GGUF/Step-3.5-Flash-288x7.4B-BF16-00001-of-00009.gguf \
    /mnt/data/models/ubergarm/Step-3.5-Flash-GGUF/Step-3.5-Flash-smol-IQ4_KSS.gguf \
    IQ4_KSS \
    128

smol-IQ3_KS 75.934 GiB (3.312 BPW)

PPL over 561 chunks for n_ctx=512 = 2.7856 +/- 0.01365

👈 Secret Recipe

#!/usr/bin/env bash

custom="
45 Repeating Layers [0-44]


Attention [0-44] GPU
blk\..\.attngate.=iq6k
blk\..\.attnq.=iq6k
blk\..\.attnk.=iq6k
blk\..\.attnv.=iq6k
blk\..\.attnoutput.=iq6k

First 3 Dense Layers [0-2] GPU
blk\..*\.ffndown\.weight=iq6k
blk\..*\.ffn(gate|up)\.weight=iq6k

Shared Expert Layers [3-44] GPU
blk\..*\.ffndownshexp\.weight=iq6_k
blk\..*\.ffn(gate|up)shexp\.weight=iq6_k

Routed Experts Layers [3-44] CPU
blk\..*\.ffndownexps\.weight=iq3_ks
blk\..*\.ffn(gate|up)exps\.weight=iq3_ks

Non-Repeating Layers
tokenembd\.weight=iq4k
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/Step-3.5-Flash-GGUF/imatrix-Step-3.5-Flash-BF16.dat \
    /mnt/data/models/ubergarm/Step-3.5-Flash-GGUF/Step-3.5-Flash-288x7.4B-BF16-00001-of-00009.gguf \
    /mnt/data/models/ubergarm/Step-3.5-Flash-GGUF/Step-3.5-Flash-smol-IQ3_KS.gguf \
    IQ3_KS \
    128

smol-IQ2_KS 53.786 GiB (2.346 BPW)

PPL over 561 chunks for n_ctx=512 = 4.2597 +/- 0.02425

👈 Secret Recipe

#!/usr/bin/env bash

custom="
45 Repeating Layers [0-44]


Attention [0-44] GPU
blk\..\.attngate.=iq6k
blk\..\.attnq.=iq6k
blk\..\.attnk.=iq6k
blk\..\.attnv.=iq6k
blk\..\.attnoutput.=iq6k

First 3 Dense Layers [0-2] GPU
blk\..*\.ffndown\.weight=iq6k
blk\..*\.ffn(gate|up)\.weight=iq6k

Shared Expert Layers [3-44] GPU
blk\..*\.ffndownshexp\.weight=iq6_k
blk\..*\.ffn(gate|up)shexp\.weight=iq6_k

Routed Experts Layers [3-44] CPU
blk\..*\.ffndownexps\.weight=iq2_ks
blk\..*\.ffn(gate|up)exps\.weight=iq2_ks

Non-Repeating Layers
tokenembd\.weight=iq4k
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/Step-3.5-Flash-GGUF/imatrix-Step-3.5-Flash-BF16.dat \
    /mnt/data/models/ubergarm/Step-3.5-Flash-GGUF/Step-3.5-Flash-288x7.4B-BF16-00001-of-00009.gguf \
    /mnt/data/models/ubergarm/Step-3.5-Flash-GGUF/Step-3.5-Flash-smol-IQ2_KS.gguf \
    IQ2_KS \
    128

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 -DCMAKEBUILDTYPE=Release -DGGML_CUDA=ON
$ cmake --build build --config Release -j $(nproc)

Run full offload on >2 GPUs with -sm graph Graph Parallel
https://github.com/ikawrakow/ik_llama.cpp/pull/1236
https://github.com/ikawrakow/ik_llama.cpp/pull/1231
https://github.com/ikawrakow/ik_llama.cpp/pull/1239
https://github.com/ikawrakow/ik_llama.cpp/pull/1240
CUDAVISIBLEDEVICES="0,1" \
./build/bin/llama-server \
  --model "$model" \
  --alias ubergarm/Step-Fun-3.5-Flash \
  -c 65536 \
  -ger \
  -sm graph \
  -ngl 99 \
  -ub 4096 -b 4096 \
  -ts 47,48 \
  --threads 1 \
  --host 127.0.0.1 \
  --port 8080 \
  --jinja \
  --no-mmap

CPU-only Mainline llama.cpp Example
numactl -N "$SOCKET" -m "$SOCKET" \
./build/bin/llama-server \
    --model "$model"\
    --alias ubergarm/Step-3.5-Flash \
    --ctx-size 65536 \
    -ctk q80 -ctv q80 \
    -ub 4096 -b 4096 \
    --parallel 1 \
    --threads 96 \
    --threads-batch 128 \
    --numa numactl \
    --host 127.0.0.1 \
    --port 8080 \
    --no-mmap \
    --jinja

The chat template baked into these GGUFs from the original
one.
Because of this note and later updated official gguf chat
template
you will probably want to copy paste the template
from the official gguf chat
template
and use --chat-template-file myTemplate.jinja.

Also Check Discussion 1 for a tested working chat template for tool use thanks to mindkrypted!

Another option for mainline tool calling users is to check out pwilkin's autoparser branch.

References

ikllama.cpp
Getting Started Guide (already out of date lol)
ubergarm-imatrix-calibration-corpus-v02.txt
mainline llama.cpp PR19283 converted with pull/19283/head:pr/step3.5-flash@5737bcf1b plus casting step35.attention.slidingwindowpattern to [INT32] as for some reason it defaults to [BOOL] for me (which would work fine for mainline regardless).
ikllama.cpp PR1231 imatrix & quantized with ik/step35_compat@9a0b5e80

📂 GGUF File List

No GGUF files available

📊 Model Information

🆔 Model ID: ubergarm/Step-3.5-Flash-GGUF

📅 Created: 3 weeks ago

🔄 Last Updated: 2 weeks ago

📥 Downloads: 8.0K

❤️ Likes: 34

🎯 Difficulty: Beginner

🏷️ Tags

ggufimatrixconversationalik_llama.cppstep3p5text-generationbase_model:stepfun-ai/Step-3.5-Flashbase_model:quantized:stepfun-ai/Step-3.5-Flashlicense:apache-2.0endpoints_compatibleregion:us

🔗 Related Links

🤗 Visit HuggingFace

ubergarm/Step-3.5-Flash-GGUF

📋 Model Description

ik_llama.cpp imatrix Quantizations of stepfun-ai/Step-3.5-Flash

Big Thanks

Quant Collection

IQ5_K 136.891 GiB (5.970 BPW)

45 Repeating Layers [0-44]

Attention [0-44] GPU

First 3 Dense Layers [0-2] GPU

Shared Expert Layers [3-44] GPU

Routed Experts Layers [3-44] CPU

Non-Repeating Layers

IQ4_XS 100.53 GiB (4.38 BPW)

45 Repeating Layers [0-44]

Attention [0-44] GPU

First 3 Dense Layers [0-2] GPU

Shared Expert Layers [3-44] GPU

Routed Experts Layers [3-44] CPU

Non-Repeating Layers

smol-IQ4_KSS 94.080 GiB (4.103 BPW)

45 Repeating Layers [0-44]

Attention [0-44] GPU

First 3 Dense Layers [0-2] GPU

Shared Expert Layers [3-44] GPU

Routed Experts Layers [3-44] CPU

Non-Repeating Layers

smol-IQ3_KS 75.934 GiB (3.312 BPW)

45 Repeating Layers [0-44]

Attention [0-44] GPU

First 3 Dense Layers [0-2] GPU

Shared Expert Layers [3-44] GPU

Routed Experts Layers [3-44] CPU

Non-Repeating Layers

smol-IQ2_KS 53.786 GiB (2.346 BPW)

45 Repeating Layers [0-44]

Attention [0-44] GPU

First 3 Dense Layers [0-2] GPU

Shared Expert Layers [3-44] GPU

Routed Experts Layers [3-44] CPU

Non-Repeating Layers

Quick Start

Build for hybrid CPU+CUDA

Run full offload on >2 GPUs with -sm graph Graph Parallel

https://github.com/ikawrakow/ik_llama.cpp/pull/1236

https://github.com/ikawrakow/ik_llama.cpp/pull/1231

https://github.com/ikawrakow/ik_llama.cpp/pull/1239

https://github.com/ikawrakow/ik_llama.cpp/pull/1240

CPU-only Mainline llama.cpp Example

References

📂 GGUF File List

📊 Model Information

🏷️ Tags

🔗 Related Links

`ik_llama.cpp` imatrix Quantizations of stepfun-ai/Step-3.5-Flash

Run full offload on >2 GPUs with `-sm graph` Graph Parallel