format stringclasses 1
value | format_version int64 2 2 | key_hash stringclasses 1
value | key stringclasses 1
value | entries listlengths 5 30 |
|---|---|---|---|---|
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llamAmpere eval caches for Swift 1.5 (IQ4_XS-M) at SJ-KVaRN 4/4
Why use this
About 6-7x faster long-context evals with everything on: eval cache + llamAmpere's MTP drafter + --parallel 2. For a 100K-token Qwen3.8-27B (Swift 1.5) prompt on one RTX 3090 Ti with a typical 2,000-token answer, that is about 153 s down to about 22 s; about 17x for 500-token answers and about 3x for 10,000-token answers. Prompt loading alone: 1.65 s vs 105.1 s fresh. This is arithmetic from measured parts, not one end-to-end run (release notes: https://github.com/JakeATX/llamAmpere/blob/main/docs/llamampere-v0.5.5/RELEASE_NOTES.md).
Long-context benchmarks send the same huge prompts every run, and the model has to read ("prefill") each one before it
writes a word. At 100K tokens that is minutes per prompt. This dataset holds the finished result of that reading step
for several well-known benchmarks, so your server can load it from disk instead of recomputing it. It is made for the
--eval-cache option of llamAmpere v0.5.5 (see tools/eval-cache/README.md in that release).
Every folder was built with llamAmpere commit c305c699f (the v0.5.5 release code; eval-cache format 2) on RTX 3090 cards.
A cache only works with the exact model file and the exact settings below. A server with different settings refuses
to start and says which setting differs. Do not change the model, -ctk, -ctv, the drafter defaults, or the cache format.
Run it (copy and paste)
llama-server \
--hf-repo jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF \
--hf-file ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M.gguf \
-c 262144 -ngl 99 -fa on -ctk sjkvarn4 -ctv sjkvarn4 -b 4096 -ub 1024 --parallel 1 \
--eval-cache ./evalcache/swift15-iq4xs-m/sjkvarn-44/longcodeqa/32K
(--parallel 1 and -b/-ub match how the folders were built. Keeping them gives byte-identical output at
temperature 0. The multi-token-prediction drafter and the vocabulary map are on by default and are part of the key.)
Download one folder only
huggingface-cli download jakeatx/llamampere-evalcache --repo-type dataset \
--include "swift15-iq4xs-m/sjkvarn-44/longcodeqa/32K/*" --local-dir ./evalcache
Replace the path with any folder in the table below. Each folder is self-contained: eval-cache.json (index),
entries/ (the saved states), manifest.json (model hash, flags, checksums) and build-server.log. Check a download with
python3 tools/eval-cache/llama-eval-cache.py verify --dir <folder>.
Your prompts must match exactly
A cache entry is used only when your request starts with the same tokens. Use the prompt files in prompts/ (the exact
lines the folders were built from): one user message, no system message, reasoning_effort "medium", sent to
/v1/chat/completions. Lines sharing a group share one saved prefix. Where a tier is split into parts, the part files
are in prompts/<benchmark>/parts/.
What is in it (measured)
| Benchmark | Folder | Entries | Questions served | Tokens cached | Size (GB) | Build GPU-minutes | KB per token | Break-even download speed (MB/s) | Identity check |
|---|---|---|---|---|---|---|---|---|---|
| AA-LCR | swift15-iq4xs-m/sjkvarn-44/aa-lcr/all30 |
30 | 100 | 3,103,744 | 107.7 | 70.3 | 34.7 | 26 | 2/2 byte-identical at temperature 0 (downloaded back from HF, checksums OK); request time 24.3 s with cache vs 289.2 s without |
| LongCodeQA | swift15-iq4xs-m/sjkvarn-44/longcodeqa/32K |
27 | 113 | 427,008 | 22.1 | 8.5 | 51.7 | 44 | 2/2 byte-identical at temperature 0 (downloaded from HF); request time 9.5 s with cache vs 28.2 s without |
| LongCodeQA | swift15-iq4xs-m/sjkvarn-44/longcodeqa/64K |
16 | 76 | 798,720 | 30.7 | 15.1 | 38.5 | 34 | not run on this part (file sizes and SHA-256 checked at upload; box verify passed) |
| LongCodeQA | swift15-iq4xs-m/sjkvarn-44/longcodeqa/128K |
20 | 92 | 1,985,536 | 69.1 | 43.0 | 34.8 | 27 | not run on this part (file sizes and SHA-256 checked at upload; box verify passed) |
| MRCR | swift15-iq4xs-m/sjkvarn-44/mrcr/8needle-64k-128k-part-a |
19 | 19 | 1,720,320 | 62.8 | 32.7 | 36.5 | 32 | 2/2 byte-identical at temperature 0 (downloaded back from HF, checksums OK); request time 8.8 s with cache vs 279.7 s without |
| MRCR | swift15-iq4xs-m/sjkvarn-44/mrcr/8needle-64k-128k-part-b |
14 | 14 | 1,604,608 | 57.0 | 33.1 | 35.5 | 29 | not run on this part (file sizes and SHA-256 checked at upload; box verify passed) |
| MRCR | swift15-iq4xs-m/sjkvarn-44/mrcr/8needle-64k-128k-part-c |
17 | 17 | 1,634,304 | 59.3 | 32.1 | 36.3 | 31 | not run on this part (file sizes and SHA-256 checked at upload; box verify passed) |
| RULER | swift15-iq4xs-m/sjkvarn-44/ruler/128k-part-a |
10 | 10 | 1,300,480 | 46.4 | 30.9 | 35.7 | 25 | 2/2 byte-identical at temperature 0 (downloaded back from HF, checksums OK); request time 15.7 s with cache vs 514.7 s without |
| RULER | swift15-iq4xs-m/sjkvarn-44/ruler/128k-part-b |
10 | 10 | 1,294,336 | 45.8 | 30.1 | 35.3 | 25 | not run on this part (file sizes and SHA-256 checked at upload; box verify passed) |
| RULER | swift15-iq4xs-m/sjkvarn-44/ruler/128k-part-c |
10 | 10 | 1,300,480 | 46.4 | 29.9 | 35.7 | 26 | not run on this part (file sizes and SHA-256 checked at upload; box verify passed) |
| RULER | swift15-iq4xs-m/sjkvarn-44/ruler/128k-part-d |
9 | 9 | 1,138,688 | 40.4 | 26.2 | 35.5 | 26 | not run on this part (file sizes and SHA-256 checked at upload; box verify passed) |
| LongBench v2 | swift15-iq4xs-m/sjkvarn-44/longbench-v2/196k-245k-part-a |
7 | 7 | 1,539,072 | 51.7 | 68.5 | 33.6 | 13 | 2/2 byte-identical at temperature 0 (downloaded back from HF, checksums OK); request time 19.3 s with cache vs 1223.9 s without |
| LongBench v2 | swift15-iq4xs-m/sjkvarn-44/longbench-v2/196k-245k-part-b |
5 | 5 | 1,105,920 | 37.3 | 33.0 | 33.7 | 19 | 1/1 byte-identical at temperature 0 on the build box before upload (1 prompt, not re-run after download); request time 9.5 s with cache vs 427.5 s without |
| LongBench v2 | swift15-iq4xs-m/sjkvarn-44/longbench-v2/196k-245k-part-c |
5 | 5 | 1,120,256 | 37.6 | 34.1 | 33.5 | 18 | 1/1 byte-identical at temperature 0 on the build box before upload (1 prompt, not re-run after download); request time 10.8 s with cache vs 432.4 s without |
| LongBench v2 | swift15-iq4xs-m/sjkvarn-44/longbench-v2/196k-245k-part-d |
5 | 5 | 1,130,496 | 38.0 | 34.2 | 33.6 | 19 | 1/1 byte-identical at temperature 0 on the build box before upload (1 prompt, not re-run after download); request time 7.7 s with cache vs 410.6 s without |
| LongBench v2 | swift15-iq4xs-m/sjkvarn-44/longbench-v2/196k-245k-part-e |
5 | 5 | 1,084,416 | 36.7 | 31.8 | 33.9 | 19 | 1/1 byte-identical at temperature 0 on the build box before upload (1 prompt); downloaded back from HF and checksums verified (5 entries, 0 problems); request time 6.6 s with cache vs 312.1 s without |
Total published: 209 entries, 497 questions, 22,288,384 tokens cached, 789 GB, 553 build GPU-minutes.
Is downloading faster than recomputing?
An entry stores the model state for the cached prefix: the size per cached token is in the table (it is larger for shorter prefixes). The break-even column multiplies that size by the prefill speed seen while building (tokens cached divided by build time on an RTX 3090). A download beats recomputing only when your link is faster than that. Below that speed, recomputing is quicker. Once a folder is on a local disk, a restore from NVMe is far faster than a prefill. These caches pay off for repeated runs of the same eval, or for a fast link.
Sources and terms
| Benchmark | Source | Terms |
|---|---|---|
| AA-LCR | ArtificialAnalysis/AA-LCR v1.1 (revision 9a77ef56b717), documents as extracted text | Questions Apache-2.0; documents are text of publicly available documents, "We do not claim copyright or place any license over this data." |
| LongCodeQA | Steefano/LCB (revision 989d5eff750d), prompt field used verbatim |
Dataset Apache-2.0; each repository inside is under its own GitHub license |
| MRCR | openai/mrcr (revision f4c69fae7cf8), 8-needle rows | MIT |
| RULER | NVIDIA/RULER commit c3f5e3b4, --random_seed 42 --chunk_amount 1 --model_template_type base --prepare_for_ns --max_seq_length 131072 --num_samples 3 |
Code Apache-2.0. Prompt text is NOT published here (essay haystack is not redistributable; QA tasks are CC BY-SA). Only prompts/ruler/rendered_prompt_hashes.json (SHA-256 of each user message) and the render scripts in render_scripts/ are provided, so you can regenerate the prompts and check them against the hashes. |
| LongBench v2 | zai-org/LongBench-v2 (revision 2b48e494f2c7), THUDM 0shot template | Dataset Apache-2.0, code MIT |
The cached states are derived from the benchmark texts above and from the model; they follow the terms of the sources.
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