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Danny-Dasilva/BTL-4-IQ2_XXS-DSPARK-GGUF
BTL-4-IQ2_XXS-DSPARK-GGUF is a text generation model from Danny-Dasilva. Use it when you need the model to write or continue text. It is set up for llama.cpp. The card lists the license as apache-2.0.
A self-contained, tested pairing of badtheorylabs/BTL-4-Compact and the Qwen3.6 DSpark GGUF draft converted by williamliao/Qwen3.6-35B-A3B-DSPARK-GGUF.
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From the Hugging Face model README
A self-contained, tested pairing of
badtheorylabs/BTL-4-Compact
and the Qwen3.6 DSpark GGUF draft converted by
williamliao/Qwen3.6-35B-A3B-DSPARK-GGUF.
BTL-4 is Qwen3.6/Ornith-derived and proved compatible with this draft in the experimental llama.cpp DSpark verifier. The weights are unchanged from the upstream releases. This repository adds a reproducible launch profile and RTX 5090 measurements at a 200,704-token configured context window.
DSpark support is experimental. These results use
llama.cppPR #26275, commitf806441edb5006fdacb07df42445f337476dd169.
| File | Purpose | Size |
|---|---|---|
BTL-4-IQ2_XXS.gguf | verifier/target model | 9.97 GB |
Qwen3.6-35B-A3B-DSPARK.gguf | DSpark draft model; not standalone | 1.04 GB |
launch-dspark.sh | tested 200K launch profile | — |
benchmark-results.json | machine-readable measurements | — |
Measured locally on 2026-08-07. Each result is an end-to-end wall-clock OpenAI Chat Completions request generating 512 tokens. Values are three independent runs after loading the full 200,704-token context configuration.
| Configuration | Runs (tok/s) | Mean | Best | VRAM | Relative to baseline |
|---|---|---|---|---|---|
| No draft | 260.51, 267.85, 271.10 | 266.49 tok/s | 271.10 | 12,586 MiB | 1.000x |
| DSpark, max draft 3 | 275.16, 307.40, 312.49 | 298.35 tok/s | 312.49 | 17,264 MiB | 1.120x |
The DSpark run accepted 326 of 553 drafted tokens (58.95%) with mean accepted
length 2.76. --spec-draft-n-max 3 is recommended; longer draft settings were
already slower on the Qwen3.6 Aggressive target tested on the same machine.
| Component | Value |
|---|---|
| GPU | NVIDIA GeForce RTX 5090, 32,607 MiB |
| Driver / power limit | 595.84 / 575 W |
| OS / kernel | Ubuntu 26.04 LTS / Linux 7.0.0-28-generic x86_64 |
| Target quantization | IQ2_XXS experts / Q4_K_M mixture, 9.97 GB |
| Draft | BF16 GGUF, 1.04 GB |
| Configured context | 200,704 tokens |
| KV cache | Q8_0 K and V for both target and draft |
| Batch / microbatch | 2,048 / 512 |
| Parallel slots | 1 |
| Flash attention | enabled |
These are single-stream decode measurements, not prompt-processing throughput or multi-user aggregate throughput. The first request can include warm-up overhead, which is intentionally retained in the mean.
The upstream full-precision
badtheorylabs/BTL-4 model card
reports the following official-harness results:
| Benchmark | Upstream BTL-4 BF16 | Attribution |
|---|---|---|
| LiveCodeBench v6 | 66.1% pass@1 | Bad Theory Labs; 442 problems, 2024-08 through 2025-05 |
| SWE-bench Verified | 78.4% | Bad Theory Labs; official harness |
| BFCL v4 AST | 73.5% | Bad Theory Labs; 1,240 cases |
Those values are upstream-reported BF16 scores, not measurements of this IQ2_XXS file. The Compact card reports 94.1% behavioral retention (111/118 on its quantization replay gate), but LiveCodeBench and SWE-bench Verified were not rerun for the compact quant. Do not present 66.1% or 78.4% as measured compact scores.
DSpark is target-verifying speculative decoding: accepted draft tokens are verified by BTL-4, so it accelerates this quantized target without replacing the target's token decisions.
Build the experimental branch with CUDA enabled:
git clone --branch dspark-speculators https://github.com/wjinxu/llama.cpp.git
cmake -S llama.cpp -B llama.cpp/build \
-DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Release \
-DCMAKE_CUDA_ARCHITECTURES=120a
cmake --build llama.cpp/build --config Release -j --target llama-server
Then, from this repository directory:
LLAMA_SERVER=/path/to/llama.cpp/build/bin/llama-server ./launch-dspark.sh
OpenAI-compatible API:
curl http://127.0.0.1:8080/v1/chat/completions \
-H 'Content-Type: application/json' \
-d '{
"model": "btl-4-compact-dspark",
"messages": [{"role": "user", "content": "Refactor this function."}],
"temperature": 0,
"max_tokens": 512
}'
The upstream compact build is text-only and has its MTP layer disabled. The
external DSpark draft is separate from MTP. See THIRD_PARTY_NOTICES.md and
LICENSE.