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Spark-X2.5-1.7B-ASHQ1-GGUF
wepiqx/Spark-X2.5-1.7B-ASHQ1-GGUF
Spark-X2.5-1.7B-ASHQ1-GGUF is a text generation model from wepiqx. Use it when you need the model to write or continue text. It is set up for gguf. The card lists the license as apache-2.0.
🧪 New experimental quant available: BF-RELIEF-1000.gguf — beats all regular quants at 1000 MiB (PPL 66.70). More coming soon.
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At a glance
- Task
- Text Generation
- Library
- gguf
- License
- apache-2.0
- Access
- Public
- Created
- Sep 6, 2026
- Updated
- Sep 10, 2026
- SHA
- f4a33934
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Base models
Model card
From the Hugging Face model README
Spark-X2.5-1.7B — ASHQ1 Quantization
🧪 New experimental quant available:
BF-RELIEF-1000.gguf— beats all regular quants at 1000 MiB (PPL 66.70). More coming soon.
ASHQ1 quantization of Spark-X2.5-1.7B (dense ~2B, spark2_5 arch) — the little sibling of Spark-X2.5-4B, same hybrid attention (1 full + 3 sliding-window layers), native 1M context.
Status: published. Dedicated 1.7B imatrix (168 tensors, 28 layers) from Qwen3.8 calibration set. F16 reference PPL on calibration set: 12.21.
Architecture (why ASHQ1 fits as-is)
- Dense decoder,
spark2_5arch string — samespark2_5ASHQ1 preset as the 4B (layer count auto-detected: 28 blocks here vs 36 on 4B) - Fused
q_k_v_proj, MLPffn_gate/ffn_up/ffn_down, RMSNorms - No MTP, no MoE, no SSM — plain dense path
- Native 1M context, BF16, Apache-2.0
Quants
| File | Size | PPL (wiki.test.raw, ctx 1024) | Description |
|---|---|---|---|
BF-RELIEF-1000.gguf | 1006 MiB | 66.70 ± 0.68 | 🧪 Experimental — best at 1000 MiB, details coming |
Spark-X2.5-1.7B-BF16-Q8_0.gguf | 1.7 GB | 60.91 ± 0.62 | Uniform Q8_0 reference |
Spark-X2.5-1.7B-BF16-ASHQ1-1000.gguf | 1006 MiB | 71.50 ± 0.74 | Recommended: ASHQ1 bottom-up, output/token_embd pinned at Q5_K |
Spark-X2.5-1.7B-BF16-ASHQ1-1000-FREE.gguf | 1006 MiB | 73.14 ± 0.76 | Experimental: --free-pins (pins in open budget fight) — loses, pins validated |
Spark-X2.5-1.7B-BF16-ASHQ1-1000-EMBD8.gguf | 1006 MiB | 83.42 ± 0.86 | Experimental: embd pinned at Q8_0 — starves the rest, worst of the three |
PPL measured with stock llama.cpp (llama-perplexity, -ngl 99 -c 1024 -b 512 --seed 7) on wikitext-2-raw wiki.test.raw. Absolute values are inflated (model reads raw text without its jinja template); relative comparison is valid — all files measured identically.
⚠️ PPL is a canary, not a rank column: PPL below the BF16 base flags over-confidence (sharpening), not quality. Cross-checking all PPL-only wins on a KLD-vs-BF16 battery (with Soulfate24) — rankings below are provisional until KLD-scored. See the TOX lesson on our Ornith repo: a −0.145 PPL "win" with KLD 0.2894 (degraded).
Notes
- Source BF16:
XHToken/Spark-X2.5-1.7B-GGUF(BF16 conversion) - Runs on stock llama.cpp (native
spark2_5support since Sep 2026, no fork needed) - Quantization and tuning by wepiqx
- Task
- Text Generation
- Library
- gguf
- License
- apache-2.0
- Languages
- en
- Created
- Sep 6, 2026
- Updated
- Sep 10, 2026