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LiquidAI/LFM2.5-8B-A1B-DSpark-GGUF

sourceHugging Faceotherupdated 1mo agoView on Hugging Face

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LFM2.5-8B-A1B-DSpark-GGUF

GGUF build of `LiquidAI/LFM2.5-8B-A1B-DSpark` for llama.cpp (DSpark speculative decoding is in mainline, ggml-org/llama.cpp #25173). This is a standalone draft sidecar: it carries only the drafter (5 attention layers, rank-256 Markov head, confidence head, block size 9). Token embeddings and the LM head are shared from the target model at load time, so it must be paired with a LFM2.5-8B-A1B-GGUF target file.

Find more information about LFM2.5-DSpark in our blog post.

📦 Files

file	quant	size	notes
`LFM2.5-8B-A1B-DSpark-F16.gguf`	F16	664 MB	best accept length, recommended when memory allows
`LFM2.5-8B-A1B-DSpark-Q8_0.gguf`	Q8_0	349 MB	accept length −2% vs F16
`LFM2.5-8B-A1B-DSpark-Q4_K_M.gguf`	Q4KM	191 MB	accept length −3% vs F16, smallest recommended — sub-4-bit draft quants measurably hurt both accept length and throughput

Draft quantization changes speed only marginally (the drafter is a small share of each cycle); choose by memory budget. The target model quant is the main speed/quality lever and is independent of this file.

🏃 How to run (llama.cpp)

bash

llama-server -m LFM2.5-8B-A1B-F16.gguf \
  -md LFM2.5-8B-A1B-DSpark-F16.gguf \
  --spec-type draft-dspark --spec-draft-n-max 10 --spec-draft-n-min 0 \
  -fa on -ngl 99

The block size is read from the sidecar metadata (n-max is clamped to it). Speculative decoding is exact: the target verifies every proposed token, so greedy output equals the target alone; per-response timings report draft_n / draft_n_accepted.

Other models in the LFM2.5-DSpark GGUF family:

Draft (GGUF)	Target (GGUF)
LFM2.5-1.2B-Instruct-DSpark-GGUF	LFM2.5-1.2B-Instruct-GGUF
LFM2.5-2.6B-DSpark-GGUF	LFM2.5-2.6B-GGUF
LFM2.5-8B-A1B-DSpark-GGUF	LFM2.5-8B-A1B-GGUF

📊 Acceptance and benchmarks

See `LiquidAI/LFM2.5-8B-A1B-DSpark` for acceptance-length tables (H100 and Apple silicon) and target benchmarks.

📬 Contact

—Got questions or want to connect? Join our Discord community
—If you are interested in custom solutions with edge deployment, please contact our sales team.

Citation

bibtex

@article{liquidAI202626B,
  author  = {Liquid AI},
  title   = {LFM2.5-2.6B: Agents Everywhere},
  journal = {Liquid AI Blog},
  year    = {2026},
  note    = {www.liquid.ai/blog/lfm2-5-2-6b},
}

bibtex

@article{liquidAI2026dspark,
  author = {Liquid AI},
  title = {LFM2.5-DSpark: Up to 3.2x Faster Inference from H100 to MacBook},
  journal = {Liquid AI Blog},
  year = {2026},
  note = {www.liquid.ai/blog/lfm2.5-dspark},
}